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Predictive Analytics: Implementation in Business Processes

Predictive Analytics: Implementation in Business Processes
Knowledge is power in business, and knowing what will happen in the future is a superpower. When data analytics, statistical algorithms, AI, and machine learning are combined, this superpower, also known as predictive analytics, becomes a skill that can significantly influence on a company's choices and outcomes.

Predictive analytics is the use of modern analytical tools. For example, machine learning concludes about the future based on historical data. Businesses can consider application of predictive analytics tools and models to forecast trends and generate accurate future predictions by leveraging historical and current data. Let’s look at the top three reasons why predictive analytics is important for your business.

Why is Predictive Analytics Important for Businesses?

Businesses are looking at predictive analytics to help them solve challenges and discover new opportunities. Here are some of the most common benefits of predictive business analytics and an understanding of how is predictive analytics used in business.

Fraud Detection

In general, various analyzing techniques are merged to analyze data to enhance the accuracy of pattern recognition and discover criminal behavior, thereby reducing the incidence of frequent fraud. With behavioral analytics, you can look at any suspicious behavior and activities that happen on a network in real-time to look for fraud, zero-day breaches, and underlying threats.

Enhancing Business Campaigns

The predictive analytics process can help you optimize marketing campaigns and promotional events. Predictive designs helps businesses attract, retain, and increase valuable customers by determining their purchase responses and promoting cross-sell opportunities.

Minimizing Potential Risk

The predictive analytics process helps businesses decide on appropriate steps to avoid or reduce losses. Predictive analytics is revolutionizing risk management by alerting businesses about future developments. For example, credit scores, which financial institutions use to predict defaulters depending on a user's purchasing behavior.

How Does Predictive Analytics Help the C-Suite?

The C-suite is the final decision maker, so they are the ones who must use predictive analytics the most for insightful decision-making. Let’s look at ways in which predictive analytics can help C-level executives.


Predict Customer Behavior

Predictive analytics utilizes data to forecast future customer behavior. Customer intent becomes the primary aspect rather than historical transactional data, allowing for hyper-personalized marketing and communications. For example, researchers at China's Renmin University used predictive analytics and machine learning to figure out that data on consumer interests and jobs can predict customer preferences and purchase intent for cars.

Predicting customer requirements accurately is a huge opportunity for businesses. Companies can use AI and predictive analytics models to figure out what customers will do based on data instead of guesswork.


Pricing Optimization

Predictive business analytics can help companies improve pricing optimization quickly and affordably. A business can use predictive analytics to figure out how to make a product more affordable in the future by looking at past data, industry trends, competitive prices, and other data sources.

Each customer provides a unique value to the products. To add to the complexity, a consumer's value of a product may vary depending on the purchase circumstances and environment. Simplicity in pricing misses opportunities and can result in a significant drop in revenue.

Product information, consumer segmentation, and purchase circumstances are all enhanced by predictive analytics. Businesses can use this data to uncover trends and patterns to help them price more profitably.


Predicting Growth and Market Trends

Businesses can use predictive market analysis to decipher existing and future market trends. With this data, businesses can develop a plan to maximize opportunities, expand market share, and sustain disruption and new competition. Companies can use it to detect unmet customer demand and fill any gaps. Consumption sentiment is revealed through social media data. A product that does not match customer demand creates a market opportunity for a new product or service.

Predictive market analysis can uncover customer perceptions of a product or service and unmet consumer demands. Predictive business analytics helps businesses better understand their customers, meet their needs, and find new ways to earn revenue and grow.


Example: Reu La La Uses Predictive Analytics to Increase its Revenue by 10%

You often hear about giant enterprises like Amazon, Airbnb, Microsoft, Google, and others utilizing predictive analytics to extend their reach, boost sales, and more. Today let’s look at Reu La La and how they used predictive analytics to enhance their revenue.

Rue La La, a boutique retailer, often needs to predict sales and fix pricing for products being sold for the first time in its online store with no existing sales data. They observed that many products were either sold out within the first few hours of release or did not sell, which lead to revenue loss.

Rue La La took action by creating a set of quantitative qualities for its items and predicting future demand by utilizing historical sales data. They used statistical and computing technologies, such as regression analysis and machine learning, to create a demand forecast and pricing optimization model. In partnership with the Massachusetts Institute of Technology, they created an automated price decision assistance tool. Revenue increased from 10% to 13% across all departments because they used the pricing tool's proposed optimal rates.


