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5 Predictive Data Analytics Applications

According to Google trends, predictive data analytics has gained a significant amount of popularity over the last few years. Many businesses have implemented predictive analytics applications to increase their business reach, gain new customers, forecast sales, and more.

Predictive Analytics is a type of data analytics technology that makes predictions with the help of data sets, statistical modeling, and machine learning. Predictive analytics uses historical data. This historical data is fed into a mathematical model that recognizes patterns and trends that are then applied to current data to forecast trends, practices, and behaviors from milliseconds to days and even years.

Based on the parameters supplied to them, organizations find patterns within that data to detect risks, opportunities, forecast conditions, and events that would occur at a particular time. At its heart, the use of predictive analytics answers a simple question, “What would happen based on my current data and what can be done to change the outcome.”

In the current times, businesses have multiple products offerings at their disposal to choose from vendors of big data predictive analytics in different industries. They can help these businesses leverage historical data discovering complex data correlation, recognizing patterns, and forecasting.

Organizations are turning to predictive analytics to increase their bottom line and gain advantages against their competition. Some of those reasons are listed below:

• With the growing amount and types of data, there is more interest in utilizing it to produce valuable insights
• Better computers
• An abundance of easy to use software
• Need of competitive differentiation due to tougher
  economic conditions

As more and more easy-to-use software have been introduced, businesses no longer need statisticians and mathematicians for predictive analytics and forecasting.

Benefits of Predictive Analytics

Competitive edge over other businesses

The most common reason why multiple companies picked up predictive analytics was to gain an advantage over their competitors. Customer trends and buying patterns keep changing from time to time. The ones who can identify it first will go ahead in the game. Embracing predictive analytics is how you will stay ahead of your competition. Predictive analytics will aid in qualified lead generation and give you an insight into the present and potential customers.

Business growth

Businesses opt for predictive analytics to predict customer behavior, preferences, and responses. Using this information, they attract their target audience and entice them into becoming loyal customers. Predictive analytics gives valuable information about your customers such as which of them are likely to lapse, how to retain them, whether you should market directly at them, etc. The more you know about them, the stronger your marketing will become. Your business will become the leader in predicting your customer’s exact needs.

Customer satisfaction

Retaining existing customers is almost five times more difficult than acquiring new ones. The most successful company is the one that invests money in retaining those customers as much as acquiring new ones.

Predictive analytics helps in directing marketing strategies towards your existing customers and get them to return frequently. The analytics tool will make sure your marketing strategy caters to the diverse requirements of your customers.

Personalized services

Earlier marketing strategies revolved around the ‘one size fits all’ approach, but gone are those days. If you want to retain and acquire new customers, you have to create personalized marketing campaigns to attract customers.

Predictive analytics and data management help you to get new information about customer expectations, previous purchases, buying behaviors, and patterns. Using this data, you can create these personalized marketing strategies that will help keep up the engagement and acquire new customers.
 

Application of Predictive Analytics

Customer targeting

Customer targeting divides the customer base into different demographic groups according to age, gender, interests, buying, and spending habits. It helps companies to create tailored marketing communications specifically to the customers who are likely to buy their products. Traditional techniques do not even come close to identifying potential customers as well as predictive analytics does.

The major constituents that create these customer groups are:

• Socio-demographic factors: age, gender, education, and marital status
• Engagement factors: recent interaction, frequency, spending habits, etc.
• Past campaign response: contact response, type, day, month, etc.

The customer-specific targeting for the company is highly advantageous. They can:

• Better communicate with the customers
• Save money on marketing
• Increase profits


Customer churn prevention

Customer churn prevention creates major hurdles in a company’s growth. Although it has been proven that retaining customers is cheaper than gaining new ones, it can become a problem. Detecting a client’s dissatisfaction is not an easy task as they can abruptly stop using your services without any warning.
Here, churn prevention comes into the picture. Churn prevention aims to predict who will end their relationship with the company, when, and why. The existing data sets can help develop predictive models so companies can be proactive to prevent the fallout.

Factors that can influence the churn are as follows:

• Customer variables
• Service use
• Engagement
• Technicalities
• Competitor variables

Using these variables, companies can then take necessary steps to avoid the churn by offering customers personalized services or products.

Risk management

Risk assessment and management processes in many companies are antiquated. Even though customer information is abundantly available for evaluation, it is still antiquated.

With advanced analytics, this data can be quickly and accurately analyzed while maintaining customer privacy and boundaries. Risk assessment thus allows companies to analyze problems with any business. Predictive analytics can approximate with certainty which operations are profitable and which are not.

