Home > Resources > Articles > Exploring Business Success with Location Intelligence

Exploring Business Success with Location Intelligence

Location Intelligence
Discover the essentials of location intelligence across various industries, foundational concepts, industry-specific implementations, and location intelligence software in this insightful article.

Contents

1. Introduction
2. Foundational Elements of Location Intelligence
3. Location Intelligence in Industry Verticals
4. Top Providers in Location Intelligence 5. Wrap Up

1. Introduction

In the modern digital age, businesses continually pursue innovative strategies to gain a competitive edge and succeed. Among these strategies, location intelligence has emerged as a potent tool for driving informed decision-making. By leveraging geospatial data and advanced analytics, companies can extract actionable insights into customer behavior, market dynamics, and operational efficiencies. This article provides an overview of location intelligence, highlighting its role in enabling businesses across diverse industry verticals to navigate complex arenas and make data-driven decisions effectively.

2. Foundational Elements of Location Intelligence

  • Geospatial Data: Geospatial data encompasses information tied to specific geographic locations on Earth's surface. It includes various types, such as vector data (points, lines, polygons), raster data (imagery), and attribute data (descriptive information about spatial features). Sources of geospatial data range from satellite imagery, aerial photography, GPS data, and LiDAR scans to census data, social media check-ins, and IoT devices.
  • Mapping and Visualization Techniques: Mapping and visualization techniques are essential for conveying spatial information effectively. Cartography principles help design clear, informative, and aesthetically pleasing maps. Visualization techniques range from simple thematic maps to complex interactive web maps and 3D visualizations.
  • GIS (Geographic Information Systems): GIS software allows users to capture, store, analyze, and visualize geospatial data. It enables the integration of various data types and provides tools for spatial analysis, such as overlay, proximity analysis, and spatial querying. In addition, Geographic Information Systems software is crucial in creating and analyzing spatial data.
  • GPS (Global Positioning System): GPS technology provides precise location information by utilizing a network of satellites. It is widely used in navigation, asset tracking, surveying, and location-based services.
  • Remote Sensing: Remote sensing involves capturing information about Earth's surface from a distance, typically using satellites or aircraft. It provides valuable data for monitoring environmental changes, agriculture, disaster management, and natural resource management.

3. Location Intelligence in Industry Verticals

Location intelligence finds applications across various industry verticals, each with its unique challenges and opportunities.

  • Transport and logistics organizations leverage location intelligence software to optimize supply chains and streamline delivery operations. With location analytics, fleet managers can minimize delivery time and control warehouse loading and unloading processes. Real-time monitoring helps in route accuracy, identifying delivery delays, and accurately calculating expenses, including fuel reimbursement and fleet operating costs.
  • Location analytics is a game-changer in the pharmaceutical industry, revolutionizing on-field activities such as dispatching medicines, inventory management, and managing client visits. Companies harness location data to fine-tune sales operations and align sales territories effectively. By analyzing sales patterns geographically, pharma companies can optimize sales efforts, minimize travel time for medical reps, and ensure equitable distribution of tasks.
  • In banking and insurance industries, location intelligence data is integral to channel optimization and sales operations. Banks and insurance companies leverage location data to expand their branch networks strategically and make informed decisions about acquisitions or investments. By engaging customers with relevant information based on their location, they can maintain positive relationships, develop better policies, and boost marketing strategies.
  • The retail industry utilizes location intelligence to deliver personalized experiences and optimize supply chain management. By integrating location data with consumer and operational data, retailers gain insights into consumer behavior and preferences at the store level. This enables them to deliver consistent purchasing experiences, drive customized marketing outreach, and optimize supply chain processes for enhanced efficiency and customer satisfaction.

4. Top Providers in Location Intelligence

When exploring the world of location intelligence, it becomes apparent that the source of the data driving these insights is a crucial yet often neglected aspect. Every business industry requires distinct datasets; not all providers offer the same breadth or depth. Therefore, understanding where to obtain these essential resources is paramount. Look at a list of top providers in location intelligence to ensure access to the precise data types required to propel your business forward.

4.1 Tango Analytics

Tango Analytics_logo
Tango Analytics solutions offer businesses the intelligence to develop more brilliant location strategies and make informed capital investment decisions. By integrating advanced modeling with robust data within a scalable geospatial analytics platform, Tango provides unparalleled insights that optimize location decisions, maximizing the potential for brand success.

Tango's Integrated Workplace Management System (IWMS) software suite equips organizations with comprehensive tools to analyze and optimize their corporate offices or physical locations. With purpose-built GIS, predictive analytics, and management tools, Tango enables businesses to better predict and respond to market opportunities while enhancing the execution of location strategies. From space management and facilities maintenance to lease administration and capital project management, Tango's IWMS solutions streamline processes and improve resource utilization.

4.2 Precisely

Precisely_logo
Precisely, a renowned leader in data integrity has expanded its portfolio with the acquisition of PlaceIQ in 2022. This strategic move has enriched Precisely’s offerings with advanced location intelligence solutions, notably PlaceIQ Audiences and PlaceIQ Movement. These solutions empower businesses to leverage real-world behaviors and foot traffic insights, enabling targeted consumer engagement and informed decision-making.

PlaceIQ's platform equips businesses with powerful location-based insights, attribution, and measurement capabilities. Its sophisticated approach allows companies to understand and connect with location-based audiences, measure real-time ROI, and apply insights to drive intelligent marketing and business outcomes. Key features include implementing audience strategies with proven results, measuring foot traffic patterns for actionable insights, analyzing movement patterns for market insights, and accessing high-quality location data through subscription models.

4.3 Local Logic

Local Logic_Logo
Local Logic specializes in providing comprehensive location intelligence solutions tailored for the real estate sector. Founded in 2015, the company emerged from a commitment to enhancing urban livability through objective data and metrics. Headquartered in Montreal, Canada, Local Logic offers tools designed to quantify and analyze location-related data, aiding residential and commercial real estate professionals in making informed decisions across the United States and Canada.

Local Logic's provides comprehensive location intelligence solutions which offers granular demographic insights, proprietary location scores, detailed neighborhood profiles, real-time points of interest (POI) data, school-focused information, and climate risk assessments. These features enable users to access information crucial for evaluating property investments and matching properties with ideal lifestyles. By integrating diverse data points into intuitive tools and maps, Local Logic empowers clients to leverage data-driven decision-making in the competitive real estate market.

4.4 GBG

GBG_logo
Loqate, a GBG solution, provides comprehensive services focused on address verification, data validation, and geocoding. With a global reach, Loqate's solutions are designed to enhance data quality, improve customer experiences, and support efficient business operations across various industries.

Loqate offers various critical services, including real-time address capture, address verification for 249 countries and territories, data maintenance services, geocoding capabilities, and email, phone, and bank details validation. Additionally, their points of interest and store finder feature provides comprehensive POI data to enhance location-based services. These features help businesses maintain high-quality datasets, improve operational efficiency, reduce costs, ensure compliance, and scale with business needs, ultimately enhancing customer experience and satisfaction.

4.5 Nextbillion.ai

NextBillion.ai_logo
NextBillion.ai offers advanced routing and navigation solutions tailored to tackle complex logistical challenges across various industries. With an integrated platform leveraging AI-powered technology, NextBillion.ai provides a range of APIs and SDKs to optimize routing, enhance navigation, and ensure real-time tracking, ultimately improving operational efficiency and customer satisfaction.

NextBillion.ai's essential products and features include the Route Optimization API, which handles over 50 constraints and customizable parameters to generate optimized routes tailored to specific business needs. The Directions and Distance Matrix API processes large matrices for accurate ETAs and distances, with scalable pricing based on fleet size or order volume. Their Navigation API & SDKs offer turn-by-turn navigation across multiple platforms. At the same time, the Live Tracking API & SDKs provide real-time monitoring and comprehensive integration with telematics and CRM systems, enhancing fleet management and operational efficiency. These features cater to various industries, including supply chain and logistics, mobility and field services, offering scalability, cost efficiency, and developer-friendly integration processes.