Conclusion

“As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein.”

Eric Siegel

You can consider the predictions that predictive analytics makes as gold, but, using predictive analytics is like a crystal ball that shows the future. You can look into the future, prevent issues in your company from escalating, and recognize profitable possibilities.

If you haven't started leveraging predictive analytics, start by experimenting with it on a modest scale and gradually build up as you acquire expertise and observe positive outcomes.


FAQ


How can Predictive Analytics Improve Performance Measurement?

Predictive analytics improves performance measurements by expanding an organization's understanding of the important performance drivers. It also helps with the weighting of different performance metrics based on how important they are.
 

What Are the Four Steps in Predictive Analytics?

In simple terms, predictive analytics involves four steps: creating a baseline prediction, assessing it, adding assumptions, and building a consensus demand plan. To do so, we must first choose a modeling technique, create a test design, then construct the model, evaluate the mode, and achieve alignment.
 

What Are the Three Different Types of Predictive Analytics?

Businesses utilize three forms of analytics to drive their decision-making:

Descriptive analytics — tells something that has already happened;
Predictive analytics — shows what can happen;
Prescriptive analytics — tells what should happen in the future

 

Spotlight

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Business Intelligence, Big Data Management, Big Data

Predictive Analytics in Finance: Understanding What 2022 Holds

Article | July 4, 2023

The financial industry has been going through digital transformation for years. Digital technologies have helped to automate manual and tedious tasks like processing and reporting of historical data to forecasting and financial predictive analytics. The financial services industry owes its success to data. Data is constantly evolving in the form of market trends, client investment, customer service, campaigns. Data gives a boost to banking strategies. As reported by Accenture in a recent survey, 78 percent of banks have made the shift to using data for operations; however, only seven percent of them have extended to using predictive analytics in finance. Predictive analytics in finance has had a slow but steady start. It is an area of growing interest for banks and other institutions as new newer technologies launch in the market. To complete your company’s digital transformation, data analytics in finance will make a difference in that process. To be successful, organizations must have the ability to adapt to changes. Having predictive analytics on your side, your organization can deal with ever-changing circumstances with less to no difficulty. Understanding Predictive Analytics: What is it? Predictive analytics is a process of interpreting data to measure any possible future outcomes. It is carried out with the help of statistical modeling, historical data sets, and machine learning. The collected historical data is fed into an algorithm that recognizes patterns and forecast trends and possible future behavior from days to years in advance. Analyzing historical data and predicting the future has been an old practice in the finance sector. Banks and financial institutions have been evaluating past events or historical data for a long time now. Making precise forecasts in trends and analyzing data becomes easier due to predictive analytics. There is a wider scope to predictive efforts with more speed and accuracy and apply them throughout strategic and tactical business practice areas. Predictive Analytics in the Financial Sector: What are the Benefits? Many organizations are ready to accept the positive applications of predictive analytics but remain skeptical about the return on investment. It is worth understanding the potential of predictive analytics to any business big or small. It doesn’t matter if you are not in the banking sector to benefit from taking a peek into the future of financial performance. Any finance and accounting department can take advantage of advanced predictive analytics for the following reasons: Precise Monitoring The technology keeps a regular track of the consistency between expectations and reality to warn you about possible gaps. Risk Alleviating Analytics accurately helps you identify any possible threats to your business and warns you. Enhanced User Experience Predictive analytics guides you to recognize the strengths of your business and lets you know how to maximize customer satisfaction. Analyzed Decision Making You can understand your customers better with predictive analytics. With this