7 Reasons Why Business Intelligence (BI) is Crucial

7 Reasons Why Business Intelligence (BI) is Crucial

In today’s digital and customer-centric world, businesses are facing stiff competition. Most of these businesses are bombarded with information and are actively exploring ways to derive significant insights and control from the data gathered. For businesses to resolve the issue of data overloading, obtain a competitive edge in the market, and make informed decisions, there is a need to adopt business intelligence. Unfortunately, even with the long list of benefits and the increasing number of users, most companies are very slow in adopting it. Business intelligence empowers you to combine the power of technology and business expertise to make informed decisions and outplay competitors. According to Techjury, more than 46% of businesses are already using a business intelligence tool as a core part of their business strategy.


Swain Scheps rightly highlights the importance of business intelligence in his quote:

“Business intelligence is essentially timely, accurate, high-value, and actionable business insights, and the work processes and technologies used to obtain them.”

Business Intelligence VS Business Analytics

Business intelligence and business analytics are often considered synonyms with the same meaning, definition, and method of working, but that's not the case.

Business intelligence refers to technologies and strategies developed by enterprise industries to analyze existing business data and provide historical, current, and predictive events for business operations. Present-day businesses are widely accepting business intelligence technologies.

Business analytics is the process of technologies and strategies utilized to continue analyzing and extracting insights and performance from historical business data to drive successful future business planning. There is also a long list of the importance of business analytics.

Common Challenges Faced by Today’s C-Suite

The responsibility of the C-Suite and the CEO, in particular, is to accelerate the growth of a company and work towards achieving industrial excellence. They face immense pressure from various stakeholders who sometimes have theoretical expectations regarding the performance of the company and its results. Let’s check out some of the common challenges faced by the C-Suite.

Expectations for Growth Acceleration

Driving growth and achieving a significant increase in the profit margin annually are among the top challenges faced by today’s C-suite. In the event of continued failure in achieving this goal, CEOs can affect their record.

Business intelligence solutions analyze all the company data and assist the C-suite in making informed decisions. They also help in accelerating the growth of the organization by optimizing internal business processes, enhancing operational efficiency, gaining a competitive edge, and others. By extracting important information from unstructured data and turning it into useful information, BI helps to speed up the process.

Stakeholder’s Demands

Stakeholders can sometimes demand theoretical or special reports and data. Failure to fulfill this demand can upset the stakeholders.

While business intelligence tools may not help you meet the special demands of your stakeholders, but it will certainly help you analyse and explain why a particular target could not be achieved. Moreover, it also keeps track of all the activities, your decisions, and how the company has performed, which will reflect your efforts and incremental progress to the stakeholders.

Budgetary Restrictions

According to Betsy Burton, vice president and distinguished analyst with Gartner, the cost of BI tools is high, which limits their implementation in businesses with limited budget access, such as small to mid-sized companies. Despite the demand and need for business intelligence, often a minimum portion of the operating budget is allocated for the improvement and upgradation of data analytics and the business intelligence systems. As a result, progress is not made, benefits of business intelligence are not reaped, and the cycle of challenges continues in the C-suite.

In this case, businesses can either explore adopting business intelligence tools in phases, or they can opt for self-service BI or embedded BI tools, which are more affordable and can be easily integrated with existing systems.

How Can Business Intelligence Make a Difference?

Not only enterprise companies, but even small, mid-sized, and large businesses can benefit from business intelligence. Adopting business intelligence technologies has numerous benefits. Here are the top seven reasons why having business intelligence (BI) is crucial.

Gain Customer Insights

With the help of business intelligence, businesses can analyze their customers’ buying patterns to obtain customer insights and create user profiles as per their behavior. Customer insights will help businesses create better products and enhance the product experience for their customers.

Improved Efficiency Across the Organization

Having an effective business intelligence system significantly improves the efficiency of the overall business processes and has a positive impact on revenue. In addition, access to meaningful insights reduces the waiting time for reports and increases team productivity.


Gain Sales and Market Intelligence

If you are a sales executive or a marketer, you probably keep track of your customers with the help of a CRM solution. A CRM solution aims to collect all the data and make sense of the data about your customers through charts and tables.


Insights into Consumer Behavior

One of the significant benefits of investing in business intelligence is that it increases the ability to analyze and understand customer behavior. It will highlight a customer’s buying behavior and highlight changes in behavioral patterns.


Improved Business Operations Visibility

Understanding the importance of business intelligence helps control business processes. It helps to assess what is going on in a business carefully. Active vigilance over processes and standard procedures can help to fix errors.


Return on Investment (ROI)

Business intelligence helps a company get a better return on its investment (ROI) by improving strategic awareness, speeding up reporting, cutting operating costs, and getting better quality data.


Gives a Competitive Edge

Apart from all the other benefits of business intelligence, having the potential to handle and analyze enormous amounts of data is in itself a competitive advantage. Furthermore, budgeting, planning, and forecasting are effective ways to keep up with the competition, go well beyond ordinary analysis, and are simple to implement with business intelligence tools.


Final Thoughts

Understanding the importance of business intelligence and having a great business intelligence system has become quite essential for businesses these days. Business intelligence is much more than just graphical representation. It is a set of tools that businesses can use to help their employees succeed. BI can change your business by providing the information required to make fast and informed decisions.


FAQ


Will my business data be secure?

Any IT system must have data security and availability as their top priority. A business intelligence solution should provide the high standards of performance, reliability, and security. To keep the data safe, credible business intelligence solutions make use of existing security infrastructures.


My business has already invested in CRM, Accounting, and Marketing Software. So, why should I also invest in Business Intelligence?

