Bihar Industries Association - A CONCEPT OF BIG DATA (DATA MINNING)
A CONCEPT OF BIG DATA (DATA MINNING) Study by Mukesh Kumar , Chairman , IT & ITes Sub Committee ( BIA ) What is Data? The quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media. What is Big Data? Big Data is also data but with a huge size. Big Data is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. In short such data is so large and complex that none of the traditional data management tools are able to store it or process it efficiently. Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Big data is a term used to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with. ... Big data was originally associated with three key concepts: volume, variety, and velocity. The data lying in the servers of your company was just data until yesterday – sorted and filed. Suddenly, the slang Big Data got popular, and now the data in your company is Big Data. The term covers each and every piece of data your organization has stored till now. It includes data stored in clouds and even the URLs that you bookmarked. Your company might not have digitized all the data. You may not have structured all the data already. But then, all the digital, papers, structured and non-structured data with your company is now Big Data. In short, all the data – whether or not categorized – present in your servers is collectively called BIG DATA. All this data can be used to get different results using different types of analysis. It is not necessary that all analysis use all the data. The different analysis uses different parts of the BIG DATA to produce the results and predictions necessary. Big Data is essentially the data that you analyze for results that you can use for predictions and other uses. When using the term Big Data, suddenly your company or organization is working with top level Information technology to deduce different types of results using the same data that you stored intentionally or unintentionally over the years. How big is Big Data Essentially, all the data combined is Big Data, but many researchers agree that Big Data – as such – cannot be manipulated using normal spreadsheets and regular tools of database management. They need special analysis tools like Hadoop (we’ll study this in a separate post) so that all the data can be analyzed at one go (may include iterations of analysis). Contrary to the above, though I am not an expert on the subject, I would say that data with any organization – big or small, organized or unorganized – is Big Data for that organization and that the organization may choose its own tools to analyze the data. Normally, for analyzing data, people used to create different data sets based on one or more common fields so that analysis becomes easy. In case of Big Data, there is no need to create subsets for analyzing it. We now have tools that can analyze data irrespective of how huge it is. Probably, these tools themselves categorize the data even as they are analyzing it. I find it important to mention two sentences from the book “Big Data” by Jimmy Guterman: “Big Data: when the size and performance requirements for data management become significant design and decision factors for implementing a data management and analysis system.” -and- “ For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.” So you see that both volume and analysis are an important part of Big Data. What is Data Mining? Big Data Concepts This is another point where most people don’t agree. Some experts say that the Big Data Concepts are three V’s: 1. Volume 2. Velocity 3. Variety Some others add few more V’s to the concept: 4. Visualization 5. Veracity (Reliability) 6. Variability and 7. Value I will cover concepts of Big Data in a separate article as this post is already getting big. In my opinion, the first three V’s are enough to explain the concept of Big Data. The Evolution of Big Data While the term “big data” is the new in this era, as it is the act of gathering and storing huge amounts of information for eventual analysis is ages old. The concept came into existence in the early 2000s when Industry analyst Doug Laney the definition of big data as the three categories as follows: Volume: Organizations collects the data from relative sources, which includes business transactions, social media and information from sensor or machine-to-machine data. Before, storage was a big issue but now the advancement of new technologies (such as Hadoop) has reduced the burden. Velocity: Data streams unparalleled speed of velocity and have improved in timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in real time operations. Variety: Data comes in all varieties in form of structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions. In SAS, we consider two additional dimensions with respect to big data: 3 Vs of Big Data : Big Data is the combination of these three factors; High-volume, High-Velocity and High-Variety. Volume Big Data observes and tracks what happens from various sources which include business transactions, social media and information from machine-to-machine or sensor data. This creates large volumes of data. Velocity The data streams in high speed and must be dealt with timely. The processing of data that is, analysis of streamed data to