information, you can correctly match your customers with the product in a better way. Importance of Predictive Analytics Most successful banking and financial institutions depend on predictive analytics because it simplifies and integrates data to increase profits for companies. Predictive analytics can improve different finance processes. But the importance of analytics goes beyond just banking services and actually goes into a better quality of customer service. Better customer service is only possible because of the advanced technology that shares customer feedback and preferences throughout the organization, in turn giving relevant information to every employee to make necessary product enhancements. To understand the importance of predictive analytics, below are some of its use cases: Customer first Predictive analytics in financial institutions and banking give you a complete profile of your customer base. It is impossible to contact every customer and interview them about their likes, needs and wants. This is where big data analytics in finance comes into play. It gives you the whole information about your customers regardless of the services they subscribe. Customers usually don’t have the same needs throughout their lives. As they grow older and have families, their financial needs change accordingly. For instance, a young person considering getting married will always try and save monetarily to buy a house, life insurance, college funds, whereas an older couple will save that money for their retirement. Apart from enabling different financial services, predictive analytics empowers you to serve individual customers with ease. Let’s take an example. When a customer applies for a loan, predictive financial services can help you analyze if the customer can repay the loan. Predictive analytics also helps offer alternative services like secured loans to customers who may not qualify for the originally applied services. Online Banking Made Better Consumer interest fluctuates in spikes. Predictive analytics informs managers enough in advance so they can set up online infrastructures in those areas. Predictive analytics has made it easier to identify a possible customer base. For example, it can provide metrics to the marketing teams. In turn, the marketing teams can target the customers with ads for probable mortgage loans or business loans in hopes of converting them into their customers. Data analytics in finance also helps in preventing and detecting fraud and abuse. Although detecting fraud doesn’t necessarily fall under predictive analytics, it can inform the IT department about potential scammers and which online services must be protected. Foreseeing Market Variations Predictive analytics can predict market variations and changes. By combining internal and external data, your organization can predict revenue growth in particular market sectors. For nascent or growing companies, predicting market changes is an important ability. Profitable companies should also be reviewed through predictive analytics to generate demand projections owing to the uncertainties caused by the Covid-19 pandemic. Your return on investment can grow or reduce even with the minutest changes to the growth plans that would seriously impact investor confidence in the future. Predictive analytics also help to establish which marketing campaigns are working and which strategies need to change. Predictive Analytics and the Future: What Next? Technological improvements have allowed predictive analytics in finance to improve and change constantly. Any organization can use customized data solutions to meet your customers’ needs and reach new ones efficiently. Your organization can use predictive analytics to move your business and products ahead and understand how the market will thrive, giving you the much needed heads up you would need to change your strategies and tactics. Frequently Asked Questions Is predictive analytics is the future of finance? Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness. How can analytics be used in finance? Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection. How do predictive analytics benefit financial institutions? Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Is predictive analytics is the future of finance?", "acceptedAnswer": { "@type": "Answer", "text": "Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness." } },{ "@type": "Question", "name": "How can analytics be used in finance?", "acceptedAnswer": { "@type": "Answer", "text": "Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection." } },{ "@type": "Question", "name": "How do predictive analytics benefit financial institutions?", "acceptedAnswer": { "@type": "Answer", "text": "Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc." } }] }