4.6 GeoComply

GeoComply_logo
GeoComply is a renowned provider of geolocation compliance and anti-fraud solutions. Established in 2011, it aims to foster a safer internet environment by combating fraud and ensuring regulatory adherence across diverse sectors such as gaming, finance, and media. GeoComply offers a suite of critical products addressing various aspects of fraud prevention and compliance.

GeoComply Core provides precise geolocation data and anti-fraud solutions, which are crucial for industries like online gaming and financial services. GeoGuard, its award-winning solution, identifies and blocks fraudulent access through VPNs and proxies, safeguarding geolocation data integrity. IDComply offers comprehensive KYC & AML solutions, ensuring regulatory compliance with identity verification requirements. PinPoint provides custom geofencing solutions for on-property location compliance, while GeoComply Chargeback Integrator (GCI) automates chargeback management to combat fraud effectively. These solutions deliver enhanced security, regulatory compliance, operational efficiency, and revenue optimization benefits trusted by leading businesses worldwide.

4.7 Mapbox

Mapbox_logo
Mapbox is a premier provider of mapping and location data platforms, offering developers a suite of tools and services to craft bespoke maps, navigation systems, and geospatial data solutions. With a founding mission to democratize mapping and deliver highly customizable, performance-driven mapping solutions, Mapbox serves a diverse array of industries, including automotive, logistics, and mobile applications.

Mapbox's key features and services encompass various offerings tailored to diverse developer needs. Mapbox Studio enables users to create and customize map styles, incorporating features like 3D buildings and terrain contours for enhanced functionality and aesthetics. Navigation solutions include SDKs for mobile and automotive applications, featuring embedded routing, turn-by-turn navigation, and specialized solutions such as Mapbox for EVs, catering to electric vehicles with route planning and battery range predictions. Location data and analytics offerings include Directions and Matrix APIs for optimal route calculation along with real-time data integration for accurate, up-to-date maps and traffic information. Cross-platform development support extends to polished SDKs for web and mobile applications, facilitating seamless integration, high performance across devices, and offline map functionality crucial for areas with limited connectivity.

4.8 GapMaps

GapMaps_logo
GapMaps is a leading provider of cloud-based GIS mapping and location intelligence solutions. It empowers businesses to make informed decisions based on spatial data analysis. Its platform, GapMaps Live, offers a comprehensive suite of features tailored to various industries, including retail, healthcare, and real estate.GapMaps Live provides a user-friendly cloud-based platform for real-time data visualization and insights, facilitating customizable catchments for understanding customer behavior and optimizing store locations. Competitive analysis tools allow businesses to assess competitor locations and gain strategic insights.

GapMaps also offers GapAdvisory services, providing expert market planning, network, and growth strategies. GapInsite furnishes the latest industry intel, aiding businesses in understanding market conditions and customer behaviors. The GapMaps Connect Mobile App enables real-time field data collection and synchronization with GapMaps Live. Additionally, GapMaps offers comprehensive Point of Interest and demographics data globally, aiding businesses in market assessment and site selection.

4.9 Qlik

Qlik_logo
Qlik offers a comprehensive data integration and analytics platform designed to empower organizations to leverage their data to drive business outcomes. With a range of tools and capabilities supporting data integration, real-time analytics, and artificial intelligence, Qlik provides a robust solution for businesses across various industries.

Qlik Cloud Data Integration facilitates the creation of data pipelines to automate data movement and transformation alongside modern analytics capabilities such as self-service analytics and interactive dashboards. Qlik Sense, powered by its associative engine, enables users to explore data freely and generate insights efficiently through innovative visualizations and continuous learning resources like Qlik Continuous Classroom. The platform also emphasizes data quality and governance, ensuring data accuracy, completeness, and reliability while promoting a unified approach to management. Qlik Application Automation offers a no-code automation interface to seamlessly build automated analytics and data workflows.

4.10 Connectbase

Connectbase_logo
Connectbase offers a comprehensive platform called ‘The Connected World,’ designed to revolutionize the buying and selling of network connectivity through advanced location intelligence and automation. Tailored to improve efficiency and accuracy for network providers, this platform enables better decision-making and streamlined processes.

Connectbase provides highly accurate, location-specific data for over 1.4 billion buildings worldwide, including insights into building structures, tenant information, and competitive geography. Continuous updates ensure users have the most current information, reducing the risk of decision-making based on outdated data. The platform automates the quoting process, enabling quick and accurate responses to deals and eliminating delays caused by manual methods. Configure, Price, Quote (CPQ) functionality facilitates scalable, real-time quoting from configurable supplier product catalogs. Users can leverage real-time market and revenue data to make informed decisions about where to build new routes and expand their network, optimizing ROI. Tools for prospecting and pricing based on detailed market insights help businesses target sales efforts and maximize profitability. Centralized management of partner-building lists and product catalogs streamlines buying and selling processes.

5. Wrap Up

Location intelligence offers numerous benefits for businesses, including risk management, predictive analytics, and real-time trend tracking. It facilitates streamlined operations and services, enhancing efficiency across various functions. With the increasing digitization of business processes, organizations can gather more user information, driving further industry growth. Particularly for customer-facing businesses, leveraging real-time location data improves the in-store experience significantly. From sales and marketing to customer and facility management, location intelligence presents plenty of opportunities for businesses to optimize their operations and enhance their competitive edge.

Looking ahead, the future of location intelligence promises even more incredible advancements and opportunities for businesses. As technology evolves, we can expect enhanced precision and granularity in location data, enabling more accurate predictive analytics and risk management strategies. Integrating location intelligence with emerging technologies such as artificial intelligence and augmented reality will unlock new dimensions of customer engagement and personalized experiences. Moreover, with the increase of Internet of Things devices, businesses will have access to vast streams of real-time location data, empowering them to make proactive decisions and stay ahead in a dynamic market. Embracing these developments, businesses can remain agile, responsive, and poised for success in the ever-evolving digital ecosystem.

Spotlight

Tigerspike

Tigerspike is a global digital products company specializing in strategy, design, development and systems integration...