produce near or real time results is also fast. Variety Data comes in all formats that may be structured, numeric in the traditional database or the unstructured text documents, video, audio, email, stock ticker data. Examples Of Big Data Following are some the examples of Big Data- The New York Stock Exchange generates about one terabyte of new trade data per day. Social Media The statistic shows that 500+terabytes of new data get ingested into the databases of social media site Facebook, every day. This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc. A single Jet engine can generate 10+terabytes of data in 30 minutes of flight time. With many thousand flights per day, generation of data reaches up to many Peta bytes. Types Of Big Data Big Data' could be found in three forms: 1. Structured 2. Unstructured 3. Semi-structured Structured Any data that can be stored, accessed and processed in the form of fixed format is termed as a 'structured' data. Over the period of time, talent in computer science has achieved greater success in developing techniques for working with such kind of data (where the format is well known in advance) and also deriving value out of it. However, nowadays, we are foreseeing issues when a size of such data grows to a huge extent, typical sizes are being in the rage of multiple zeta bytes. Looking at these figures one can easily understand why the name Big Data is given and imagine the challenges involved in its storage and processing. Examples Of Structured Data An 'Employee' table in a database is an example of Structured Data Employee_ID Employee_Name Gender Department Salary_In_lacs 2365 Rajesh Kulkarni Male Finance 650000 3398 Pratibha Joshi Female Admin 650000 7465 Shushil Roy Male Admin 500000 7500 Shubhojit Das Male Finance 500000 7699 Priya Sane Female Finance 550000 Unstructured Any data with unknown form or the structure is classified as unstructured data. In addition to the size being huge, un-structured data poses multiple challenges in terms of its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos etc. Now day organizations have wealth of data available with them but unfortunately, they don't know how to derive value out of it since this data is in its raw form or unstructured format. Examples of Un-structured Data The output returned by 'Google Search' Semi-structured Semi-structured data can contain both the forms of data. We can see semi-structured data as a structured in form but it is actually not defined with e.g. a table definition in relational DBMS. Example of semi-structured data is a data represented in an XML file. Examples Of Semi-structured Data Personal data stored in an XML file- Prashant RaoMale35 Seema R.Female41 Satish ManeMale29 Subrato RoyMale26 Jeremiah J.Male35 Data Growth over the years Please note that web application data, which is unstructured, consists of log files, transaction history files etc. OLTP systems are built to work with structured data wherein data is stored in relations (tables). Characteristics Of Big Data (i) Volume – The name Big Data itself is related to a size which is enormous. Size of data plays a very crucial role in determining value out of data. Also, whether a particular data can actually be considered as a Big Data or not, is dependent upon the volume of data. Hence, 'Volume' is one characteristic which needs to be considered while dealing with Big Data. (ii) Variety – The next aspect of Big Data is its variety. Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. During earlier days, spreadsheets and databases were the only sources of data considered by most of the applications. Nowadays, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. are also being considered in the analysis applications. This variety of unstructured data poses certain issues for storage, mining and analyzing data. (iii) Velocity – The term 'velocity' refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data. Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, and social media sites, sensors, Mobile devices, etc. The flow of data is massive and continuous. (iv) Variability – This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively. Benefits of Big Data Processing Ability to process Big Data brings in multiple benefits, such as- o Businesses can utilize outside intelligence while taking decisions Access to social data from search engines and sites like facebook, twitter are enabling organizations to fine tune their business strategies. o Improved customer service Traditional customer feedback systems are getting replaced by new systems designed with Big Data technologies. In these new systems, Big Data and natural language processing technologies are being used to read and evaluate consumer responses. o Early identification of risk to the product/services, if any o Better operational efficiency Big Data technologies can be used for creating a staging area or landing zone for new data before identifying what data should be moved to the data warehouse. In addition, such integration of Big Data technologies and data warehouse helps an organization to offload infrequently accessed data. In simple way we do understand as • Big Data is defined as data that is huge in size. Bigdata is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. • Examples