Risk assessment analyzes the following data types:

• Socio-demographic factors
• Product details
• Customer behavior
• Risk metrics


Forecast sales

Evaluating the previous history, seasonality, and market-affecting events make revenue predicting vital for a company’s planning and result in a company’s demand for a product or a service. This can be applied to short-term, medium-term, and long-term forecasting.

Predictive models help in anticipating a customer’s reaction to the factors that affect sales.

Following factors can be used in sales forecasting:

• Calendar data
• Weather data
• Company data
• Social data
• Demand data

Sales forecasting allows revenue prediction and optimal resource allocation.


Healthcare

Healthcare organizations have begun to use predictive analytics as this technology is helping them save money. They are using predictive analytics in several different ways. With the help of this technology, based on past trends they can now allocate facility resources, optimize staff schedules, identify patients at risk, adding intelligence to pharmaceutical and supply acquisition management.

Using predictive analytics in the health domain has also helped in preventing cases and risks of developing health complications like diabetes, asthma, and other life-threatening problems. The application of predictive analytics in health care can lead to making better clinical decisions for patients.

Predictive analytics is being used across different industries and is good way to advance your company’s growth and forecast future events to act accordingly. It has gained support from many different organizations at a global scale and will continue to grow rapidly.


Frequently Asked Questions

What is predictive analytics?

Predictive analytics uses historical data to predict future events. The historical data is used to build mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes.


How to do predictive analytics?

• Define business objectives
• Collect relevant data available from resources
• Improve on collected data by data cleaning methods
• Choose a model or build your own to test data
• Evaluate and validate the predictive model to ensure


How does predictive analytics work for business?

Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations.


What tools are used for predictive analytics?

Some tools used for predictive analytics are:
• SAS Advanced Analytics
• Oracle DataScience
• IBM SPSS Statistics
• SAP Predictive Analytics
• Q Research

Spotlight

Cmbi

CMBI is a BI and data analytics consultancy established in 2010 by Sydney based BI/DW specialist, Colin McGowan LL.B, PGDip Soft Dev, MSc Computing. CMBI designs and develops BI and analytics solutions for clients in Sydney and regional NSW.

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Data visualization refers to a graphical representation of information that displays patterns and trends while allowing viewers to gain rapid insights. Data visualization is critical for businesses to quickly detect trends in data that would otherwise be time-consuming. However, making sense of the quintillion bytes of daily data is difficult without data proliferation, including data visualization. Understanding data is beneficial to every professional business; hence, data visualization is spreading to all fields where data exists. For any company, data is its most valuable asset. One can effectively communicate their points and use that knowledge by using visualization. "Data visualization is a great way to simplify data and show it in a form that is understandable, insightful, and actionable. Data visualization is being increasingly seen as the vital final step of any successful data-driven analytics plan." Caroline Lee, CocoSign Why Does Data Visualization Matter? As we acquire more and more data, data visualization becomes increasingly critical, to the point where we are nearly drowning in information, and it is difficult to distinguish what is significant and what is not—for example, the product development program for a new automobile or plane. Analyzing test data is vital, but a massive amount of information is created with each test drive or flight, making processing at the required speed challenging. Visualization tools help comprehend complex data and detect patterns and anomalies. Visual information accounts for 90% of the information sent to the brain. Data visualizations can shorten business meetings by 24%. Managers who use visual data recovery tools are 28 percent more likely than those who rely on managed reporting and dashboards to find timely information. 48% of these managers can find the data they need without the help of I.T. staff. For every dollar spent on business intelligence with data visualization skills, $13.01 will be returned. Let us look into some of the benefits of data visualization in businesses. The Creditable Impact of Data Visualizations on Business While big data is ruling industries, business intelligence transforms much of this data into actionable data points. As a result, data visualization plays a role in transferring information to the human brain by swiftly presenting data. Visualization has a lot of aesthetic importance in representing and conveying a clear message. Businesses that rely solely on data will eventually go out of business if data visualization is not implemented. Data visualization's competitive benefits can make or kill enterprises. It is critical to know that there are no shortcuts to making faster and better judgments without visualizing the data. Data Visualization Helps You Take Better Decisions Based on Data Unlike meetings that focus on text or numbers, business meetings that address visual data tend to be shorter and easier to reach consensus on. Data visualization speeds up decision-making and allows viewers to better understand patterns and trends. The benefits of data analytics can be accessed across all departments, right from admin to IT, sales to marketing. Even if they are not experts at reading data, your sales team can better understand consumer behavior and impressions if the correct data visualization solutions are in place. With the proper training and tools in place, you can build specialists using data visualization, a combination of technical analytics, and artistic narrative. When visualizations are created to meet your business goals, you will obtain the best results. For example, some data visualizations help in analysis, while others make the data visually appealing. Some are created to demonstrate concepts, processes, or tactics to various audiences. You can create your own based on your specific goals, data types, and stakeholder requirements. Data Visualization Is a Medium to Tell a Data Story to the Viewers Data visualization can also tell a story about data to the audience. The visualization can convey facts in an easy-to-understand format while creating an account and bringing the audience to a predetermined conclusion. This data tale should have a strong start, a basic storyline, and a logical decision like any other story. If a data analyst is tasked with creating a data visualization for company leaders that details the profitability of various items, the data story could begin with the profits and losses of multiple products before moving on to advice on how to address the failures. Create your own depending on your specific goals, data types, and stakeholder requirements. Data Visualization Helps You Gather Data Faster and Saves Time Data visualization is a much faster way to get insights from data than examining a chart. Using data visualization, business meetings can take decisions faster, saving time. When the long meetings are cut short by utilizing data visualization, businesses can dedicate more time to other core business activities. Summing up To develop excellent data visualizations, various tools and methodologies are available. You and your team must comprehend the fundamental principles and choose appropriate tools. Above all, the data must be presented accurately. Layouts, colors, text, and dashboards must be appropriately crafted to build data visualizations that best help your business objectives. Frequently Asked Questions Why is data visualization important? Presenting data in a visual or graphical style is known as data visualization. It allows decision-makers to see analytics in a graphic format, making it easier to grasp complex topics or spot new patterns. What are some of the types of data visualization? There are several types of data visualization. They are line graphs, scatter plots, pie charts, heat maps, area charts, histograms, and choropleths. What are the main goals of data visualization? The main goals of data visualization are to understand your audience, stick to a strict timeline, and deliver a compelling story.