While you may utilize a variety of line-of-business systems to administer your company, BI is about integrating data from numerous sources in an organized way to graphically represent information in a meaningful way. A constructive business intelligence solution should be able to connect to daily business software with ease.


Why Is BI Reporting Better Than Conventional MIS Reports?

Management reporting is only a small part of business intelligence. It gives you real-time, quick, and easy access to actionable business information about customers, goods, finance, and the market.

Spotlight

Soundhound Inc.

SoundHound Inc. is the leading innovator in voice-enabled AI and conversational intelligence technologies.Houndify is the first independent AI platform that enables developers and business owners to deploy it anywhere and retain control of their brand and users, while differentiating and innovating.

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The Importance of Data Governance

Article | July 4, 2023

Data has settled into regular business practices. Executives in every industry are looking for ways to optimize processes through the implementation of data. Doing business without analytics is just shooting yourself in the foot. Yet, global business efforts to embrace data-transformation haven't had resounding success. There are many reasons for the challenging course, however, people and process management has been cited as the common thread. A combination of people touting data as the “new oil” and everyone scrambling to obtain business intelligence has led to information being considered an end in itself. While the idea of becoming a data-driven organization is extremely beneficial, the execution is often lacking. In some areas of business, action over strategy can bring tremendous results. However, in data governance such an approach often results in a hectic period of implementations, new processes, and uncoordinated decision-making. 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While that still rings true in many cases today, overall data load within companies has significantly risen in the past few years. With the proliferation of data-as-a-service companies and overall improvement in information acquisition, medium-size enterprises can now derive beneficial results from implementing data governance if they are within a data-heavy field. However, data governance programs will differ according to several factors. Each of these will influence the complexity of the strategy: Business model - the type of organization, its hierarchy, industry, and daily activities. Content - the volume, type (e.g. internal and external data, general information, documents, etc.) and location of content being governed. Federation - the extent and intensity of governance. Smaller businesses will barely have to think about the business model as they will usually have only one. 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Proper management of data results in easier compliance with laws and regulations, reduced data breach risk, and better decision making due to more streamlined access to information. “Why even bother?” Data governance is difficult, messy, and, sometimes, brutal. After all, most bad data is created out of human behavior, not technical error. That means telling people they’re doing something wrong (through habit or semi-intentional action). Proving someone wrong, at times repeatedly, is bound to ruffle some feathers. Going to a social war for data might seem like overkill. However, proper data governance prevents numerous invisible costs and opens up avenues for growth. Without it, there’s an increased likelihood of: Costs associated with data. Lack of consistent quality control can lead to the derivation of unrealistic conclusions. Noticing these has costs as retracing steps and fixing the root cause takes a considerable amount of time. 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Data Integration Platform: Leveraging the Power of Data

Article | July 10, 2023

Data is not stored in a single database, file system, data lake, or repository. Data generated in a system of record must meet various business requirements, connect with other data sources, and then be utilized for analytics, customer-facing apps, or internal procedures. A well-established data integration solution provides a unified picture of data received from various places and formats. This can also happen when two organizations merge or when internal applications are consolidated. Data integration can also facilitate the development of a more complete data warehouse, resulting in more accurate and effective analysis. Data integration establishes the foundation for effective Business Intelligence (BI) and decision-making. Data Integration as a Tool for Business Strategy The gathering, analysis, and integration of data is essential to the success of businesses. Let’s have a look at the way in which data integration technology enables business strategy. Set Data-Integration Goals These objectives should be part of the larger company objective. A thorough awareness of your consumers, for example, is corporate goal. To do this, your integration strategy should aim to embed customer data into your service, sales, and marketing platforms. Improved Financial Data Management A robust data integration strategy allows you to monitor and manage vital financial and operational data through simple dashboards that combine all business and financial data into a single platform. Any effective financial management system will include basic accounting capabilities that will enable you to track revenue and spending, assets and liabilities, and amortizations in order to provide accurate financial reports. Enhanced Marketing Analytics Data about competitors, industry trends, consumer behavior, and campaign performance should drive your marketing strategy. Update often as fresh data becomes available. By assessing your marketing tools and channels, you can determine the optimum time, place, and technique to advertise your company. Gather data from social media, email marketing tools, CRMs, CMSs, and other platforms for marketing analytics. This also allows you to evaluate where you should spend additional resources to improve the consistency of your marketing effort. Save Time and Resources Business intelligence experts have a huge workload of sifting through business data. Analysts worry less when teams have direct access to essential data. This frees them to concentrate on difficult, valuable data sets. Without data integration platforms, even the simplest business report requires manual processing of all sources, creating code or automatically uploading data to the database, and exhausting systematization. Not to mention the challenge to monitor and correct any human factor errors. This will be completely eliminated by integration automation. How AI Is Enhancing the Data Integration Process? Data Mapping: Businesses can map data faster using AI for insights generation and decision-making. Autonomous learning: An ML-based data integration process enables autonomous learning to discover patterns and trends in the stored data. Big data processing: Machine learning (ML) makes it possible to quickly and accurately transform unstructured and inconsistent data into desirable formats. Closing Lines Regardless of the size of your company or resources, processing and managing data correctly can expand vision of your business and customers. As organizations rely on data analytics and business intelligence, data integration will become more user-friendly in the coming years. Data integration is an unavoidable aspect of every organization's digital transformation path. Implement the most current data integration techniques to stay ahead of your competition.

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Understanding Big Data and Artificial Intelligence

Article | June 18, 2021

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. 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AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics." } }] }

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Spotlight

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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.

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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.

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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.

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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.

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