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Predictive Analytics: Enabling Businesses Achieve Accurate Data Prediction using AI

Article | August 17, 2023

We are living in the age of Big Data, and data has become the heart and the most valuable asset for businesses across industry verticals. In the hyper-competitive market that exists today, data acts as a major contributor to achieving business intelligence and brand equity. Thus, effective data management is the key to accelerating the success of businesses. For effective data management to take place, organizations must ensure that the data that is used is accurate and reliable. With the advent of AI, businesses can now leverage machine learning to predict outcomes using historical data. This is called predictive analytics. With predictive analytics, organizations can predict anything from customer turnover to forecasting equipment maintenance. Moreover, the data that is acquired through predictive analytics is of high quality and very accurate. Let us take a look at how AI enables accurate data prediction and helps businesses to equip themselves for the digital future.

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Data Mining Techniques and How Businesses Implement Them

Article | May 2, 2023

Businesses have more data than ever in contemporary times because of rapidly evolving technology. Data is one of the most valuable resources available to any business or entrepreneur. Businesses today are overflowing with data from a wide range of sources, including websites, mobile devices, social media, and other digital channels, but they don’t know where to start. It doesn't matter if you have massive data; what matters is what you do with it. This is where data mining comes into play. Turning unstructured data into insights helps companies accomplish their goals and improvement strategy for the future. These days, data mining techniques are widely used by organizations from different industries. “With data collection, ‘the sooner the better’ is always the best answer.” - Marissa Mayer, Ex-CEO of Yahoo! Let’s explore some of the top data mining techniques used by businesses. What are the Must-Have Data Mining Techniques? Data mining is a successful process that uses a variety of data mining approaches. The problem is determining which data mining techniques are most appropriate for your situation and business. Even though many data mining techniques are often required to uncover insights hidden inside massive datasets, it's wise to use more than one. Data Cleaning When it comes to data mining, data cleaning is a must-have approach. For raw data to be used for various sorts of analytical techniques, it must first be cleaned, formatted, and analyzed. This data mining technique is used in data modeling, transformation, aggregation, and migration processes. Clustering Analysis Clustering analysis is the recognition and clustering of related data from an big data collection. Clustering analysis can assist an organization in evaluating the differences and similarities in data from the perspective of a company. This can help them develop customer personas, similar segment leads, and others. Association Rule Discovery This is a technique for discovering interesting connections and interdependencies among variables in big datasets. This data mining technique can help decipher hidden data patterns that would otherwise go unnoticed. Association rule discovery a term that's frequently used in machine learning. Classification Analysis The technique of extracting information about the data is called classification analysis. It is the most complicated data mining technique. Data classification involves splitting data into categories that have similarities in their context. As a result, classification analysis is useful in combination with clustering analysis. The structure or recognition of the data is known as classification. Data Visualization Data visualization uses real-time graphs and charts to provide users with extra insights into their data and help them better grasp performance targets. Data visualization is a popular data mining technique because it can get data from any source, such as file uploads, database queries, and application connections. Top Industries Using Data Mining Data mining in business can help you manage risk by detecting fraud, errors, and discrepancies that can result in revenue loss and reputation damage. Data mining is used in various industries to gain a deeper understanding of their customers and businesses. Many brands in various industries perfectly portray how data mining is used in business. Finance and Banking Using data mining approaches, financial organizations gather information about loans and credit reports. Financial institutions can evaluate if a lender has a good or bad credit score by using an analysis model based on historical financial data. Banks can also keep a watch on suspicious or fraudulent transactions with the help of data mining techniques. E-commerce E-commerce platforms are among the most well-known examples of data mining and business analytics. Many e-commerce websites utilize data mining and business intelligence to provide cross-sells and up-sells. Amazon is, of course, one of the most popular users of data mining and business intelligence. Retail Customers are segmented into 'recency, frequency, and monetary' (RFM) categories by retailers, focusing on marketing to those segments. A consumer who spends little but frequently and recently will be treated differently from one who spends a lot just once, which was some time ago. Loyalty, up-sell, and cross-sell offers may be made to the frequent buyer, while the big spender may provide a win-back deal. Top 3 Companies Leveraging Data Mining Techniques Businesses use data mining to boost revenue, save expenses, locate consumers, improve customer experience, listen to what others have to say, and conduct competitive intelligence. These are just a few ways of using data mining techniques. Here are the top three examples of data mining in business. Amazon Amazon is gathering competitive intelligence and pricing data from its competitors. Consumers who use the Amazon Price Check Mobile App to scan items in-store, capture an image of the product, or do a text search to find the best price will get a $5 discount. The application also encourages customers to report the in-store price. Netflix House of Cards – the American thriller series was an ideal entertainment experience for creating data models and discovering what makes a show or movie successful among viewers based on the insights data gathered. They went all out for the license, winning a bidding battle with rival businesses and instantly scheduling two seasons before presenting a single episode. It was a super hit, and the best thing is that they had a pretty good idea of what it would be. Walmart The current search engine of Walmart contains semantic data. Polaris, an in-house platform, uses text analysis, machine learning, and even synonym mining to provide relevant search results. Walmart says that integrating semantic search has led to a 10% to 15% increase in the number of people who buy things online. Conclusion Data mining's ultimate significance for data-driven growth and progress cannot be overstated. Using the right data mining technique will give you unparalleled insight into your massive data. Data mining will only get better as technology improves, which will allow for more in-depth analysis. FAQ What are the 3 types of data mining? Pictorial data mining, text mining, social media mining, online mining, and audio and video mining are only a few examples of data mining. What are the 6 processes of data mining? Data mining is both an analytical process and a collection of algorithms and models. The CRISP-DM process model has been broken down, just like the CIA Intelligence Process. These parts are business understanding, data understanding, data preparation, modeling, evaluation, and deployment. What are the most commonly used data mining processes? Clustering, data cleansing, association, data warehousing, machine learning, data visualization, classification, neural networks, and prediction are just a few of the important data mining techniques to consider when starting out in the industry.

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Can Blockchain Change The Game Of Data Analytics And Data Science?