of Big Data generation include stock exchanges, social media sites, jet engines, etc. • Big Data could be 1) Structured, 2) Unstructured, 3) Semi-structured • Volume, Variety, Velocity, and Variability are few Characteristics of Big data • Improved customer service, better operational efficiency, Better Decision Making are few advantages of Bigdata Big Data Example – How NetFlix used it to fix its problems Towards 2008, there was an outage at NetFlix due to which many customers were left in the dark. While some could still access the streaming services, most of them could not. Some customers managed to get their rented DVDs whereas others failed. A blog post on Wall Street Journal says Netflix had just started on-demand-streaming. The outage made the management think about the possible future problems and the hence; it turned to Big Data. It analyzed high traffic areas, susceptible points, and network throughput, etc. using that data and worked on it to lower the downtime if a future problem arises as it went global. Here is the link to the Wall Street Journal Blog, if you wish to check out the examples of Big Data. The above summarizes what is Big Data in a layman’s language. You can call it a very basic introduction. I plan to write few more articles on associated factors such as – Concepts, Analysis, Tools, and uses of Big Data, Big Data 3 V’s, etc. Meanwhile, if you would like to add anything to the above, please comment and share with us. Is Big Data a Volume or a Technology? While the term may seem to reference the volume of data, that isn't always the case. The term big data, especially when used by vendors, may refer to the technology (which includes the tools and processes), that an organization requires to handle the large amounts of data and storage facilities. The term is believed to have originated with web search companies who needed to query very large distributed aggregations of loosely-structured data. Having a lot of data pouring into your organisation is one thing, being able to store it, analyse it and visualize it in real-time is a whole different ball game. More and more organisation want to have real-time insights in order to fully understand what is going on within their organisation. What are the advantages of Real-Time Big Data Analytics and what are the challenges and which tools can be used for real-time processing of Big Data? The Advantages of Real-Time Big Data Analytics The advantages of processing Big Data in real-time are many: Errors within the organisation are known instantly. Real-time insight into errors helps companies react quickly to mitigate the effects of an operational problem. This can save the operation from falling behind or failing completely or it can save your customers from having to stop using your products. New strategies of your competition are noticed immediately. With Real-Time Big Data Analytics you can stay one step ahead of the competition or get notified the moment your direct competitor is changing strategy or lowering its prices for example. Service improves dramatically, which could lead to higher conversion rate and extra revenue. When organizations monitor the products that are used by its customers, it can pro-actively respond to upcoming failures. For example, cars with real-time sensors can notify before something is going wrong and let the driver know that the car needs maintenance. Fraud can be detected the moment it happens and proper measures can be taken to limit the damage. The financial world is very attractive for criminals. With a real-time safeguard system, attempts to hack into your organisation are notified instantly. Your IT security department can take immediately appropriate action. Cost savings: The implementation of a Real-Time Big Data Analytics tools may be expensive, it will eventually save a lot of money. There is no waiting time for business leaders and in-memory databases (useful for real-time analytics) also reduce the burden on a company’s overall IT landscape, freeing up resources previously devoted to responding to requests for reports. Better sales insights, which could lead to additional revenue. Real-time analytics tell exactly how your sales are doing and in case an internet retailer sees that a product is doing extremely well, it can take action to prevent missing out or losing revenue. Keep up with customer trends: Insight into competitive offerings, promotions or your customer movements provides valuable information regarding coming and going customer trends. Faster decisions can be made with real-time analytics that better suit the (current) customer. The Challenges of Real-Time Big Data Analytics Of course, Real-Time Big Data Analytics is not only positive as it also offers some challenges. It requires special computer power: The standard version of Hadoop is, at the moment, not yet suitable for real-time analysis. New tools need to be bought and used. There are however quite some tools available to do the job and Hadoop will be able to process data in real-time in the future. Using real-time insights requires a different way of working within your organisation: if your organisation normally only receives insights once a week, which is very common in a lot of organizations, receiving these insights every second