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Business Intelligence, Big Data Management, Big Data

Choose the Right BI Solution: Top Business Intelligence Companies

Article | August 17, 2023

“Data are becoming the new raw material of business.” Craig Mundie, Senior Advisor to CEO, Microsoft Currently, the most valuable asset that a company has is data. By analyzing a large quantity of data and drawing valuable insights, companies can use raw materials (data) to work more effectively. In addition, many big data analytics case studies show that data gives businesses a big advantage over their less tech-savvy competitors. Let’s explore more about big data and analytics in this article. Why Do the C-Level Executives Need Big Data? Every C-level executive is on the lookout for new insights that help them keep their company viable. In recent years, the use of data analytics has become crucial for business leaders to make important decisions. According to McKinsey & Company, companies using big data analytics extensively across all business segments see a 126% profit improvement over companies that don’t. With the use of big data analytics, these companies see 6.5 times more customer retention, 7.4 times more outperformance than competitors, and almost 19 times more profitability. Here are some top reasons why the C-suite needs big data. Take Calculated Actions Harvard Business Review estimated that 70% of companies don’t feel that they understand the needs of their customers well enough to recognize what initiatives will drive growth. In such cases, you already know what you need to do, i.e., leverage big data and analytics. Big data analytics for businesses can help in recognizing customer preferences and customer segments on the basis of those preferences. C-suites in any industry can align their structure and product offerings to create value and take calculated actions. Recognize the Data According to Statista, data creation will increase to more than 180 zettabytes by 2025, which is a huge number. So, you can’t keep an approach of ‘gather now and sort it out later.’ With this approach to big data, you will be buried under tons of non-structured data. Start tracking the data early and capture the ones that are customer-generated and provide value to your company. Segment Your Customer’s Experience Analyze your present data and utilize your analytics to evaluate which characteristics a group of customers have in common and which aspects they don’t share. Segment and organize customers according to their preferences to build a clear lifecycle structure for every segment. Biggest Concerns About Big Data Analytics According to Concepta, 80% of C-suites think that data analytics will be a transformative force for businesses, but only 1 in 10 deliberately use it. 48% describe analytics as critical to decision-making, but only 7.4% say they use analytics to guide corporate strategy. So, what are the issues or concerns that tech-savvy C-level executives face when it comes to big data and analytics? Integrating Data with Current Technology "Tech inertia" usually disrupts certain businesses from evolving. Sometimes, the analytics framework businesses have in place is outdated to accommodate new techniques. According to Concepta, more than half the C-suite feel their analytics infrastructure is too rigid, and 75% say that due to inflexibility, they could not fulfill their business needs. Changing or upgrading the current technology would result in a loss of productivity. Companies must get the appropriate tools like Oracle Data Integrator 12c, SAP Data Services, MuleSoft, etc. to handle their data integration challenges. Another option is to seek professional assistance. You may either engage seasoned specialists who are far more knowledgeable about these instruments. Another option is to hire big data consultants. Big Data Silos There is a lot of unstructured data that is collected by different departments within a company, which leads to big data silos. The C-suite plays a critical role in developing a strategy, ensuring all departments communicate and integrate data from various sources to get a holistic picture of their business operations. Integrating your software that collects and stores data correctly is one of the most effective ways to avoid data silos Make a decision to use an all-in-one tool to unify and speed up your data management Spare some time to filter your outdated data Big Data Security Big data security is one of the most difficult tasks. Businesses are often so preoccupied with understanding, storing, and analyzing data that they overlook data security. Unsecured data repositories may become fertile grounds for malicious hackers. A data breach may cost a company up to $3.7 million. Businesses are hiring more cybersecurity experts to protect their data. Other measures taken to secure big data include: encryption of data, data segregation, identity and access management endpoint security implementation, real-time security monitoring, and use of big data security technologies such as the IBM Guardian. Key to Big Success from Big Data To get the most out of your big data and overcome the associated challenges, we have listed some key pointers that make a business successful and show how companies using big data are standing out. Have a Calculated Approach While laying the foundation of big data and business analytics, it is important to have a calculated approach as it reduces the risk in the early stages of setting up big data analytics. So, rather than attempting to implement it all at once, businesses should focus on resources that drive value from big data. Programmatic Integration In an action-driven system, success demands synchronizing big data, relevant analytics, and decision-making platforms at the appropriate time. The most successful companies using big data get insights directly from the data analytics tools used by executives who can act immediately according to the insights from the data. Focus on Building Skills Businesses must expand the big data capabilities of current workers through training and development since data analytics talent still remains one of the major challenges. 54% of the CEOs say that their companies have already set up in-house technical training programs for their employees. State-of-the-Art Technology To create strong big data and analytics capabilities, you need the right tools and technologies. Unfortunately, those who don’t have access to efficient big data analytics tools like Hadoop find themselves falling behind. Conclusion There's no going back when it comes to technology. Business decisions and activities are now made based on the use of data, so businesses that don't learn how to use their data will soon be out of date because data is now at the heart of everything. Businesses can align their data structures according to the requirements of their product offerings to generate value by utilizing big data and analytics. It helps to determine consumer preferences and segment consumers based on insights. FAQ How much data does it take to be called “Big Data”? There is no definitive answer to this question. Based on the current market infrastructure, the minimum threshold is somewhere around 1 to 3 terabytes (TB). However, big data technologies are also suitable for smaller databases. Do I Need to Hire a Data Scientist? The decision to hire a data scientist for your company is often a difficult one, and it depends entirely on your business's position. While there has been a huge demand for data scientists over the last few years, they are not easily available. Many businesses just use the support of a data architect or analyst. How are big data and Hadoop related to each other? Hadoop and big data are almost synonymous. Hadoop is a framework that specializes in big data processing that has grown in popularity with the advent of big data. Professionals may use the framework to analyze large amounts of data and assist companies with better decision-making.