Article | June 1, 2022

Blockchain has been causing ripples across major industries and verticals in the recent couple of years. We are seeing the future potential of blockchain technology that is scaling beyond just cryptocurrencies and trading. It is only natural that Blockchain is going to have a huge impact on Data Analytics, another field that has been booming and seems to continue in the same trajectory for the foreseeable future. However, very little research has been done on the implications of blockchain on Data Science or the potential of Data Science in Blockchain. While Blockchain is about validating data and data science is about predictions and patterns, they are linked together by the fact that they both use algorithms to control interactions between various data points. Blockchain in Big Data Analytics Big Data has traditionally been a very centralized method where we had to collate data from various sources and bring it together in one place. Blockchain, considering its decentralized nature can potentially allow analysis of data to happen at the origin nodes of individual sources. Also, considering that all data parsed through blockchain is validated across networks in a fool proof manner, the data integrity is ensured. This can be a game changer for analytics. With the digital age creating so many new data points and making data more accessible than ever, the need for diving into depth with advanced analytics has been realized by businesses around the world. However, the data is still not organized and it takes a very long time to bring them together to make sense of it. The other key challenge in Big Data remains data security. Centralized systems historically have been known for their vulnerability for leaks and hacks. A decentralized infrastructure can address both of the above challenges enabling data scientists to build a robust infrastructure to build a predictive data model and also giving rise to new possibilities for more real time analysis. Can Blockchain Enhance Data Science? Blockchain can address some of the key aspects of Data Science and Analytics. Data Security & Encoding: The smart contracts ensure that no transaction can be reversed or hidden. The complex mathematical algorithms that form the base of Blockchain are built to encrypt every single transaction on the ledger. Origin Tracing & Integrity: Blockchain technology is known for enabling P2P relationships. With blockchain technology, the ledgers can be transparent channels where the data flowing through it is validated and every stakeholder involved in the process is made accountable and accessible. This also enables the data to be of higher quality than what was possible with traditional methods. Summing Up Data science itself is fairly new and advancing in recent years. Blockchain Technology, as advanced as it seems, is still at what is believed to be a very nascent stage. We have been seeing an increasing interest in data being moved to the cloud and it is only a matter of time when businesses will want it to be moved to decentralized networks. On the other hand, blockchain’s network and server requirements are still not addressed and data analytics can be very heavy on the network, considering the volume of data collected for analysis. With very small volumes of data stored in blocks, we need viable solutions to make sure data analysis in blockchain is possible at scale. At Pyramidion, we have been working with clients globally on some exciting blockchain projects. These projects are being led by visionaries, who are looking to change how the world functions, for good. Being at the forefront of innovation, where we see the best minds working on new technologies, ICOs and protocols, we strongly believe it is only a matter of time before the challenges are addressed and Blockchain starts being a great asset to another rapidly growing field like Data Science and Data Analytics.

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Business Intelligence for Actionable Insights: Top BI books to Explore

