and get fully confidential personalized recommendations for your software and services search. "Now, attributes used to feed predictive algorithms can now be appended to account records to support both intricate and automated segmentation. Forward looking big data analytics requires statistical analysis, statistical forecasting, casual analysis, optimization, predictive modeling and text mining on the large chunk of data available. Intuitively design very complex predictive models using casual factors. With the Big data analytics the relevant information from data warehouse in terabytes, petabytes and exabytes can be extracted and analyzed to transform the business decisions for the future. That said, predictive analytics is not like a crystal ball or Biff Tannen's sports almanac from Back to the Future 2. Beats include: startups, business and venture capital, blockchain and cryptocurrencies, AI, augmented and virtual reality, IoT and automation, legal cannabis tech, social media, streaming, security, mobile commerce, M&A, and entertainment. This affects every business, governments and individual. With these technologies, it is now possible to bring insights from these data in to the day to day decision making process. For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment. Some of the examples where Predictive Analytic can be used on Big data are : You may also like to read, Predictive Analytics Free Software, Top Predictive Analytics Software, Predictive Analytics Software API, Top Free Data Mining Software, Top Data Mining Software,and Data Ingestion Tools. The specialist field of Big Data & Analytics therefore requires a different set of skills and tools. Tools such as our Editors' Choices Tableau Desktop (Visit Store at Tableau) and Microsoft Power BI (Visit Site at Microsoft Power BI) sport intuitive design and usability, and large collections of data connectors and visualizations to make sense of the massive volumes of data businesses import from sources such as Amazon Elastic MapReduce (EMR), Google BigQuery, and Hadoop distributions from players such as Cloudera, Hortonworks, and MapR. Today due to the incessant growth of big data and the need to make data-driven decisions, it is imperative on each and every organization to make use of Predictive Analytics. Google uses ML algorithms in its data centers to run predictive maintenance on the server farms powering its Google Cloud Platform (Visit Site at Google Cloud) (GCP) public cloud infrastructure. NewSQL relational database management systems provide the same scalable performance for OLTP – online transaction processing read-write workloads. Are you are looking for data-driven services to aid your entrepreneurial efforts? The data mining and text analytics along with statistics , allows the business users to create predictive intelligence by uncovering patterns and relationships in both the structured and unstructured data. Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. Predictive analytics — Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions. Due to its multiple benefits, over 49% of the companies make use of it … Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning and assorted mathematical processes. © 1996-2020 Ziff Davis, LLC. These are simple metrics but often too voluminous to manage without an analytics tool. Quality improvement is one of the most common, functional forms of predictive analytics. By clicking Sign In with Social Media, you agree to let PAT RESEARCH store, use and/or disclose your Social Media profile and email address in accordance with the PAT RESEARCH  Privacy Policy  and agree to the  Terms of Use. The first among these is volume. Organizations use predictive analytics in a variety of different ways, from predictive marketing and data mining to applying machine learning (ML) and artificial intelligence (AI) algorithms to optimize business processes and uncover new statistical patterns. Aside from regression analysis (the intricacies and subsets of which you can read more about in this Harvard Business Review primer), predictive analytics is also using progressively more data mining and ML. The algorithms use data on weather, load, and other variables to adjust data center cooling pumps preemptively and significantly reduce power consumption. ML innovations such as neural networks and deep learning algorithms can process these unstructured data sets faster than a traditional data scientist or researcher, and with greater and greater accuracy as the algorithms learn and improve. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics is not a branch of traditional analytics such as reporting or statistical analysis. How do I leverage the past to segment regions to concentrate to reduce the drop moving forward? Achieving Smarter Data-Driven Initiatives NEC Analytics leverages decades of best practices to deliver the right information to the right people at the right time. You've got a predictive model from which to extrapolate an effective strategy for pitching and selling a product to the right leads. "B2B marketers have traditionally been able to segment only by generic attributes, like industry, and did so with such manual effort that personalization applied only to highly prioritized campaigns," said Snow. ML techniques are, with greater regularity, becoming the sifting pans and pickaxes for finding the gold data nuggets. July 07, 2020 - In the midst