will require a different approach and way of working. Insights require action and instead of acting on a weekly basis this action is now in real-time required. This will have an affect on the culture. The objective should be to make your organisation an information-centric organisation. Real-Time Big Data Analytics Tools More and more tools offer the possibility of real-time processing of Big Data. As Hadoop at the moment does not offer Real-Time Big Data Analytics, other products should be used. Fortunately, there a quite some (open source) tools that do the job well. Storm Storm, which is now owned by Twitter, is a real-time distributed computation system. It works the same way as Hadoop provides batch processing as it uses a set of general primitives for performing real-time analyses. Storm is easy to use and it works with any programming language. It is very scalable and fault-tolerant. Cloudera Cloudera offers the Cloudera Enterprise RTQ tools that offers real-time, interactive analytical queries of the data stored in HBase or HDFS. It is an integral part of Cloudera Impala, an open source tool of Cloudera. Gridgrain GridGain is an enterprise open source grid computing made for Java. It is compatible with Hadoop DFS and it offers a substitute to Hadoop’s Map Reduce. GridGain offers a distributed, in-memory, real-time and scalable data grid, which is the link between data sources and different applications. Space Curve The technology that SpaceCurve is developing can discover underlying patterns in multidimensional geodata. Geodata is different data than normal data as mobile devices create new data really fast and not in a way traditional databases are used to. They offer a Big Data platform and their tool set a new world record on February 12, 2013 regarding running complex queries with tens of gigabytes per second. Of course there are many more different Real-Time Big Data Analytics tools available but it would be a bit too much to describe all of theses tools here. Fact is that Real-Time Big Data Analytics is a Big Data trend that will increase substantially in the coming period and will have a large impact on any organisation due to the many advantages. Real-Time Big Data Analytics is probably the ultimate usage of Big Data. Read More: 7 Steps: How to Create a Successful Big Data Strategy Why is Big Data Important ? The importance of big data does not revolve around how much data a company has but how a company utilises the collected data. Every company uses data in its own way; the more efficiently a company uses its data, the more potential it has to grow. The company can take data from any source and analyse it to find answers which will enable: 1. Cost Savings : Some tools of Big Data like Hadoop and Cloud-Based Analytics can bring cost advantages to business when large amounts of data are to be stored and these tools also help in identifying more efficient ways of doing business. 2. Time Reductions: The high speed of tools like Hadoop and in-memory analytics can easily identify new sources of data which helps businesses analyzing data immediately and make quick decisions based on the learnings. 3. New Product Development : By knowing the trends of customer needs and satisfaction through analytics you can create products according to the wants of customers. 4. Understand the market conditions : By analyzing big data you can get a better understanding of current market conditions. For example, by analyzing customers’ purchasing behaviors, a company can find out the products that are sold the most and produce products according to this trend. By this, it can get ahead of its competitors. 5. Control online reputation: Big data tools can do sentiment analysis. Therefore, you can get feedback about who is saying what about your company. If you want to monitor and improve the online presence of your business, then, big data tools can help in all this. Conclusion: Big Data-A Competitive Advantage for Businesses The use of Big Data is becoming common these days by the companies to outperform their peers. In most industries, existing competitors and new entrants alike will use the strategies resulting from the analyzed data to compete, innovate and capture value. Big Data helps the organizations to create new growth opportunities and entirely new categories of companies that can combine and analyze industry data. These companies have ample information about the products and services, buyers and suppliers, consumer preferences that can be captured and analyzed. It also understands and optimizes business processes. Retailers can easily optimize their stock based on predictive models generated from the social media data, web search trends and weather forecasts. Let our experts guide you further on the major benefits of using Big Data outlined specifically around your business model. ig data is a term defined for data sets that are large or complex that traditional data processing applications are inadequate. Big Data basically consists of analysis zing, capturing the data, data creation, searching, sharing, storage capacity, transfer, visualization, and querying and information privacy. • development of the organization. What are the categories which come under Big Data? Big data works on the data produced by various