Article | July 4, 2023

The financial industry has been going through digital transformation for years. Digital technologies have helped to automate manual and tedious tasks like processing and reporting of historical data to forecasting and financial predictive analytics. The financial services industry owes its success to data. Data is constantly evolving in the form of market trends, client investment, customer service, campaigns. Data gives a boost to banking strategies. As reported by Accenture in a recent survey, 78 percent of banks have made the shift to using data for operations; however, only seven percent of them have extended to using predictive analytics in finance. Predictive analytics in finance has had a slow but steady start. It is an area of growing interest for banks and other institutions as new newer technologies launch in the market. To complete your company’s digital transformation, data analytics in finance will make a difference in that process. To be successful, organizations must have the ability to adapt to changes. Having predictive analytics on your side, your organization can deal with ever-changing circumstances with less to no difficulty. Understanding Predictive Analytics: What is it? Predictive analytics is a process of interpreting data to measure any possible future outcomes. It is carried out with the help of statistical modeling, historical data sets, and machine learning. The collected historical data is fed into an algorithm that recognizes patterns and forecast trends and possible future behavior from days to years in advance. Analyzing historical data and predicting the future has been an old practice in the finance sector. Banks and financial institutions have been evaluating past events or historical data for a long time now. Making precise forecasts in trends and analyzing data becomes easier due to predictive analytics. There is a wider scope to predictive efforts with more speed and accuracy and apply them throughout strategic and tactical business practice areas. Predictive Analytics in the Financial Sector: What are the Benefits? Many organizations are ready to accept the positive applications of predictive analytics but remain skeptical about the return on investment. It is worth understanding the potential of predictive analytics to any business big or small. It doesn’t matter if you are not in the banking sector to benefit from taking a peek into the future of financial performance. Any finance and accounting department can take advantage of advanced predictive analytics for the following reasons: Precise Monitoring The technology keeps a regular track of the consistency between expectations and reality to warn you about possible gaps. Risk Alleviating Analytics accurately helps you identify any possible threats to your business and warns you. Enhanced User Experience Predictive analytics guides you to recognize the strengths of your business and lets you know how to maximize customer satisfaction. Analyzed Decision Making You can understand your customers better with predictive analytics. With this information, you can correctly match your customers with the product in a better way. Importance of Predictive Analytics Most successful banking and financial institutions depend on predictive analytics because it simplifies and integrates data to increase profits for companies. Predictive analytics can improve different finance processes. But the importance of analytics goes beyond just banking services and actually goes into a better quality of customer service. Better customer service is only possible because of the advanced technology that shares customer feedback and preferences throughout the organization, in turn giving relevant information to every employee to make necessary product enhancements. To understand the importance of predictive analytics, below are some of its use cases: Customer first Predictive analytics in financial institutions and banking give you a complete profile of your customer base. It is impossible to contact every customer and interview them about their likes, needs and wants. This is where big data analytics in finance comes into play. It gives you the whole information about your customers regardless of the services they subscribe. Customers usually don’t have the same needs throughout their lives. As they grow older and have families, their financial needs change accordingly. For instance, a young person considering getting married will always try and save monetarily to buy a house, life insurance, college funds, whereas an older couple will save that money for their retirement. Apart from enabling different financial services, predictive analytics empowers you to serve individual customers with ease. Let’s take an example. When a customer applies for a loan, predictive financial services can help you analyze if the customer can repay the loan. Predictive analytics also helps offer alternative services like secured loans to customers who may not qualify for the originally applied services. Online Banking Made Better Consumer interest fluctuates in spikes. Predictive analytics informs managers enough in advance so they can set up online infrastructures in those areas. Predictive analytics has made it easier to identify a possible customer base. For example, it can provide metrics to the marketing teams. In turn, the marketing teams can target the customers with ads for probable mortgage loans or business loans in hopes of converting them into their customers. Data analytics in finance also helps in preventing and detecting fraud and abuse. Although detecting fraud doesn’t necessarily fall under predictive analytics, it can inform the IT department about potential scammers and which online services must be protected. Foreseeing Market Variations Predictive analytics can predict market variations and changes. By combining internal and external data, your organization can predict revenue growth in particular market sectors. For nascent or growing companies, predicting market changes is an important ability. Profitable companies should also be reviewed through predictive analytics to generate demand projections owing to the uncertainties caused by the Covid-19 pandemic. Your return on investment can grow or reduce even with the minutest changes to the growth plans that would seriously impact investor confidence in the future. Predictive analytics also help to establish which marketing campaigns are working and which strategies need to change. Predictive Analytics and the Future: What Next? Technological improvements have allowed predictive analytics in finance to improve and change constantly. Any organization can use customized data solutions to meet your customers’ needs and reach new ones efficiently. Your organization can use predictive analytics to move your business and products ahead and understand how the market will thrive, giving you the much needed heads up you would need to change your strategies and tactics. Frequently Asked Questions Is predictive analytics is the future of finance? Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness. How can analytics be used in finance? Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection. How do predictive analytics benefit financial institutions? Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Is predictive analytics is the future of finance?", "acceptedAnswer": { "@type": "Answer", "text": "Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness." } },{ "@type": "Question", "name": "How can analytics be used in finance?", "acceptedAnswer": { "@type": "Answer", "text": "Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection." } },{ "@type": "Question", "name": "How do predictive analytics benefit financial institutions?", "acceptedAnswer": { "@type": "Answer", "text": "Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc." } }] }

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Choose the Right BI Solution: Top Business Intelligence Companies

Article | August 17, 2023

We are living in the age of Big Data, and data has become the heart and the most valuable asset for businesses across industry verticals. In the hyper-competitive market that exists today, data acts as a major contributor to achieving business intelligence and brand equity. Thus, effective data management is the key to accelerating the success of businesses. For effective data management to take place, organizations must ensure that the data that is used is accurate and reliable. With the advent of AI, businesses can now leverage machine learning to predict outcomes using historical data. This is called predictive analytics. With predictive analytics, organizations can predict anything from customer turnover to forecasting equipment maintenance. Moreover, the data that is acquired through predictive analytics is of high quality and very accurate. Let us take a look at how AI enables accurate data prediction and helps businesses to equip themselves for the digital future.