of a situation as uncertain as the COVID-19 pandemic, the healthcare industry has sought to use big data and predictive analytics tools to better understand the virus and its spread.. For more coronavirus updates, visit our resource page, updated twice daily by Xtelligent Healthcare Media.. It uses patented big data analytics and machine learning with automated monitoring and remediation to help reduce IT costs, allowing you to act faster. Predictive analytics look at patterns in data … The feature uses a ML algorithm to process satisfaction survey results, throwing variables including time to resolve a ticket, customer service response latency, and specific ticket wording into a regression algorithm to calculate a customer's projected satisfaction rating. Join over 55,000+ Executives by subscribing to our newsletter... its FREE ! Think about a sales representative looking at a lead profile in a customer relationship management (CRM) platform such as (Visit Site at . Linguistic analysis and extracts relevant content from files, Web logs and social media. Analytics is probably the most important tool a company has today to gain customer insights.This is why the Big Data space is set to reach over $273 Billion by … ADDITIONAL INFORMATIONI too think this was interesting reading; it covered many of the salient points of Big Data Analytics. Thank you ! What are a few planned scenarios moving forward ? BI tools and open-source frameworks such as Hadoop are democratizing data as a whole but, aside from B2B marketing, predictive analytics is also being baked into more and more cloud-based software platforms across a host of industries. Big Data and Predictive Analytics differ strongly from regular Business Intelligence projects, where reports, KPI management, and dashboarding are central. These platforms are still very much in their early days, but the idea of using data to predict which job seekers are the best fit for specifics jobs and companies has the potential to reinvent how human resources (HR) managers recruit talent. "Descriptive or historical analytics is the foundation on which an algorithm might be developed. We've only scratched the surface, both in the ways different industries could integrate this type of data analysis and the depths to which predictive analytics tools and techniques will redefine how we do business in concert with the evolution of AI. DW/BI vendors have to start offering or, at least, showing on their product road maps how they address the issue of ZLE or VLLE in an interoperable, heterogeneous environment. Gartner, I believe, published a report on Zero Latency Enterprises [ZLE] in a paper a number of years ago, but no one today save for SAP, and they vaguely refer to ZLE, has taken on the requirement of ZLE or very low latency [VLLE.] March towards business goals faster by turning dormant data into new opportunities making use of big data analytics. The algorithms and models can't tell your business beyond the shadow of a doubt that its next product will be a billion-dollar winner or that the market is about to tank. What Is Predictive Analytics? I’d like to see the authors [Gartner, perhaps] reintroduce this requirement as it applies to Big Data Predictive Analytics. Predictive analytics is an enabler of big data: Businesses collect vast amounts of real-time customer data and predictive analytics uses this historical data, combined with customer insight, to predict future events. You may unsubscribe from the newsletters at any time. Data is emerging as the world’s newest resource for competitive advantage among nations, organizations and business. is a leading authority on technology, delivering Labs-based, independent reviews of the latest products and services. Big Data Analytics will help organizations in providing an overview of the drivers of their business by introducing big data technology into the organization. These use cases are just the tip of the iceberg in exploring all of the ways predictive analytics is changing business, many more of which we'll get into below. Big Data gained huge acceptance from almost all the businesses in very less or no time. Predictive analytics describe the use of statistics and modeling to determine future performance based on current and historical data. This newsletter may contain advertising, deals, or affiliate links. Which products and product groups are our best and worst? This has its purpose and business uses, but doesnot meet the needs of a forward looking business. It's the same way IBM Watson works, and open-source toolkits such as Google's TensorFlow and Microsoft's CNTK offer ML functionality along the same lines. What do you do when your business collects staggering volumes of new data? I would like to see more on this topic. It is about finding predictive models that firms can use to predict future business outcomes and/or customer behavior.". The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future. Experts predict that by 2020, the volume of data in the world will grow to 40 Zettabytes. Identification Models: Identifying and acquiring prospects with attributes similar to existing customers. Predictive analytics is often discussed in the context of big data, Engineering data, for example, comes from sensors, instruments, and connected systems out in the world. "Descriptive analytics, while not particularly 'advanced,' simply capture things that happened," said