devices and their applications. Below are some of the fields that are involved in the umbrella of Big Data. Black Box Data: It is an incorporated by flight crafts, which stores a large sum of information, which includes the conversation between crew members and any other communications (alert messages or any order passed)by the technical grounds duty staff. Social Media Data: Social networking sites such as Face book and Twitter contains the information and the views posted by millions of people across the globe. Stock Exchange Data: It holds information (complete details of in and out of business transactions) about the ‘buyer’ and ‘seller’ decisions in terms of share between different companies made by the customers. Power Grid Data: The power grid data mainly holds the information consumed by a particular node in terms of base station. Transport Data: It includes the data’s from various transport sectors such as model, capacity, distance and availability of a vehicle. Search Engine Data: Search engines retrieve a large amount of data from different sources of database. What is the importance of Big Data? The importance of big data is how you utilize the data which you own. Data can be fetched from any source and analyze it to solve that enable us in terms of 1) Cost reductions 2) Time reductions, 3) New product development and optimized offerings, and 4) Smart decision making. Combination of big data with high-powered analytics, you can have great impact on your business strategy such as: • Finding the root cause of failures, issues and defects in real time operations. • Generating coupons at the point of sale seeing the customer’s habit of buying goods. • Recalculating entire risk portfolios in just minutes. • Detecting fraudulent behavior before it affects and risks your organization. Who are the ones who use the Big Data Technology? Banking Large amounts of data streaming in from countless sources, banks have to find out unique and innovative ways to manage big data. It’s important to analyze customers needs and provide them service as per their requirements, and minimize risk and fraud while maintaining regulatory compliance. Big data have to deal with financial institutions to do one step from the advanced analytics. Government When government agencies are harnessing and applying analytics to their big data, they have improvised a lot in terms of managing utilities, running agencies, dealing with traffic congestion or preventing the affects crime. But apart from its advantages in Big Data, governments also address issues of transparency and privacy. Education Educator regarding Big Data provides a significant impact on school systems, students and curriculums. By analyzing big data, they can identify at-risk students, ensuring student’s progress, and can implement an improvised system for evaluation and support of teachers and principals in their teachings. Health Care When it comes to health care in terms of Patient records. Treatment plans. Prescription information etc., everything needs to be done quickly and accurately and some aspects enough transparency to satisfy stringent industry regulations. Effective management results in good health care to uncover hidden insights that improve patient care. Manufacturing Manufacturers can improve their quality and output while minimizing waste where processes are known as the main key factors in today’s highly competitive market. Several manufacturers are working on analytics where they can solve problems faster and make more agile business decisions. Retail Customer relationship maintains is the biggest challenge in the retail industry and the best way to manage will be to manage big data. Retailers must have unique marketing ideas to sell their products to customers, the most effective way to handle transactions, and applying improvised tactics of using innovative ideas using BigData to improve their business. Enterprise servers are using the above measures to overcome the barriers mentioned above. Differentiation between Operational vs. Analytical Systems Operational Analytical Latency 1 ms to 100 ms 1 min to 100 min Concurrency 1000 to100,000 1 to 10 Access Pattern Writes and Reads Reads Queries Selective Unselective Data Scope Operational Retrospective End User Customer Data Scientist Technology NoSQL Database MapReduce, MPP Database Cloud Computing Tops List of Emerging Risks Cloud computing is growing in popularity and has become a solution for issues that have plagued organizations and overtaxed IT departments for years. In fact, the number of cloud managed service providers is predicted to triple by 2020. While executives are keen to expand into cloud services and make them an integral part of their digital business initiatives, there are concerns. Free Webinar The cloud phenomenon continues to grow. As many companies have moved to a cloud first position, many others are finding they need advice in structuring their overall cloud journey. Join Gartner VP & Fellow Daryl Plummer as he discusses the state of cloud computing, major cloud strategy elements and way to ensure cloud success in this free on-demand webinar. 