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Business Intelligence, Big Data Management, Data Science

Predictive Maintenance with Industrial Big Data: Reactive to Proactive Strategies

Article | May 2, 2023

Businesses have more data than ever in contemporary times because of rapidly evolving technology. Data is one of the most valuable resources available to any business or entrepreneur. Businesses today are overflowing with data from a wide range of sources, including websites, mobile devices, social media, and other digital channels, but they don’t know where to start. It doesn't matter if you have massive data; what matters is what you do with it. This is where data mining comes into play. Turning unstructured data into insights helps companies accomplish their goals and improvement strategy for the future. These days, data mining techniques are widely used by organizations from different industries. “With data collection, ‘the sooner the better’ is always the best answer.” - Marissa Mayer, Ex-CEO of Yahoo! Let’s explore some of the top data mining techniques used by businesses. What are the Must-Have Data Mining Techniques? Data mining is a successful process that uses a variety of data mining approaches. The problem is determining which data mining techniques are most appropriate for your situation and business. Even though many data mining techniques are often required to uncover insights hidden inside massive datasets, it's wise to use more than one. Data Cleaning When it comes to data mining, data cleaning is a must-have approach. For raw data to be used for various sorts of analytical techniques, it must first be cleaned, formatted, and analyzed. This data mining technique is used in data modeling, transformation, aggregation, and migration processes. Clustering Analysis Clustering analysis is the recognition and clustering of related data from an big data collection. Clustering analysis can assist an organization in evaluating the differences and similarities in data from the perspective of a company. This can help them develop customer personas, similar segment leads, and others. Association Rule Discovery This is a technique for discovering interesting connections and interdependencies among variables in big datasets. This data mining technique can help decipher hidden data patterns that would otherwise go unnoticed. Association rule discovery a term that's frequently used in machine learning. Classification Analysis The technique of extracting information about the data is called classification analysis. It is the most complicated data mining technique. Data classification involves splitting data into categories that have similarities in their context. As a result, classification analysis is useful in combination with clustering analysis. The structure or recognition of the data is known as classification. Data Visualization Data visualization uses real-time graphs and charts to provide users with extra insights into their data and help them better grasp performance targets. Data visualization is a popular data mining technique because it can get data from any source, such as file uploads, database queries, and application connections. Top Industries Using Data Mining Data mining in business can help you manage risk by detecting fraud, errors, and discrepancies that can result in revenue loss and reputation damage. Data mining is used in various industries to gain a deeper understanding of their customers and businesses. Many brands in various industries perfectly portray how data mining is used in business. Finance and Banking Using data mining approaches, financial organizations gather information about loans and credit reports. Financial institutions can evaluate if a lender has a good or bad credit score by using an analysis model based on historical financial data. Banks can also keep a watch on suspicious or fraudulent transactions with the help of data mining techniques. E-commerce E-commerce platforms are among the most well-known examples of data mining and business analytics. Many e-commerce websites utilize data mining and business intelligence to provide cross-sells and up-sells. Amazon is, of course, one of the most popular users of data mining and business intelligence. Retail Customers are segmented into 'recency, frequency, and monetary' (RFM) categories by retailers, focusing on marketing to those segments. A consumer who spends little but frequently and recently will be treated differently from one who spends a lot just once, which was some time ago. Loyalty, up-sell, and cross-sell offers may be made to the frequent buyer, while the big spender may provide a win-back deal. Top 3 Companies Leveraging Data Mining Techniques Businesses use data mining to boost revenue, save expenses, locate consumers, improve customer experience, listen to what others have to say, and conduct competitive intelligence. These are just a few ways of using data mining techniques. Here are the top three examples of data mining in business. Amazon Amazon is gathering competitive intelligence and pricing data from its competitors. Consumers who use the Amazon Price Check Mobile App to scan items in-store, capture an image of the product, or do a text search to find the best price will get a $5 discount. The application also encourages customers to report the in-store price. Netflix House of Cards – the American thriller series was an ideal entertainment experience for creating data models and discovering what makes a show or movie successful among viewers based on the insights data gathered. They went all out for the license, winning a bidding battle with rival businesses and instantly scheduling two seasons before presenting a single episode. It was a super hit, and the best thing is that they had a pretty good idea of what it would be. Walmart The current search engine of Walmart contains semantic data. Polaris, an in-house platform, uses text analysis, machine learning, and even synonym mining to provide relevant search results. Walmart says that integrating semantic search has led to a 10% to 15% increase in the number of people who buy things online. Conclusion Data mining's ultimate significance for data-driven growth and progress cannot be overstated. Using the right data mining technique will give you unparalleled insight into your massive data. Data mining will only get better as technology improves, which will allow for more in-depth analysis. FAQ What are the 3 types of data mining? Pictorial data mining, text mining, social media mining, online mining, and audio and video mining are only a few examples of data mining. What are the 6 processes of data mining? Data mining is both an analytical process and a collection of algorithms and models. The CRISP-DM process model has been broken down, just like the CIA Intelligence Process. These parts are business understanding, data understanding, data preparation, modeling, evaluation, and deployment. What are the most commonly used data mining processes? Clustering, data cleansing, association, data warehousing, machine learning, data visualization, classification, neural networks, and prediction are just a few of the important data mining techniques to consider when starting out in the industry.