OTHER ARTICLES
Text Analytics, Business Intelligence, Data Visualization

Understanding Big Data and Artificial Intelligence

Article | June 21, 2024

Data is an important asset. Data leads to innovation and organizations tend to compete for leading these innovations on a global scale. Today, every business requires data and insights to stay relevant in the market. Big Data has a huge impact on the way organizations conduct their businesses. Big Data is used in different enterprises like travel, healthcare, manufacturing, governments, and more. If they need to determine their audience, understand what clients want, forecast the needs of the customers and the clients, AI and big data analysis is vital to every decision-making scenario. When companies process the collected data accurately, they get the desired results, which leads them to their desired goals. The term Big Data has been around since the 1990s. By the time we could fully comprehend it, Big Data had already amassed a huge amount of stored data. If this data is analyzed properly, it would reveal valuable industry insights into the industry to which the data belonged. IT professionals and computer scientists realized that going through all of the data and analyzing it for the purpose was too big of a task for humans to undertake. When artificial intelligence (AI) algorithm came into the picture, it accomplished analyzing the accumulated data and deriving insights. The use of AI in Big Data is fundamental to get desired results for organizations. According to Northeastern University, the amount of data in the world was 4.4 zettabytes in 2013. By of 2020, the data rose to 44 zettabytes. When there is this amount of data produced globally, this information is invaluable to the enterprises and now can leverage AI algorithms to process it. Because of this, the companies can understand and influence customer behavior. By 2018, over 50% of countries had adopted Big Data. Let us understand what Big Data, convergence of big data and AI, and impact of AI on big data analytics. Understanding Big Data In simple words, Big Data is a term that comprises every tool and process that helps people use and manage vast sets of data. According to Gartner, Big Data is a “high-volume and/or high-variety information assets that demand cost-effective, innovative forms of information processing to enable enhanced insight, decision-making, and process automation.” The concept of Big Data was created to capture trends, preferences, and user behavior in one place called the data lake. Big Data in enterprises can help them analyze and configure their customers’ motivations and come up with new ideas for the creation of new offerings. Big Data studies different methods of extracting, analyzing, or dealing with data sets that are too complicated for traditional data processing systems. To analyze a large amount of data requires a system designed to stretch its extraction and analysis capability. Data is everywhere. This stockpile of data can give us insights and business analytics to the industry belonging to the data set. Therefore, the AI algorithms are written to benefit from large and complex data. Importance of Big Data Data is an integral part of understanding customer demographics and their motivations. When customers interact with technology in active or passive manner, these actions create a new set of data. What contributes to this data creation is what they carry with them every day - their smartphones. Their cameras, credit cards, purchased products all contribute to their growing data profile. A correctly done analysis can tell a lot about their behavior patterns, personality, and events in the customer’s life. Companies can use this information to rethink their strategies, improve on their product, and create targeted marketing campaigns, which would ultimately lead them to their target customer. Industry experts, for years and years, have discussed Big Data and its impact on businesses. Only in recent years, however, has it become possible to calculate that impact. Algorithms and software can now analyze large datasets quickly and efficiently.The forty-four zettabyte of data will only quadruple in the coming years. This collection and analysis of the data will help companies get the AI insights that will aid them in generating profits and be future-ready. Organizations have been using Big Data for a long time. Here’s how those organizations are using Big Data to drive success: Answering customer questions Using big data and analytics, companies can learn the following things: • What do customers want? • Where are they missing out on? • Who are their best and loyal customers? • Why people choose different products? Every day, as organizations gather more information, they can get more insights into sales and marketing. Once they get this data, they can optimize their campaigns to suit the customer’s needs. Learning from their online habits and with correct analysis, companies can send personalized promotional emails. These emails may prompt this target audience to convert into full-time customers. Making confident decisions As companies grow, they all need to make complex decisions. With in-depth analysis of marketplace knowledge, industry, and customers, Big Data can help you make confident choices. Big Data gives you a complete overview of everything you need to know. With the help of this, you can launch your marketing campaign or launch a new product in the market, or make a focused decision to generate the highest ROI. Once you add machine learning and AI to the mix, your Big Data collections can form a neural network to help your AI suggest useful company changes. Optimizing and Understanding Business Processes Cloud computing and machine learning help you to stay ahead by identifying opportunities in your company’s practices. Big Data analytics can tell you if your email strategy is working even when your social media marketing isn’t gaining you any following. You can also check which parts of your company culture have the right impact and result in the desired turnover. The existing evidence can help you make quick decisions and ensure you spend more of your budget on things that help your business grow. Convergence of Big Data and AI Big Data and Artificial Intelligence have a synergistic relationship. Data powers AI. The constantly evolving data sets or Big Data makes it possible for machine learning applications to learn and acquire new skills. This is what they were built to do. Big Data’s role in AI is supplying algorithms with all the essential information for developing and improving features, pattern recognition capabilities. AI and machine learning use data that has been cleansed of duplicate and unnecessary data. This clean and high-quality big data is then utilized to create and train intelligent AI algorithms, neural networks, and predictive models. AI applications rarely stop working and learning. Once the “initial training” is done (initial training is preparing already collected data), they adjust their work as and when the data changes. This makes it necessary for data to be constantly collected. When it comes to businesses using this technology, AI helps them use Big Data for analytics by making advanced tools accessible and obtainable to help users gain insights that would otherwise have been hidden in the huge amount of data. Once firms and businesses gain a hold on using AI and Big Data, they can provide decision-makers with a clear understanding of factors that affect their businesses. Impact of AI on Big Data Analytics AI supports users in the Big Data cycle, including aggregation, storage, and retrieval of diverse data types from different data sources. This includes data management, context management, decision management, action management, and risk management. Big Data can help alert problems and help find new solutions and get ideas about any new prospects. With the amount of information stream that comes in, it can be difficult to determine what is important and what isn’t. This is where AI and machine learning come in. It can help identify unusual patterns in the processes, help in the analysis, and suggest further steps to be taken. It can also learn how users interact with analytics and learn subtle differences in meanings or context-specific nuances to understand numeric data sources. AI can also caution users about anomalies, unforeseen data patterns, monitoring events, and threats from system logs or social networking data. Application of Big Data and Artificial Intelligence After establishing how AI and Big Data work together, let us look at how some applications are benefitting from their synergy: Banking and financial sectors The banking and financial sectors apply these to monitor financial marketing activities. These institutions also use AI to keep an eye on any illegal trading activities. Trading data analytics are obtained for high-frequency trading, and decision making based on trading, risk analysis, and predictive analysis. It is also used for fraud warning and detection, archival and analysis of audit trails, reporting enterprise credit, customer data transformation, etc. Healthcare AI has simplified health data prescriptions and health analysis, thus benefitting healthcare providers from the large data pool. Hospitals are using millions of collected data that allow doctors to use evidence-based medicine. Chronic diseases can be tracked faster by AI. Manufacturing and supply chain AI and Big Data in manufacturing, production management, supply chain management and analysis, and customer satisfaction techniques are flawless. The quality of products is thus much better with higher energy efficiency, reliable increase in levels, and profit increase. Governments Governments worldwide use AI applications like facial recognition, vehicle recognition for traffic management, population demographics, financial classifications, energy explorations, environmental conservation, criminal investigations, and more. Other sectors that use AI are mainly retail, entertainment, education, and more. Conclusion According to Gartner’s predictions, artificial intelligence will replace one in five workers by 2022. Firms and businesses can no longer afford to avoid using artificial intelligence and Big Data in their day-to-day. Investments in AI and Big Data analysis will be beneficial for everyone. Data sets will increase in the future, and with it, its application and investment will grow over time. Human relevance will continue to decrease as time goes by. AI enables machine learning to be the future of the development of business technologies. It will automate data analysis and find new insights that were previously impossible to imagine by processing data manually. With machine learning, AI, and Big Data, we can redraw the way we approach everything else. Frequently Asked Questions Why does big data affect artificial intelligence? Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents. Is AI or big data better? AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale. Is AI used in big data? When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI. What is the future of AI in big data? AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why does big data affect artificial intelligence?", "acceptedAnswer": { "@type": "Answer", "text": "Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents." } },{ "@type": "Question", "name": "Is AI or big data better?", "acceptedAnswer": { "@type": "Answer", "text": "AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale." } },{ "@type": "Question", "name": "Is AI used in big data?", "acceptedAnswer": { "@type": "Answer", "text": "When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI." } },{ "@type": "Question", "name": "What is the future of AI in big data?", "acceptedAnswer": { "@type": "Answer", "text": "AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics." } }] }

Read More
Data Visualization

Advanced Data and Analytics Can Add Value in Private Equity Industry!

Article | March 15, 2024

As the organizations go digital the amount of data generated whether in-house or from outside is humongous. In fact, this data keeps increasing with every tick of the clock. There is no doubt about the fact that most of this data can be junk, however, at the same time this is also the data set from where an organization can get a whole lot of insight about itself. It is a given that organizations that don’t use this generated data to build value to their organization are prone to speed up their obsolescence or might be at the edge of losing the competitive edge in the market. Interestingly it is not just the larger firms that can harness this data and analytics to improve their overall performance while achieving operational excellence. Even the small size private equity firms can also leverage this data to create value and develop competitive edge. Thus private equity firms can achieve a high return on an initial investment that is low. Private Equity industry is skeptical about using data and analytics citing the reason that it is meant for larger firms or the firms that have deep pockets, which can afford the revamping cost or can replace their technology infrastructure. While there are few private equity investment professionals who may want to use this advanced data and analytics but are not able to do so for the lack of required knowledge. US Private Equity Firms are trying to understand the importance of advanced data and analytics and are thus seeking professionals with the expertise in dealing with data and advanced analytics. For private equity firms it is imperative to comprehend that data and analytics’ ability is to select the various use cases, which will offer the huge promise for creating value. Top Private Equity firms all over the world can utilize those use cases and create quick wins, which will in turn build momentum for wider transformation of businesses. Pinpointing the right use cases needs strategic thinking by private equity investment professionals, as they work on filling the relevant gaps or even address vulnerabilities. Private Equity professionals most of the time are also found thinking operationally to recognize where can they find the available data. Top private equity firms in the US have to realize that the insights which Big data and advanced analytics offer can result in an incredible opportunity for the growth of private equity industry. As Private Equity firms realize the potential and the power of big data and analytics they will understand the invaluableness of the insights offered by big data and analytics. Private Equity firms can use the analytics insights to study any target organization including its competitive position in the market and plan their next move that may include aggressive bidding for organizations that have shown promise for growth or leaving the organization that is stuffed with loads of underlying issues. But for all these and also to build careers in private equity it is important to have reputed qualification as well. A qualified private equity investment professional will be able to devise information-backed strategies in no time at all. In addition, with Big Data and analytics in place, private equity firms can let go of numerous tasks that are done manually and let the technology do the dirty work. There have been various studies that show how big data and analytics can help a private Equity firm.