37 Big Data Case Studies with Big Results By Rob Petersen, {grow} Community Member Big Data is the collection of large amounts of data from places like web-browsing data trails, social network communications, sensor and surveillance data that is stored in computer clouds then searched for patterns, new revelations and insights. In less than a decade, Big Data is a multi-billion-dollar industry. Who’s using it? How are they apply data? What are they achieving? Here are 37 Big Data case studies where companies see big results. 1. AETNA: Looks at patient results on a series of metabolic syndrome-detecting tests, assesses patient risk factors and focuses on treating one or two things that will have the most impact (statistically speaking) on improving their health. 90% of patients who didn’t have a previous visit with their doctor would benefit from a screening, and 60% would benefit from improving their adherence to their medicine regimen. 2. AMERICAN EXPRESS: Starts looking for indicators that could predict loyalty and developed sophisticated predictive models to analyze historical transactions and 115 variables to forecast potential churn. The company believes it can now identify 24% of accounts that will close within the next four months. 3. ATLANTA FALCONS: Use GPS technology to assess player movements during practices, which helps the coaches create more efficient plays. 4. BANK OF AMERICA: “BankAmeriDeals” provides cash-back offers to credit and debit-card customers based upon analyses of their prior purchases. 5. BASIS: Is a wrist-based health tracker and online personal dashboard that helps users incorporate small, progressive health changes over time—that ultimately add up to major results. 6. BRITISH AIRWAYS: “Know Me” program combines already existing loyalty information with the data collected from customers based on their online behavior. With the blending of these two sources of information, British Airways can make more targeted offers while responding to service lapses in ways to create a more positive experience for the flyer. 7. CAESARS ENTERTAINMENT: Combines patrons’ gambling outcomes with their rewards program information to offer enticing perks to those who are losing at the tables. 8. CATAPULT: Uncovers vitally important information like whether an athlete is developing an injury, or whether certain workouts are overly stressful. That helps teams keep their players safe and game-ready. Sales grew 64% last year and Catapult now works with nearly half of NFL teams, a third of NBA teams, and 30 major college programs. 9. COMMONBOND: Is a student lending platform that connects students and graduates to alumni investors and accomplished professionals. Thus, students can access lower, fixed-rate financing—and save thousands of dollars on their repayments. 10. DELTA: With over 130 million bags checked per year, Delta has a lot of tracking data about bags and became the first major airline to allow customers to track their bags from mobile devices. To date, the app has been downloaded over 11 million times and gives customers much greater peace of mind. 11. DUETTO: Makes it easier for companies to personalize data to individuals searching online for hotels. Prices by hotels can be personalized by taking data such as how much you typically spend at the bar or casino to incentivize you with a lower price for your room. The hotel can give you a better price, knowing you’ll spend money on other services. 12. EBAY: “the Feed” is a new homepage that allows customers to follow entire categories of items no matter how obscure. This makes it easier for customers to stay on top of the latest items they have a particular interest, especially if they are collectors. 13. EVOVL: Helps large global companies make better hiring and management decisions through tpredictive analytics. Evolv crunches more than 500 million data points on gas prices, unemployment rates, and social media usage to help clients like Xerox—who has cut attrition by 20 percent—predict, for example, when an employee is most likely to leave his job. Companies like Xerox, AT&T and Kelly Services use Evolv, and on average, our clients see a $10 million impact on their P&L. Evolv’s sales grew a whopping 150% from Q3 2012 to Q3 2013. 14. GENERAL ELECTRIC: Many machines—everything from power plants to locomotives to hospital equipment—now pump out data about how they’re operating. GE’s analytics team crunches it, then rejiggers machines to be more efficient. Even tiny improvements are substantial, given the scale: By GE’s estimates, data can boost productivity in the U.S. by 1.5%, which over a 20-year period could save enough cash to raise average national incomes by as much as 30%. 15. GOOGLE: Working with the U.S. Centers for Disease Control, tracks when users are inputting search terms related to flu topics, to help predict which regions may experience outbreaks. 16. HOMER: Handcrafted by top literacy experts, helps children learn to read. It has a complete phonics program, a library of beautifully illustrated stories, hundreds of science field trips, and exciting art and recording tools—combining the best early learning techniques into an engaging app that connects learning to read with learning to understand the world. 17. IRS: Uses Big Data to stop identity theft, fraud, and improper payments, such as those who are not paying taxes and should. The system also helps to ensure compliance with tax rules and laws. So far, the IRS has stopped billions of dollars in fraud, specifically with identity theft, and recovered more than $2 billion over the last three years. 