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Big Data Management

Can Blockchain Change The Game Of Data Analytics And Data Science?

Article | June 1, 2022

Blockchain has been causing ripples across major industries and verticals in the recent couple of years. We are seeing the future potential of blockchain technology that is scaling beyond just cryptocurrencies and trading. It is only natural that Blockchain is going to have a huge impact on Data Analytics, another field that has been booming and seems to continue in the same trajectory for the foreseeable future. However, very little research has been done on the implications of blockchain on Data Science or the potential of Data Science in Blockchain. While Blockchain is about validating data and data science is about predictions and patterns, they are linked together by the fact that they both use algorithms to control interactions between various data points. Blockchain in Big Data Analytics Big Data has traditionally been a very centralized method where we had to collate data from various sources and bring it together in one place. Blockchain, considering its decentralized nature can potentially allow analysis of data to happen at the origin nodes of individual sources. Also, considering that all data parsed through blockchain is validated across networks in a fool proof manner, the data integrity is ensured. This can be a game changer for analytics. With the digital age creating so many new data points and making data more accessible than ever, the need for diving into depth with advanced analytics has been realized by businesses around the world. However, the data is still not organized and it takes a very long time to bring them together to make sense of it. The other key challenge in Big Data remains data security. Centralized systems historically have been known for their vulnerability for leaks and hacks. A decentralized infrastructure can address both of the above challenges enabling data scientists to build a robust infrastructure to build a predictive data model and also giving rise to new possibilities for more real time analysis. Can Blockchain Enhance Data Science? Blockchain can address some of the key aspects of Data Science and Analytics. Data Security & Encoding: The smart contracts ensure that no transaction can be reversed or hidden. The complex mathematical algorithms that form the base of Blockchain are built to encrypt every single transaction on the ledger. Origin Tracing & Integrity: Blockchain technology is known for enabling P2P relationships. With blockchain technology, the ledgers can be transparent channels where the data flowing through it is validated and every stakeholder involved in the process is made accountable and accessible. This also enables the data to be of higher quality than what was possible with traditional methods. Summing Up Data science itself is fairly new and advancing in recent years. Blockchain Technology, as advanced as it seems, is still at what is believed to be a very nascent stage. We have been seeing an increasing interest in data being moved to the cloud and it is only a matter of time when businesses will want it to be moved to decentralized networks. On the other hand, blockchain’s network and server requirements are still not addressed and data analytics can be very heavy on the network, considering the volume of data collected for analysis. With very small volumes of data stored in blocks, we need viable solutions to make sure data analysis in blockchain is possible at scale. At Pyramidion, we have been working with clients globally on some exciting blockchain projects. These projects are being led by visionaries, who are looking to change how the world functions, for good. Being at the forefront of innovation, where we see the best minds working on new technologies, ICOs and protocols, we strongly believe it is only a matter of time before the challenges are addressed and Blockchain starts being a great asset to another rapidly growing field like Data Science and Data Analytics.

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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.

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.

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