Read More
Data Science

What is Data Integrity and Why is it Important?

Article | March 18, 2024

In an era of big data, data health has become a pressing issue when more and more data is being stored and processed. Therefore, preserving the integrity of the collected data is becoming increasingly necessary. Understanding the fundamentals of data integrity and how it works is the first step in safeguarding the data. Data integrity is essential for the smooth running of a company. If a company’s data is altered, deleted, or changed, and if there is no way of knowing how it can have significant impact on any data-driven business decisions. Data integrity is the reliability and trustworthiness of data throughout its lifecycle. It is the overall accuracy, completeness, and consistency of data. It can be indicated by lack of alteration between two updates of a data record, which means data is unchanged or intact. Data integrity refers to the safety of data regarding regulatory compliance- like GDPR compliance- and security. A collection of processes, rules, and standards implemented during the design phase maintains the safety and security of data. The information stored in the database will remain secure, complete, and reliable no matter how long it’s been stored; that’s when you know that the integrity of data is safe. A data integrity framework also ensures that no outside forces are harming this data. This term of data integrity may refer to either the state or a process. As a state, the data integrity framework defines a data set that is valid and accurate. Whereas as a process, it describes measures used to ensure validity and accuracy of data set or all data contained in a database or a construct. Data integrity can be enforced at both physical and logical levels. Let us understand the fundamentals of data integrity in detail: Types of Data Integrity There are two types of data integrity: physical and logical. They are collections of processes and methods that enforce data integrity in both hierarchical and relational databases. Physical Integrity Physical integrity protects the wholeness and accuracy of that data as it’s stored and retrieved. It refers to the process of storage and collection of data most accurately while maintaining the accuracy and reliability of data. The physical level of data integrity includes protecting data against different external forces like power cuts, data breaches, unexpected catastrophes, human-caused damages, and more. Logical Integrity Logical integrity keeps the data unchanged as it’s used in different ways in a relational database. Logical integrity checks data accuracy in a particular context. The logical integrity is compromised when errors from a human operator happen while entering data manually into the database. Other causes for compromised integrity of data include bugs, malware, and transferring data from one site within the database to another in the absence of some fields. There are four types of logical integrity: Entity Integrity A database has columns, rows, and tables. These elements need to be as numerous as required for the data to be accurate, but no more than necessary. Entity integrity relies on the primary key, the unique values that identify pieces of data, making sure the data is listed just once and not more to avoid a null field in the table. The feature of relational systems that store data in tables can be linked and utilized in different ways. Referential Integrity Referential integrity means a series of processes that ensure storage and uniform use of data. The database structure has rules embedded into them about the usage of foreign keys and ensures only proper changes, additions, or deletions of data occur. These rules can include limitations eliminating duplicate data entry, accurate data guarantee, and disallowance of data entry that doesn’t apply. Foreign keys relate data that can be shared or null. For example, let’s take a data integrity example, employees that share the same work or work in the same department. Domain Integrity Domain Integrity can be defined as a collection of processes ensuring the accuracy of each piece of data in a domain. A domain is a set of acceptable values a column is allowed to contain. It includes constraints that limit the format, type, and amount of data entered. In domain integrity, all values and categories are set. All categories and values in a database are set, including the nulls. User-Defined Integrity This type of logical integrity involves the user's constraints and rules to fit their specific requirements. The data isn’t always secure with entity, referential, or domain integrity. For example, if an employer creates a column to input corrective actions of the employees, this data would fall under user-defined integrity. Difference between Data Integrity and Data Security Often, the terms data security and data integrity get muddled and are used interchangeably. As a result, the term is incorrectly substituted for data integrity, but each term has a significant meaning. Data integrity and data security play an essential role in the success of each other. Data security means protecting data against unauthorized access or breach and is necessary to ensure data integrity. Data integrity is the result of successful data security. However, the term only refers to the validity and accuracy of data rather than the actual act of protecting data. Data security is one of the many ways to maintain data integrity. Data security focuses on reducing the risk of leaking intellectual property, business documents, healthcare data, emails, trade secrets, and more. Some facets of data security tactics include permissions management, data classification, identity, access management, threat detection, and security analytics. For modern enterprises, data integrity is necessary for accurate and efficient business processes and to make well-intentioned decisions. Data integrity is critical yet manageable for organizations today by backup and replication processes, database integrity constraints, validation processes, and other system protocols through varied data protection methods. Threats to Data Integrity Data integrity can be compromised by human error or any malicious acts. Accidental data alteration during the transfer from one device to another can be compromised. There is an assortment of factors that can affect the integrity of the data stored in databases. Following are a few of the examples: Human Error Data integrity is put in jeopardy when individuals enter information incorrectly, duplicate, or delete data, don’t follow the correct protocols, or make mistakes in implementing procedures to protect data. Transfer Error A transfer error occurs when data is incorrectly transferred from one location in a database to another. This error also happens when a piece of data is present in the destination table but not in the source table in a relational database. Bugs and Viruses Data can be stolen, altered, or deleted by spyware, malware, or any viruses. Compromised Hardware Hardware gets compromised when a computer crashes, a server gets down, or problems with any computer malfunctions. Data can be rendered incorrectly or incompletely, limit, or eliminate data access when hardware gets compromised. Preserving Data Integrity Companies make decisions based on data. If that data is compromised or incorrect, it could harm that company to a great extent. They routinely make data-driven business decisions, and without data integrity, those decisions can have a significant impact on the company’s goals. The threats mentioned above highlight a part of data security that can help preserve data integrity. Minimize the risk to your organization by using the following checklist: Validate Input Require an input validation when your data set is supplied by a known or an unknown source (an end-user, another application, a malicious user, or any number of other sources). The data should be validated and verified to ensure the correct input. Validate Data Verifying data processes haven’t been corrupted is highly critical. Identify key specifications and attributes that are necessary for your organization before you validate the data. Eliminate Duplicate Data Sensitive data from a secure database can easily be found on a document, spreadsheet, email, or shared folders where employees can see it without proper access. Therefore, it is sensible to clean up stray data and remove duplicates. Data Backup Data backups are a critical process in addition to removing duplicates and ensuring data security. Permanent loss of data can be avoided by backing up all necessary information, and it goes a long way. Back up the data as much as possible as it is critical as organizations may get attacked by ransomware. Access Control Another vital data security practice is access control. Individuals in an organization with any wrong intent can harm the data. Implement a model where users who need access can get access is also a successful form of access control. Sensitive servers should be isolated and bolted to the floor, with individuals with an access key are allowed to use them. Keep an Audit Trail In case of a data breach, an audit trail will help you track down your source. In addition, it serves as breadcrumbs to locate and pinpoint the individual and origin of the breach. Conclusion Data collection was difficult not too long ago. It is no longer an issue these days. With the amount of data being collected these days, we must maintain the integrity of the data. Organizations can thus make data-driven decisions confidently and take the company ahead in a proper direction. Frequently Asked Questions What are integrity rules? Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution. What is a data integrity example? Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are: • When a user tries to enter a date outside an acceptable range • When a user tries to enter a phone number in the wrong format • When a bug in an application attempts to delete the wrong record What are the principles of data integrity? The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are integrity rules?", "acceptedAnswer": { "@type": "Answer", "text": "Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution." } },{ "@type": "Question", "name": "What is a data integrity example?", "acceptedAnswer": { "@type": "Answer", "text": "Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are: When a user tries to enter a date outside an acceptable range When a user tries to enter a phone number in the wrong format When a bug in an application attempts to delete the wrong record" } },{ "@type": "Question", "name": "What are the principles of data integrity?", "acceptedAnswer": { "@type": "Answer", "text": "The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives." } }] }