18. KAISER: Uses Big Data to study the incidence of blood clots within a group of women taking oral contraceptives. The analysis revealed that one formula contained a drug that increased the threat of blood clots by 77%—understanding these types of patterns can help many people avoid visits to the doctor or emergency room. 19. KROGER: Accesses, collects, and manages data for about 770 million consumers. Claiming 95% of sales are rung up on the loyalty card, Kroger sees an impact from its award-winning loyalty program through nearly 60% redemption rates and over $12 billion in incremental revenue by using big data and analytics. 20. LENDUP: A banking startup, evaluates whether to approve loan applicants per how a user interacts with its site. 21. NETFLIX: Having drawn in millions of users with its high-quality original programming, is now using its trove of data and analytics about international viewing habits to create and buy programming that it knows will be embraced by large, ready-made audiences. 22. NEXT BIG SOUND: Explains through analytics of online activity Wikipedia page views, Facebook Likes, You Tube Views and Twittter Mentions which bands are about to break, which late night shows impact an artist’s trajectory, and many, many other quandaries that for decades had been the exclusive domain of mercurial executives 23. NORFOLK SOUTHERN: Deploys customized software to monitor rail traffic and reduce congestion, enabling trains to operate at higher speeds. The company forecasts $200 million in savings by making trains run just 1 mph faster. 24. PALANTIR TECHNOLOGIES: Uses big data to solve security problems ranging from fraud to terrorism. Their systems were developed with funding from the CIA and are widely used by the US Government and their security agencies. 25. PROCTER & GAMBLE: Examines its business program success and react more quickly to changing market conditions, P&G needed to clearly and easily understand its rapidly growing and vast amount of data. integrated vast amounts of structured and unstructured data across research and development, supply chain, customer-facing operations, and customer interactions, both from traditional data sources and new sources of online data. Now, P&G can load and integrate data faster and execute reliable analysis at scales that were previously not possible. 26. QSTREAM: Allows sales reps to engage in fun, scenario-based challenges—complete with leaderboards and scoring—and produces sophisticated, real-time analytics. With this information, companies gain important insights into their existing knowledge gaps and are given the tools to create dynamic sales forces. 27. RED ROOF INN: Produces 10% growth year over year helping people who are stranded due to bad weather. The marketing department uses historical weather information, and begin a plan to target stranded airport passengers. With some estimated 2-3% of flights cancelled daily, 500 planes don’t take off, 90,000 passengers get stranded. The company uses big data to identify the areas of demand and uses search advertising, a focus on mobile communications, and other methods to drive digital bookings with personalized messages like ‘Stranded at O’Hare? Check out Red Roof Inn.’ 28. RENTHOP: Is an apartment search platform simplifies real estate decisions, allowing users to look at curated apartment listings from trustworthy sources and determine which apartments are worth investigating—then schedule appointments with reputable brokers and property managers. 29. SEARS: Has consolidated data relating to customers, products, sales and campaigns to reduce the time needed to launch major marketing campaigns from eight weeks to one. 30. SPRINT: Uses Big Data analytics to improve quality and customer experience while reducing network error rates and customer churn. They handle 10’s of billions of transactions per day for 53 million users, and their Big Data analytics put real-time intelligence into the network, driving a 90% increase in capacity. 31. THE WEATHER CHANNEL: Is more than just a weather channel. By analyzing the behavior patterns of its digital and mobile users in 3 million locations worldwide—along with the unique climate data in each locale—the Weather Company has become an advertising powerhouse, letting shampoo brands, for example, target users in a humid climate with a new anti-frizz product. More than half of the Weather Company’s ad revenue is now generated from its digital operations. 32. T-MOBILE: Has integrated Big Data across multiple IT systems to combine customer transaction and interactions data to better predict customer defections. By leveraging social media data (Big Data) along with transaction data from CRM and Billing systems, T-Mobile USA has could “cut customer defections in half in a single quarter”. 33. UBER: Is cutting the number of cars on the roads of London by a third through UberPool that cater to users who are interested in lowering their carbon footprint and fuel costs. Uber’s business is built on Big Data, with user data on both drivers and passengers fed into algorithms to find suitable and cost-effective matches, and set fare rates. 