Snow. Subscribing to a newsletter indicates your consent to our Terms of Use and Privacy Policy. Predictive modeling based techniques help to work in a streamlined fashion and get the results delivered as per the specific framework. § I expect that ZLE will become part of the price of entry into the arena as data volumes continue to grow. {"cookieName":"wBounce","isAggressive":false,"isSitewide":true,"hesitation":"20","openAnimation":"rotateInDownRight","exitAnimation":"rotateOutDownRight","timer":"","sensitivity":"20","cookieExpire":"1","cookieDomain":"","autoFire":"","isAnalyticsEnabled":true}, What is Big data Analytics and Predictive Analytics, NewSQL relational database management systems, Customer Churn, Renew, Upsell, Cross Sell Software Tools. Prescriptive analytics is where insight meets action. "The most common entry point for B2B marketers into predictive marketing, predictive scoring adds a scientific, mathematical dimension to conventional prioritization that relies on speculation, experimentation, and iteration to derive criteria and weightings," said Snow. It is estimated that every day we create 2.5 quintillion bytes of data from a variety of sources. It's basically computers learning from past behavior about how to do certain business processes better and deliver new insights into how your organization really functions. Predictive Analytics Is Everywhere As the BI landscape evolves, predictive analytics is finding its way into more and more business use cases. These technologies are hadoop, mapreduce, massively parallel processing databases, in memory database, search based applications, data-mining grids, distributed file systems, distributed databases, cloud etc. The challenges in Big data includes capture, curation, storage, search, sharing, transfer, analysis and visualization of the data. Take online dating company eHarmony's Elevated Careers website and the handful of other vendors in the "predictive analytics for hiring" space. The New Streaming Giants Explained, The Dos and Don'ts of Securing Your VoIP Communications. 4.Massively parallel processing databases. Predictive analytics is about recognizing patterns in data to project probability, according to Allison Snow, Senior Analyst of B2B Marketing at Forrester. Drilling down deeper, Snow identified three categories of B2B marketing use cases she said dominate early predictive success and lay the foundation for more complex use of predictive marketing analytics. Hadoop is an open source Apache implementation project. What do you do when your business collects staggering volumes of new data? Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. § None of the vendors claiming to be in the Big Data space are taking on the problem of zero [or very low] data latency. How much commission did the sales folks accumulate ? Sign up for What's New Now to get our top stories delivered to your inbox every morning. What are the business benefits of Big data Analytics? These can not be achieved by standard data warehousing applications. Data mining grids are environment which uses grid computing concepts, which allows to integrate data from various online and remote data sources. With the Big data analytics the relevant information from data warehouse in terabytes, petabytes and exabytes can be extracted and analyzed to transform the business decisions for the future. This kind of predictive maintenance is becoming commonplace in factories as well. A few examples come to mind: capturing a customer before they’ve left the store in retail; real time fraud detection in credit card processing as offered in a comForte paper by comparing card transactions with something seemingly as insignificant as a Tweet. • Multi core processors • Lower power consumption • Low cost storage • High speed local networking. But before we get into all of the fascinating ways businesses and technology companies are employing predictive analytics to save time, save money, and gain an edge over the rest of the market, it's important to talk about exactly what predictive analytics is and what it's not. Big Data integration capabilities with traditional databases and other systems. Big Data holds the answer," he simply corroborated companies' dependency on Big data. MapReduce was created by Google in 2004. Definition. A database management system that primarily relies on main memory for computer data storage is called an In memory database. These self-service tools don't necessarily have the most advanced predictive analytics features yet, but they make the Big Data a lot smaller and easier to analyze and understand. "In this use case, accounts that exhibited desired behavior (made a purchase, renewed a contract, or purchased additional products and services) serve as the basis of an identification model," said Snow. Visualize, discover, and share hidden insights for forward looking plan. October 28, 2020 - A predictive analytics tool has helped public health leaders in Chicago improve the quality of COVID-19 data, reducing the category of “unknown” race in tests from 47 percent to 11 percent.. For more coronavirus updates, visit our resource page, updated twice daily by Xtelligent Healthcare Media.. Now plop those variables into a regression equation and voila! In B2B marketing, Snow said enterprises and SMBs use predictive marketing for the same reasons they use any strategy, tactic, or technology: to win, retain, and serve customers better than those that don't. What is IT Infrastructure Library (ITIL)? The full Report discusses Machine Learning use … Predictive analytics enable organizations to use big data (both stored and real-time) to move from a historical view to a forward-looking perspective of the customer. Data analytics involves finding hidden patterns in a large amount of dataset to segment and group data into logical sets to find behavior and detect trends whereas Predictive analytics involves the use of some of the advanced analytics techniques. As we inch closer to truly mapping an artificial brain, the possibilities are endless. Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. This use case help sales and marketers drive outbound communications with relevant messages, enable substantial conversations between sales and prospects, and inform content strategy more intelligently.". For example, stores that use data from loyalty programs can analyze past buying behavior to predict the coupons or promotions a customer is … "This use case help sales and marketers find valuable prospects earlier in the sales cycle, uncover new marketers, prioritize existing accounts for expansion, and power account-based marketing (ABM) initiatives by bringing to the surface accounts that can reasonably be expected to be more receptive to sales and marketing messages.". How data mining, regression analysis, machine learning (ML), and the democratization of data intelligence and visualization tools are changing the way we do business. In case of sampling a subject of interest, the more samples one has; the better is the result. It can tell you that a person bought a ticket to an event, visited the hospital for a malady, or clicked on a website. These are from the computer notes to posts on social media sites and from purchase transaction records to pictures. By successfully applying predictive analytics the businesses can effectively interpret big data for their benefit. We offer vendors absolutely FREE! What then happens to expected performance expectations [or agreements.]. Privacy Policy: We hate SPAM and promise to keep your email address safe. It's a bunch of data analysis technologies and statistical techniques rolled up under one banner. Rob was previously Assistant Editor and Associate Editor in PCMag's Business section. Top Predictive Lead Scoring Software, Top Artificial Intelligence Platforms, Top Predictive Pricing Platforms,and Top Artificial Neural Network Software, and Customer Churn, Renew, Upsell, Cross Sell Software Tools. To wit: § There is not any space allotted in the literature to address the management requirements of hyper-large clusters, and from what I’ve read; I don’t see vendors offering any products that speak to the point. However, often the requirements for big data analysis are really not well understood by the developers and business owners, thus creating an undesirable product. Business system data at a company might include transaction data, sales results, customer complaints, and marketing information. Explore Further. If yes, you should try Predictive Analytics Services. The core technique is regression analysis, which predicts the related values of multiple, correlated variables based on proving or disproving a particular assumption. The big change feeding into the predictive analytics boom is not just the advancement of ML and AI, but that it's not just data scientists using these techniques anymore. Predictive Analytics & Big Data. Applied Prediction “The powerhouse organizations of the Internet era, which include Google and Amazon … have business models that hinge on predictive models based on machine learning.” It is estimated that every day we create 2.5 quintillion bytes of data from a variety of sources. The scope and complexity of the data differ, too. Hadoop Distributed File System for faster ‘reading from’ and ‘loading to’ performance and scalability. The list of potential business apps goes on and on, from how predictive analytics is changing the retail industry to fintech start-ups using predictive modeling on fraud analysis and financial transaction risk. Keep an eye on your inbox! Specifically; in a Hadoop cluster [for example] with perhaps 100s of nodes; what is the impact on management of that cluster’s processing capacity and operations staff? Hadoop enables applications to work with huge amounts of data stored on various servers. Search based applications are search engine platform is used to aggregate and classify data and use natural language technologies for accessing the data. PCMag, and PC Magazine are among the federally registered trademarks of Ziff Davis, LLC and may not be used by third parties without explicit permission. These collection of data sets which are so large and complex and are difficult to process using the on hand database management tools are known as Big data. Provide Alert when market share for my products are dropping in specific regions. Run by Darkdata Analytics Inc. All rights reserved. Predictive analytics is the practical result of Big Data and business intelligence (BI). Coming from the healthcare space, one of the things that always fascinated me was the ability to use this wealth of data to do predictive analytics on treatment plans to improve patient outcomes. Cloud computing is distributed computing over a network. opportunity to maintain and update listing of their products and even get leads. It has