Read More
Data Science

7 Top Data Analytics Trends

Article | March 31, 2022

The COVID-19 compelled organizations utilizing traditional analytics methods to accept digital data analytics platforms. The pandemic has also accelerated the digital revolution, and as we already know, data and analytics with technologies like AI, NLP, and ML have become the heart of this digital revolution. Therefore, this is the perfect time to break through data, analytics, and AI to make the most of it and stay a step ahead of competitors. Besides that, Techjury says that by 2023, the big data analytics market is expected to be worth $103 billion. This shows how quickly the field of data analytics is growing. Today, the data analytics market has numerous tools and strategies evolving rapidly to keep up with the ever-increasing volume of data gathered and used by businesses. Considering the swift pace and increasing use of data analytics, it is crucial to keep upgrading to stay ahead of the curve. But before we explore the leading data analytics trends, let's check out some data analytics use cases. Data Analytics Use Cases Customer Relationship Analytics One of the biggest challenges is recognizing clients who will spend money continuously for a long period purchasing their products. This insight will assist businesses in attracting customers who will add long-term value to their business. Product Propensity Product propensity analytics combines data on buying actions and behaviors with online behavioral indicators from social media and e-commerce to give insight into the performance of various campaigns and social media platforms promoting the products and services of your company. This enables your business to forecast which clients are most likely to purchase your products and services and which channels are most likely to reach those customers. This lets you focus on the channels that have the best chance of making a lot of money. Recommendation Engines There are recommendations on YouTube, Spotify, Amazon Prime Videos, or other media sites, "recommendations for you." These customized recommendations help users save time and improve their entire customer experience. Top Data Analytics Trends That Will Shape 2022 1. Data Fabrics Architecture The goal of data fabric is to design an exemplary architecture and advise on when data should be delivered or changed. Since data technology designs majorly rely on the ability to use, reuse, and mix numerous data integration techniques, the data fabric reduces integration data technology design time by 30%, deployment time by 30%, and maintenance time by 70%. "The data fabric is the next middleware." -ex-CTO of Splunk, Todd Papaioannou, 2. Decision Intelligence Decision intelligence directly incorporates data analytics into the decision process, with feedback loops to refine and fine-tune the process further. Decision intelligence can be utilized to assist in making decisions, but it also employs techniques like digital twin simulations, reinforcement learning, and artificial intelligence to automate decisions where necessary. 3. XOps With artificial intelligence (AI) and data analytics throughout any firm, XOps has become an essential aspect of business transformation operations. XOps uses DevOps best practices to improve corporate operations, efficiency, and customer experience. In addition, it wants to make sure that the process is reliable, reusable, and repeatable and that there is less technology and process duplication. 4. Graph Analytics Gartner predicts that by 2025, 80% of data and analytics innovation will be developed with the help of graphs. Graph analytics uses engaging algorithms to correlate multiple data points scattered across numerous data assets by exploring relationships. The AI graph is the backbone of modern data and analytics with the help of its expandable features and capability to increase user collaboration and machine learning models. 5. Augmented Analytics Augmented Analytics is another data-trend technology that is gaining prominence. Machine learning, AI, and natural language processing (NLP) are used in augmented analytics to automate data insights for business intelligence, data preparation, discovery, and sharing. The insights provided through augmented analytics help businesses make better decisions. According to Allied Market Research, the worldwide augmented analytics market is expected to reach $29,856 million by 2025. 6. Self-Service Analytics-Low-code and no-code AI Low-code and no-code digital platforms are speeding up the transition to self-service analytics. Non-technical business users can now access data, get insights, and make faster choices because of these platforms. As a result, self-service analytics boosts response times, business agility, speed-to-market, and decision-making in today's modern world. 7. Privacy-Enhancing Computation With the amount of sensitive and personal data being gathered, saved, and processed, it has become imperative to protect consumers' privacy. As regulations become strict and customers become more concerned, new ways to protect their privacy are becoming more important. Privacy-enhancing computing makes sure that value can be extracted from the data with the help of big data analytics without breaking the rules of the game. 3 Ways in Which the C-Suite Can Ensure Enhanced Use of Data Analytics There are many businesses that fail to realize the benefits of data analytics. Here are some ways the C-suite can ensure enhanced use of data analytics. Use Data Analytics for Recommendations Often, the deployment of data analytics is considered a one-time mission instead of an ongoing, interactive process. According to recent McKinsey research, employees are considerably more inclined to data analytics if their leaders actively commit. If the C-suite starts using analytics for decision-making, it will set an example and establish a reliability factor. This shows that when leaders rely on the suggestions and insights of data analytics platforms, rest of the company will follow the C-suite. This will result in broad usage, better success, and higher adoption rates of data analytics. Establish Data Analytics Mind-Sets Senior management starting on this path should learn about data analytics to comprehend what's fast becoming possible. Then they can use the question, "Where might data analytics bring quantum leaps in performance?" to promote lasting behavioral changes throughout the business. A senior executive should lead this exercise with the power and influence to encourage action throughout each critical business unit or function. Use Machine Learning to Automate Decisions The C-suite is introducing machine learning as they are recognizing its value for various departments and processes in an organization either processing or fraud monitoring. 79% of the executives believe that AI will make their jobs more efficient and manageable. Therefore, C-level executives would make an effort to ensure the rest of the organization follows that mentality. They will have to start by using machine learning to automate time-consuming and repeatable tasks. Conclusion From the above-mentioned data analytics trends one can infer that it is no longer only a means to achieve corporate success. In 2022 and beyond, businesses will need to prioritize it as a critical business function, accurately recognizing it as a must-have for long-term success. The future of data analytics will have quality data and technologies like AI at its center. FAQ 1. What is the difference between data analytics and data analysis? Scalability is the key distinguishing factor between analytics and analysis. Data analytics is a broad phrase that encompasses all types of data analysis. The evaluation of data is known as data analysis. Data analysis includes data gathering, organization, storage, and analysis techniques and technologies. 2. When is the right time to deploy an analytics strategy? Data analytics is not a one-time-only activity; it is a continuous process. Companies should not shift their attention from analytics and should utilize it regularly. Usually, once companies realize the potential of analytics to address concerns, they start applying it to various processes. 3. What is platform modernization? Modernization of legacy platforms refers to leveraging and expanding flexibility by preserving consistency across platforms and tackling IT issues. Modernization of legacy platforms also includes rewriting a legacy system for software development.