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Big Data Management

Why Increasing Data Maturity Help Businesses Unlock Digital Potential?

Article | July 5, 2022

There is no dispute that brands that harness and invest in data capabilities will be the ones to realize their maximum revenue potential. However, while today's marketers have access to a multitude of data sources, understanding what data to use and how to utilize it are two of the biggest challenges for all. Data utilization in companies is an inconsistent experience. Some businesses have sensibly invested in improving their data maturity. They can pivot quickly to maximize income potential in an unstable economic environment. Others face a cycle of declining returns as they try to reproduce previous achievements with variable outcomes. Importance of Data Maturity for Businesses Understanding your organization's data maturity is critical for five reasons. An understanding of data maturity may assist marketers in: Align Recognize which problems and challenges the wider organization is attempting to solve and modify techniques to support those goals. Appreciate Analyze honestly what the company is good at doing presently and where adjustments are needed to create better data decision-making. Evaluate Measure data literacy levels while implementing training and upskilling resources to facilitate the implementation of an open learning environment to encourage innovative thinking. Anticipate As the company's data capabilities develop, look forward to significantly advanced analytics possibilities. Calibrate Optimize technology and infrastructure to extract maximum value now while also appropriately planning for future resources. Future-Proof Your Business with Data Maturity Data maturity applies to the whole organization. It is a company-wide effort that extends beyond the goals of a single sales or marketing team. As a result, it's critical to bring together diverse internal influencers to determine how improvements to your data strategy can assist everyone in achieving the same objectives. The mix of stakeholders is unique to each organization, so it will be determined by your company's priorities.

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Cmbi

CMBI is a BI and data analytics consultancy established in 2010 by Sydney based BI/DW specialist, Colin McGowan LL.B, PGDip Soft Dev, MSc Computing. CMBI designs and develops BI and analytics solutions for clients in Sydney and regional NSW.

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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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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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