a number of features in marked contrast to much Big Data. Data from Multiple sources analysed for one business solution. You may like to review the following Bigdata articles : Big data and Predictive Analytics processing. ADDITIONAL INFORMATIONThis was a very interesting read. "This use case help sales and marketers identify productive accounts faster, spend less time on accounts less likely to convert, and initiate targeted cross-sell or upsell campaigns.". Big Data is often transactional or behavioral. Our Certificate in Data Analytics, Big Data, and Predictive Analytics, available in a fully online format, will help you build the full range of skills you need to advance in your current job or start a new one. Prior to that, he served as an editor at SD Times. There are a couple of items, I respectfully submit that the author did not address [although they may be addressed in the links provided,] and which I’d like to see addressed at some point. These Are the Best Space Management Tools, Zoom Alternatives: Best Free Services for Group Video Chatting During the Pandemic, Peacock? The reason that big data is currently a hot topic is partly due to the fact that the technology -MapReduce, Hadoop, In memory database, Massively parallel processing database, database grids, search based functionality etc are now available to process these large data sets which are mostly a combination of structured and unstructured data. The business data is also growing at these same exponential rate too.Along with the volume, the number of sources, from where the data is extracted are also growing. Check your inbox now to confirm your subscription. Powerful partners in the pursuit of smart business insight. There are performance issues, when these high volume past data are used in the relational data model, for a forward looking big data analytics, for future in the current system landscape in many organizations. Where and what was the Rx trend and what predictions are there for future ? We are a Pan African first and only comprehensive one stop platform and center of excellence for Data Science based in Nairobi, Kenya and Johannesburg, South Africa from where we serve clients across the East and South African region.Our mission is to empower the next generation of business leaders and innovators in Data Science. 2. Unstructured data, such as texts, notes, logs makes up a large chunk of this data volume and these requires  text mining to analyze the data. Other assumptions are that the variables are product cost, the lead's role within a business, and the company's current profitability ratio. 3. Below is the list of points that describes the key difference between Big Data and Predictive Analytics : 1. Big data analytics is going to be mainstream with increased adoption among every industry and forma virtuous cycle with more people wanting access to even bigger data. Let's say the assumption is, the lead will buy your product. If you click an affiliate link and buy a product or service, we may be paid a fee by that merchant. Predictive analytics is the use of advanced analytic techniques that leverage historical data to uncover real-time insights and to predict future events. You can also find his business and tech coverage on Entrepreneur and Fox Business. "Generally speaking, dashboards and reporting are the most common use for predictive analytics within organizations today. Thus far, no vender that I’ve researched, except perhaps one, offers a single system image and automatically accounts for recovery from downed nodes at the OS layer. Essentially, predictive analytics is just a name for datasets, but predictive analytics has been directly linked to benefiting four critical manufacturing processes, reports Toolbox for IT. Bigdata Platforms and Bigdata Analytics Software. Data in main memory can be accessed faster than data stored in hard disk or other flash storage device. Data/Analytics Platform: Coordinate across stakeholders to build a platform that’s based on a reference architecture and is flexible with the evolving discipline (e.g., open source, catalogue of options, cloud or self-service capabilities); use analytics demand to build a business case for technology investment PAT RESEARCH is a B2B discovery platform which provides Best Practices, Buying Guides, Reviews, Ratings, Comparison, Research, Commentary, and Analysis for Enterprise Software and Services. Breaking Down Predictive, Prescriptive, and Descriptive AnalyticsIn another Forrester report entitled 'Predictive Analytics Can Infuse Your Applications With An 'Unfair Advantage,'" Principal Analyst Mike Gualtieri points out that "the word 'analytics' in 'predictive analytics' is a bit of a misnomer. Snow said there is a broad series of use cases for predictive analytics in business today, from detecting point-of-sale (POS) fraud, automatically adjusting digital content based on user context to drive conversions, or initiating proactive customer service for at-risk revenue sources. How will the digital media help me target new regions and what is going to be my marketing effectiveness . Big data Analytics and Predictive Analytics. The business benefits of Big Data Analytics include turn dormant data into new opportunities making use of big data analytics, intuitively design very complex predictive models using casual factors, Big Data integration capabilities with traditional databases and other systems, Hadoop