Read More
Text Analytics, Business Intelligence, Data Visualization

Top Text Analytics Software Providers That Are Powering Businesses

Article | June 21, 2024

Data is an important asset. Data leads to innovation and organizations tend to compete for leading these innovations on a global scale. Today, every business requires data and insights to stay relevant in the market. Big Data has a huge impact on the way organizations conduct their businesses. Big Data is used in different enterprises like travel, healthcare, manufacturing, governments, and more. If they need to determine their audience, understand what clients want, forecast the needs of the customers and the clients, AI and big data analysis is vital to every decision-making scenario. When companies process the collected data accurately, they get the desired results, which leads them to their desired goals. The term Big Data has been around since the 1990s. By the time we could fully comprehend it, Big Data had already amassed a huge amount of stored data. If this data is analyzed properly, it would reveal valuable industry insights into the industry to which the data belonged. IT professionals and computer scientists realized that going through all of the data and analyzing it for the purpose was too big of a task for humans to undertake. When artificial intelligence (AI) algorithm came into the picture, it accomplished analyzing the accumulated data and deriving insights. The use of AI in Big Data is fundamental to get desired results for organizations. According to Northeastern University, the amount of data in the world was 4.4 zettabytes in 2013. By of 2020, the data rose to 44 zettabytes. When there is this amount of data produced globally, this information is invaluable to the enterprises and now can leverage AI algorithms to process it. Because of this, the companies can understand and influence customer behavior. By 2018, over 50% of countries had adopted Big Data. Let us understand what Big Data, convergence of big data and AI, and impact of AI on big data analytics. Understanding Big Data In simple words, Big Data is a term that comprises every tool and process that helps people use and manage vast sets of data. According to Gartner, Big Data is a “high-volume and/or high-variety information assets that demand cost-effective, innovative forms of information processing to enable enhanced insight, decision-making, and process automation.” The concept of Big Data was created to capture trends, preferences, and user behavior in one place called the data lake. Big Data in enterprises can help them analyze and configure their customers’ motivations and come up with new ideas for the creation of new offerings. Big Data studies different methods of extracting, analyzing, or dealing with data sets that are too complicated for traditional data processing systems. To analyze a large amount of data requires a system designed to stretch its extraction and analysis capability. Data is everywhere. This stockpile of data can give us insights and business analytics to the industry belonging to the data set. Therefore, the AI algorithms are written to benefit from large and complex data. Importance of Big Data Data is an integral part of understanding customer demographics and their motivations. When customers interact with technology in active or passive manner, these actions create a new set of data. What contributes to this data creation is what they carry with them every day - their smartphones. Their cameras, credit cards, purchased products all contribute to their growing data profile. A correctly done analysis can tell a lot about their behavior patterns, personality, and events in the customer’s life. Companies can use this information to rethink their strategies, improve on their product, and create targeted marketing campaigns, which would ultimately lead them to their target customer. Industry experts, for years and years, have discussed Big Data and its impact on businesses. Only in recent years, however, has it become possible to calculate that impact. Algorithms and software can now analyze large datasets quickly and efficiently.The forty-four zettabyte of data will only quadruple in the coming years. This collection and analysis of the data will help companies get the AI insights that will aid them in generating profits and be future-ready. Organizations have been using Big Data for a long time. Here’s how those organizations are using Big Data to drive success: Answering customer questions Using big data and analytics, companies can learn the following things: • What do customers want? • Where are they missing out on? • Who are their best and loyal customers? • Why people choose different products? Every day, as organizations gather more information, they can get more insights into sales and marketing. Once they get this data, they can optimize their campaigns to suit the customer’s needs. Learning from their online habits and with correct analysis, companies can send personalized promotional emails. These emails may prompt this target audience to convert into full-time customers. Making confident decisions As companies grow, they all need to make complex decisions. With in-depth analysis of marketplace knowledge, industry, and customers, Big Data can help you make confident choices. Big Data gives you a complete overview of everything you need to know. With the help of this, you can launch your marketing campaign or launch a new product in the market, or make a focused decision to generate the highest ROI. Once you add machine learning and AI to the mix, your Big Data collections can form a neural network to help your AI suggest useful company changes. Optimizing and Understanding Business Processes Cloud computing and machine learning help you to stay ahead by identifying opportunities in your company’s practices. Big Data analytics can tell you if your email strategy is working even when your social media marketing isn’t gaining you any following. You can also check which parts of your company culture have the right impact and result in the desired turnover. The existing evidence can help you make quick decisions and ensure you spend more of your budget on things that help your business grow. Convergence of Big Data and AI Big Data and Artificial Intelligence have a synergistic relationship. Data powers AI. The constantly evolving data sets or Big Data makes it possible for machine learning applications to learn and acquire new skills. This is what they were built to do. Big Data’s role in AI is supplying algorithms with all the essential information for developing and improving features, pattern recognition capabilities. AI and machine learning use data that has been cleansed of duplicate and unnecessary data. This clean and high-quality big data is then utilized to create and train intelligent AI algorithms, neural networks, and predictive models. AI applications rarely stop working and learning. Once the “initial training” is done (initial training is preparing already collected data), they adjust their work as and when the data changes. This makes it necessary for data to be constantly collected. When it comes to businesses using this technology, AI helps them use Big Data for analytics by making advanced tools accessible and obtainable to help users gain insights that would otherwise have been hidden in the huge amount of data. Once firms and businesses gain a hold on using AI and Big Data, they can provide decision-makers with a clear understanding of factors that affect their businesses. Impact of AI on Big Data Analytics AI supports users in the Big Data cycle, including aggregation, storage, and retrieval of diverse data types from different data sources. This includes data management, context management, decision management, action management, and risk management. Big Data can help alert problems and help find new solutions and get ideas about any new prospects. With the amount of information stream that comes in, it can be difficult to determine what is important and what isn’t. This is where AI and machine learning come in. It can help identify unusual patterns in the processes, help in the analysis, and suggest further steps to be taken. It can also learn how users interact with analytics and learn subtle differences in meanings or context-specific nuances to understand numeric data sources. AI can also caution users about anomalies, unforeseen data patterns, monitoring events, and threats from system logs or social networking data. Application of Big Data and Artificial Intelligence After establishing how AI and Big Data work together, let us look at how some applications are benefitting from their synergy: Banking and financial sectors The banking and financial sectors apply these to monitor financial marketing activities. These institutions also use AI to keep an eye on any illegal trading activities. Trading data analytics are obtained for high-frequency trading, and decision making based on trading, risk analysis, and predictive analysis. It is also used for fraud warning and detection, archival and analysis of audit trails, reporting enterprise credit, customer data transformation, etc. Healthcare AI has simplified health data prescriptions and health analysis, thus benefitting healthcare providers from the large data pool. Hospitals are using millions of collected data that allow doctors to use evidence-based medicine. Chronic diseases can be tracked faster by AI. Manufacturing and supply chain AI and Big Data in manufacturing, production management, supply chain management and analysis, and customer satisfaction techniques are flawless. The quality of products is thus much better with higher energy efficiency, reliable increase in levels, and profit increase. Governments Governments worldwide use AI applications like facial recognition, vehicle recognition for traffic management, population demographics, financial classifications, energy explorations, environmental conservation, criminal investigations, and more. Other sectors that use AI are mainly retail, entertainment, education, and more. Conclusion According to Gartner’s predictions, artificial intelligence will replace one in five workers by 2022. Firms and businesses can no longer afford to avoid using artificial intelligence and Big Data in their day-to-day. Investments in AI and Big Data analysis will be beneficial for everyone. Data sets will increase in the future, and with it, its application and investment will grow over time. Human relevance will continue to decrease as time goes by. AI enables machine learning to be the future of the development of business technologies. It will automate data analysis and find new insights that were previously impossible to imagine by processing data manually. With machine learning, AI, and Big Data, we can redraw the way we approach everything else. Frequently Asked Questions Why does big data affect artificial intelligence? Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents. Is AI or big data better? AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale. Is AI used in big data? When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI. What is the future of AI in big data? AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Why does big data affect artificial intelligence?", "acceptedAnswer": { "@type": "Answer", "text": "Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents." } },{ "@type": "Question", "name": "Is AI or big data better?", "acceptedAnswer": { "@type": "Answer", "text": "AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale." } },{ "@type": "Question", "name": "Is AI used in big data?", "acceptedAnswer": { "@type": "Answer", "text": "When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI." } },{ "@type": "Question", "name": "What is the future of AI in big data?", "acceptedAnswer": { "@type": "Answer", "text": "AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics." } }] }

Read More
Data Visualization

Unleashing Data Visualization Strategies for Maximum Impact

Article | March 15, 2024

As the organizations go digital the amount of data generated whether in-house or from outside is humongous. In fact, this data keeps increasing with every tick of the clock. There is no doubt about the fact that most of this data can be junk, however, at the same time this is also the data set from where an organization can get a whole lot of insight about itself. It is a given that organizations that don’t use this generated data to build value to their organization are prone to speed up their obsolescence or might be at the edge of losing the competitive edge in the market. Interestingly it is not just the larger firms that can harness this data and analytics to improve their overall performance while achieving operational excellence. Even the small size private equity firms can also leverage this data to create value and develop competitive edge. Thus private equity firms can achieve a high return on an initial investment that is low. Private Equity industry is skeptical about using data and analytics citing the reason that it is meant for larger firms or the firms that have deep pockets, which can afford the revamping cost or can replace their technology infrastructure. While there are few private equity investment professionals who may want to use this advanced data and analytics but are not able to do so for the lack of required knowledge. US Private Equity Firms are trying to understand the importance of advanced data and analytics and are thus seeking professionals with the expertise in dealing with data and advanced analytics. For private equity firms it is imperative to comprehend that data and analytics’ ability is to select the various use cases, which will offer the huge promise for creating value. Top Private Equity firms all over the world can utilize those use cases and create quick wins, which will in turn build momentum for wider transformation of businesses. Pinpointing the right use cases needs strategic thinking by private equity investment professionals, as they work on filling the relevant gaps or even address vulnerabilities. Private Equity professionals most of the time are also found thinking operationally to recognize where can they find the available data. Top private equity firms in the US have to realize that the insights which Big data and advanced analytics offer can result in an incredible opportunity for the growth of private equity industry. As Private Equity firms realize the potential and the power of big data and analytics they will understand the invaluableness of the insights offered by big data and analytics. Private Equity firms can use the analytics insights to study any target organization including its competitive position in the market and plan their next move that may include aggressive bidding for organizations that have shown promise for growth or leaving the organization that is stuffed with loads of underlying issues. But for all these and also to build careers in private equity it is important to have reputed qualification as well. A qualified private equity investment professional will be able to devise information-backed strategies in no time at all. In addition, with Big Data and analytics in place, private equity firms can let go of numerous tasks that are done manually and let the technology do the dirty work. There have been various studies that show how big data and analytics can help a private Equity firm.

Read More
Data Science

Predictive Analytics Tools for Data-Driven Insights

Article | March 18, 2024

In an era of big data, data health has become a pressing issue when more and more data is being stored and processed. Therefore, preserving the integrity of the collected data is becoming increasingly necessary. Understanding the fundamentals of data integrity and how it works is the first step in safeguarding the data. Data integrity is essential for the smooth running of a company. If a company’s data is altered, deleted, or changed, and if there is no way of knowing how it can have significant impact on any data-driven business decisions. Data integrity is the reliability and trustworthiness of data throughout its lifecycle. It is the overall accuracy, completeness, and consistency of data. It can be indicated by lack of alteration between two updates of a data record, which means data is unchanged or intact. Data integrity refers to the safety of data regarding regulatory compliance- like GDPR compliance- and security. A collection of processes, rules, and standards implemented during the design phase maintains the safety and security of data. The information stored in the database will remain secure, complete, and reliable no matter how long it’s been stored; that’s when you know that the integrity of data is safe. A data integrity framework also ensures that no outside forces are harming this data. This term of data integrity may refer to either the state or a process. As a state, the data integrity framework defines a data set that is valid and accurate. Whereas as a process, it describes measures used to ensure validity and accuracy of data set or all data contained in a database or a construct. Data integrity can be enforced at both physical and logical levels. Let us understand the fundamentals of data integrity in detail: Types of Data Integrity There are two types of data integrity: physical and logical. They are collections of processes and methods that enforce data integrity in both hierarchical and relational databases. Physical Integrity Physical integrity protects the wholeness and accuracy of that data as it’s stored and retrieved. It refers to the process of storage and collection of data most accurately while maintaining the accuracy and reliability of data. The physical level of data integrity includes protecting data against different external forces like power cuts, data breaches, unexpected catastrophes, human-caused damages, and more. Logical Integrity Logical integrity keeps the data unchanged as it’s used in different ways in a relational database. Logical integrity checks data accuracy in a particular context. The logical integrity is compromised when errors from a human operator happen while entering data manually into the database. Other causes for compromised integrity of data include bugs, malware, and transferring data from one site within the database to another in the absence of some fields. There are four types of logical integrity: Entity Integrity A database has columns, rows, and tables. These elements need to be as numerous as required for the data to be accurate, but no more than necessary. Entity integrity relies on the primary key, the unique values that identify pieces of data, making sure the data is listed just once and not more to avoid a null field in the table. The feature of relational systems that store data in tables can be linked and utilized in different ways. Referential Integrity Referential integrity means a series of processes that ensure storage and uniform use of data. The database structure has rules embedded into them about the usage of foreign keys and ensures only proper changes, additions, or deletions of data occur. These rules can include limitations eliminating duplicate data entry, accurate data guarantee, and disallowance of data entry that doesn’t apply. Foreign keys relate data that can be shared or null. For example, let’s take a data integrity example, employees that share the same work or work in the same department. Domain Integrity Domain Integrity can be defined as a collection of processes ensuring the accuracy of each piece of data in a domain. A domain is a set of acceptable values a column is allowed to contain. It includes constraints that limit the format, type, and amount of data entered. In domain integrity, all values and categories are set. All categories and values in a database are set, including the nulls. User-Defined Integrity This type of logical integrity involves the user's constraints and rules to fit their specific requirements. The data isn’t always secure with entity, referential, or domain integrity. For example, if an employer creates a column to input corrective actions of the employees, this data would fall under user-defined integrity. Difference between Data Integrity and Data Security Often, the terms data security and data integrity get muddled and are used interchangeably. As a result, the term is incorrectly substituted for data integrity, but each term has a significant meaning. Data integrity and data security play an essential role in the success of each other. Data security means protecting data against unauthorized access or breach and is necessary to ensure data integrity. Data integrity is the result of successful data security. However, the term only refers to the validity and accuracy of data rather than the actual act of protecting data. Data security is one of the many ways to maintain data integrity. Data security focuses on reducing the risk of leaking intellectual property, business documents, healthcare data, emails, trade secrets, and more. Some facets of data security tactics include permissions management, data classification, identity, access management, threat detection, and security analytics. For modern enterprises, data integrity is necessary for accurate and efficient business processes and to make well-intentioned decisions. Data integrity is critical yet manageable for organizations today by backup and replication processes, database integrity constraints, validation processes, and other system protocols through varied data protection methods. Threats to Data Integrity Data integrity can be compromised by human error or any malicious acts. Accidental data alteration during the transfer from one device to another can be compromised. There is an assortment of factors that can affect the integrity of the data stored in databases. Following are a few of the examples: Human Error Data integrity is put in jeopardy when individuals enter information incorrectly, duplicate, or delete data, don’t follow the correct protocols, or make mistakes in implementing procedures to protect data. Transfer Error A transfer error occurs when data is incorrectly transferred from one location in a database to another. This error also happens when a piece of data is present in the destination table but not in the source table in a relational database. Bugs and Viruses Data can be stolen, altered, or deleted by spyware, malware, or any viruses. Compromised Hardware Hardware gets compromised when a computer crashes, a server gets down, or problems with any computer malfunctions. Data can be rendered incorrectly or incompletely, limit, or eliminate data access when hardware gets compromised. Preserving Data Integrity Companies make decisions based on data. If that data is compromised or incorrect, it could harm that company to a great extent. They routinely make data-driven business decisions, and without data integrity, those decisions can have a significant impact on the company’s goals. The threats mentioned above highlight a part of data security that can help preserve data integrity. Minimize the risk to your organization by using the following checklist: Validate Input Require an input validation when your data set is supplied by a known or an unknown source (an end-user, another application, a malicious user, or any number of other sources). The data should be validated and verified to ensure the correct input. Validate Data Verifying data processes haven’t been corrupted is highly critical. Identify key specifications and attributes that are necessary for your organization before you validate the data. Eliminate Duplicate Data Sensitive data from a secure database can easily be found on a document, spreadsheet, email, or shared folders where employees can see it without proper access. Therefore, it is sensible to clean up stray data and remove duplicates. Data Backup Data backups are a critical process in addition to removing duplicates and ensuring data security. Permanent loss of data can be avoided by backing up all necessary information, and it goes a long way. Back up the data as much as possible as it is critical as organizations may get attacked by ransomware. Access Control Another vital data security practice is access control. Individuals in an organization with any wrong intent can harm the data. Implement a model where users who need access can get access is also a successful form of access control. Sensitive servers should be isolated and bolted to the floor, with individuals with an access key are allowed to use them. Keep an Audit Trail In case of a data breach, an audit trail will help you track down your source. In addition, it serves as breadcrumbs to locate and pinpoint the individual and origin of the breach. Conclusion Data collection was difficult not too long ago. It is no longer an issue these days. With the amount of data being collected these days, we must maintain the integrity of the data. Organizations can thus make data-driven decisions confidently and take the company ahead in a proper direction. Frequently Asked Questions What are integrity rules? Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution. What is a data integrity example? Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are: • When a user tries to enter a date outside an acceptable range • When a user tries to enter a phone number in the wrong format • When a bug in an application attempts to delete the wrong record What are the principles of data integrity? The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are integrity rules?", "acceptedAnswer": { "@type": "Answer", "text": "Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution." } },{ "@type": "Question", "name": "What is a data integrity example?", "acceptedAnswer": { "@type": "Answer", "text": "Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are: When a user tries to enter a date outside an acceptable range When a user tries to enter a phone number in the wrong format When a bug in an application attempts to delete the wrong record" } },{ "@type": "Question", "name": "What are the principles of data integrity?", "acceptedAnswer": { "@type": "Answer", "text": "The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives." } }] }