Distributed File System , wide range of Big data applications and analytics to analyse more history data and many more. Big Data has turned out to be crucial for the modern business world; especially data-driven decisions. How to Free Up Space on Your iPhone or iPad, How to Save Money on Your Cell Phone Bill, How to Find Free Tools to Optimize Your Small Business, How to Get Started With Project Management, Predictive Analytics Can Infuse Your Applications With An 'Unfair Advantage, Amazon Starts Filling Its AWS Data Centers With Mac Minis, Microsoft Teams Now Lets You Chat With 300 Friends and Family for 24 Hours, Google Pay Redesign Focuses on Organizing, Protecting Your Money, Huawei Sells Off Honor Smartphone Business to Ensure the Brand's Survival, The Best Small Business Accounting Software for 2020, The Best Online Accounting Services for Freelancers, Headed Back to the Office? Massively parallel processing is a loosely coupled databases where each server or node have memory or processors to process data locally and data is partitioned across multiple servers or nodes. Predictive analytics determine what data is predictive of the outcome you wish to predict.". Why not get it straight and right from the original source. This certificate is one of several that offer financial aid options for eligible students. The display of third-party trademarks and trade names on this site does not necessarily indicate any affiliation or the endorsement of PCMag. Beginners guide to big data: Big data explained. Even the predictive analytics on large data is more accurate and help discover patterns. Predictive Analytics identifies meaningful patters of Big data to predict future events and assess the attractiveness of various options. In other words, models produce insights but not explicit instructions on what to do with them. Predictive analytics isn't a black-and-white concept or a discrete feature of modern database managers. Ever since McKinsey Global Institute (MGI) released Big Data: The Next Frontier For Innovation, Competition, and Productivity, it has witnessed the rise and triumph of Machine Learning, especially in Predictive Analytics. Gartner added big data to its 2011 hype cycle and has called it one of the top 10 strategic technologies for 2012, stating, “The size, complexity of formats and speed of delivery exceeds the capabilities of traditional data management technologies; it requires the use of new or exotic technologies simply to manage the volume alone".Big data has few key characteristics such as volume, sources, velocity, variety and veracity. It does an enterprise little good to claim predictive analysis and real-time monitoring capabilities via a DW unless the ZLE issue is tackled head on. Data is still a means to make an educated guess; we're simply a lot better educated than we used to be. On the other hand, Predictive analytics has to do with the applicat… Today's business applications are raking in mountains of new customer, market, social listening, and real-time app, cloud, or product performance data. Chakib Chraibi, chief data scientist at the National Technical Information Service at the Commerce Department, said his agency uses predictive analytics in collaboration with organizations to optimize resources. These self-service tools don't necessarily have the most advanced predictive analytics features yet, but they make the Big Data a lot smaller and easier to analyze and understand. Distributed file system is a shared file system which is shared by being simultaneously mounted on multiple servers. These ad hoc analysis looks at the static past of data. 1. 71. capacity, but also requiring an infrastructure and expertise to process, and handle . SQL and No SQL Cloud database runs on a cloud computing platform. The use of predictive analytics is a key milestone on your analytics journey — a point of confluence where classical statistical analysis meets the new world of artificial intelligence (AI). These tools often lack the link to business decisions, process optimization, customer experience, or any other action. To perform an ETL activity off-line in a batch or parallel batch mode won’t cut the mustard until someone figures out how to get more than 24 hours into a day. Predictive analytics is one way to leverage all of that information, gain tangible new insights, and stay ahead of the competition. Data is increasingly accelerating the velocity at which it is created, as the process are moved from batch to a real time business. Based on previous Rx, what clusters of regions should I market to? They answer the question, 'I now know the probability of an outcome [and] what can be done to influence it in the direction that's positive for me,' whether that be preventing customer churn or making a sale more likely.". Predictive Scoring: Prioritizing known prospects, leads, and accounts based on their likelihood to take action. § As regards to in-memory data bases, it does a DW owner little good to have an in-memory data base if that owner is always looking at stale information. Likewise, with a large enough cluster, one can reasonably expect to have a downed node almost consistently. Wide range of Big data applications and analytics to analyse more history data. It is a model inspired by the map and reduce functions for processing large data sets with a parallel, distributed algorithm on a cluster.
2020 predictive analytics in big data