Read More
Data Science

7 Top Data Analytics Trends

Article | March 31, 2022

The COVID-19 compelled organizations utilizing traditional analytics methods to accept digital data analytics platforms. The pandemic has also accelerated the digital revolution, and as we already know, data and analytics with technologies like AI, NLP, and ML have become the heart of this digital revolution. Therefore, this is the perfect time to break through data, analytics, and AI to make the most of it and stay a step ahead of competitors. Besides that, Techjury says that by 2023, the big data analytics market is expected to be worth $103 billion. This shows how quickly the field of data analytics is growing. Today, the data analytics market has numerous tools and strategies evolving rapidly to keep up with the ever-increasing volume of data gathered and used by businesses. Considering the swift pace and increasing use of data analytics, it is crucial to keep upgrading to stay ahead of the curve. But before we explore the leading data analytics trends, let's check out some data analytics use cases. Data Analytics Use Cases Customer Relationship Analytics One of the biggest challenges is recognizing clients who will spend money continuously for a long period purchasing their products. This insight will assist businesses in attracting customers who will add long-term value to their business. Product Propensity Product propensity analytics combines data on buying actions and behaviors with online behavioral indicators from social media and e-commerce to give insight into the performance of various campaigns and social media platforms promoting the products and services of your company. This enables your business to forecast which clients are most likely to purchase your products and services and which channels are most likely to reach those customers. This lets you focus on the channels that have the best chance of making a lot of money. Recommendation Engines There are recommendations on YouTube, Spotify, Amazon Prime Videos, or other media sites, "recommendations for you." These customized recommendations help users save time and improve their entire customer experience. Top Data Analytics Trends That Will Shape 2022 1. Data Fabrics Architecture The goal of data fabric is to design an exemplary architecture and advise on when data should be delivered or changed. Since data technology designs majorly rely on the ability to use, reuse, and mix numerous data integration techniques, the data fabric reduces integration data technology design time by 30%, deployment time by 30%, and maintenance time by 70%. "The data fabric is the next middleware." -ex-CTO of Splunk, Todd Papaioannou, 2. Decision Intelligence Decision intelligence directly incorporates data analytics into the decision process, with feedback loops to refine and fine-tune the process further. Decision intelligence can be utilized to assist in making decisions, but it also employs techniques like digital twin simulations, reinforcement learning, and artificial intelligence to automate decisions where necessary. 3. XOps With artificial intelligence (AI) and data analytics throughout any firm, XOps has become an essential aspect of business transformation operations. XOps uses DevOps best practices to improve corporate operations, efficiency, and customer experience. In addition, it wants to make sure that the process is reliable, reusable, and repeatable and that there is less technology and process duplication. 4. Graph Analytics Gartner predicts that by 2025, 80% of data and analytics innovation will be developed with the help of graphs. Graph analytics uses engaging algorithms to correlate multiple data points scattered across numerous data assets by exploring relationships. The AI graph is the backbone of modern data and analytics with the help of its expandable features and capability to increase user collaboration and machine learning models. 5. Augmented Analytics Augmented Analytics is another data-trend technology that is gaining prominence. Machine learning, AI, and natural language processing (NLP) are used in augmented analytics to automate data insights for business intelligence, data preparation, discovery, and sharing. The insights provided through augmented analytics help businesses make better decisions. According to Allied Market Research, the worldwide augmented analytics market is expected to reach $29,856 million by 2025. 6. Self-Service Analytics-Low-code and no-code AI Low-code and no-code digital platforms are speeding up the transition to self-service analytics. Non-technical business users can now access data, get insights, and make faster choices because of these platforms. As a result, self-service analytics boosts response times, business agility, speed-to-market, and decision-making in today's modern world. 7. Privacy-Enhancing Computation With the amount of sensitive and personal data being gathered, saved, and processed, it has become imperative to protect consumers' privacy. As regulations become strict and customers become more concerned, new ways to protect their privacy are becoming more important. Privacy-enhancing computing makes sure that value can be extracted from the data with the help of big data analytics without breaking the rules of the game. 3 Ways in Which the C-Suite Can Ensure Enhanced Use of Data Analytics There are many businesses that fail to realize the benefits of data analytics. Here are some ways the C-suite can ensure enhanced use of data analytics. Use Data Analytics for Recommendations Often, the deployment of data analytics is considered a one-time mission instead of an ongoing, interactive process. According to recent McKinsey research, employees are considerably more inclined to data analytics if their leaders actively commit. If the C-suite starts using analytics for decision-making, it will set an example and establish a reliability factor. This shows that when leaders rely on the suggestions and insights of data analytics platforms, rest of the company will follow the C-suite. This will result in broad usage, better success, and higher adoption rates of data analytics. Establish Data Analytics Mind-Sets Senior management starting on this path should learn about data analytics to comprehend what's fast becoming possible. Then they can use the question, "Where might data analytics bring quantum leaps in performance?" to promote lasting behavioral changes throughout the business. A senior executive should lead this exercise with the power and influence to encourage action throughout each critical business unit or function. Use Machine Learning to Automate Decisions The C-suite is introducing machine learning as they are recognizing its value for various departments and processes in an organization either processing or fraud monitoring. 79% of the executives believe that AI will make their jobs more efficient and manageable. Therefore, C-level executives would make an effort to ensure the rest of the organization follows that mentality. They will have to start by using machine learning to automate time-consuming and repeatable tasks. Conclusion From the above-mentioned data analytics trends one can infer that it is no longer only a means to achieve corporate success. In 2022 and beyond, businesses will need to prioritize it as a critical business function, accurately recognizing it as a must-have for long-term success. The future of data analytics will have quality data and technologies like AI at its center. FAQ 1. What is the difference between data analytics and data analysis? Scalability is the key distinguishing factor between analytics and analysis. Data analytics is a broad phrase that encompasses all types of data analysis. The evaluation of data is known as data analysis. Data analysis includes data gathering, organization, storage, and analysis techniques and technologies. 2. When is the right time to deploy an analytics strategy? Data analytics is not a one-time-only activity; it is a continuous process. Companies should not shift their attention from analytics and should utilize it regularly. Usually, once companies realize the potential of analytics to address concerns, they start applying it to various processes. 3. What is platform modernization? Modernization of legacy platforms refers to leveraging and expanding flexibility by preserving consistency across platforms and tackling IT issues. Modernization of legacy platforms also includes rewriting a legacy system for software development.

Read More
MORE ARTICLES

Spotlight

Tigerspike

Tigerspike is a global digital products company specializing in strategy, design, development and systems integration...

Related News

Big Data

Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

Read More

Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

Read More

Big Data Management

data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

Read More

Big Data

Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

Read More

Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

Read More

Big Data Management

data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

Read More

Events