34. UPS: On a daily basis, UPS makes 16.9 package and document deliveries every day and over 4 billion items shipped per year through almost 100,000 vehicles. With this volume, there are numerous ways UPS uses Big Data, and one of the applications is for fleet optimization. On-truck telematics and advanced algorithms help with routes, engine idle time, and predictive maintenance. Since starting the program, the company has saved over 39 million gallons of fuel and avoided driving 364 million miles. 35. US XPRESS: A provider of a wide variety of transportation solutions collects about a thousand data elements ranging from fuel usage to tire condition to truck engine operations to GPS information, and uses this data for optimal fleet management and to drive productivity saving millions of dollars in operating costs. 36. VIROOL: is a powerful video service, allowing clients to target desired audiences on its global network of more than 100 million viewers. With affordable campaigns starting as low as $10 per day, Virool gives anyone the ability to distribute YouTube video content through a series of online publishers and offers clients full transparency with accurate and detailed analytics. 37. WAL-MART: Relies on text analysis, machine learning and even synonym mining to produce relevant search results. Wal-Mart says adding semantic search has improved online shoppers completing a purchase by 10% to 15%. In Wal-Mart terms, that is billions of dollars. Do these case studies show how Big Data can work? And results that can be achieved? Does your company need help putting Big Data to use? Rob Petersen is an experienced advertising and marketing executive and the founder of theBarnRaisers agency. Follow Rob on Twitter:@RobPetersen Illustration courtesy Flickr CC and Kevin Dooley Three implications for Big Data and your marketing department In "Big Data and Analytics" The data-driven sales team Over the last three years I've been doing quite a bit of "social selling" training for big companies and it has been an interesting experience. I see that some people are eager to embrace the change. Others are in the class because they are being FORCED to embrace the change.… In "Marketing Solutions" The Data-Driven Sales Team: Why Social Selling Works In "Big Data and Analytics" Examples of Big Data Analytics In Healthcare That Can Save People Big Data has changed the way we manage, analyze and leverage data in any industry. One of the most promising areas where it can be applied to make a change is healthcare. Healthcare analytics have the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. Average human lifespan is increasing along world population, which poses new challenges to today’s treatment delivery methods. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for best strategies to use these numbers. In this article, we would like to address the need of big data in healthcare: why and how can it help? What are the obstacles to its adoption? We will then provide you with 12 big data examples in healthcare that already exist and that we benefit from. What Is Big Data In Healthcare? The application of big data analytics in healthcare has a lot of positive and also life-saving outcomes. Big data refers to the vast quantities of information created by the digitization of everything that gets consolidated and analyzed by specific technologies. Applied to healthcare, it will use specific health data of a population (or of a particular individual) and potentially help to prevent epidemics, cure disease, cut down costs, etc. Now that we live longer, treatment models have changed and many of these changes are namely driven by data. Doctors want to understand as much as they can about a patient and as early in their life as possible, to pick up warning signs of serious illness as they arise – treating any disease at an early stage is far more simple and less expensive. With healthcare data analytics, prevention is better than cure and managing to draw a comprehensive picture of a patient will let insurances provide a tailored package. This is the industry’s attempt to tackle the siloes problems a patient’s data has: everywhere are collected bits and bites of it and archived in hospitals, clinics, surgeries, etc., with the impossibility to communicate properly. Indeed, for years gathering huge amounts of data for medical use has been costly and time-consuming. With today’s always-improving technologies, it becomes easier not only to collect such data but also to convert it into relevant critical insights that can then be used to provide better care. This is the purpose of healthcare data analytics: using data-driven findings to predict and solve a problem before it is too late, but also assess methods and treatments faster, keep better track of inventory, involve patients more in their own health and empower them with the tools to do so. Why We Need Big Data Analytics In Healthcare There’s a huge need for big data in healthcare as well, due to rising costs in nations like the United States. As a McKinsey report states, “After more than 20 years of steady increases, healthcare expenses now represent 17.6 percent of GDP —nearly $600 billion more than the expected benchmark for a nation of the United States’s size and wealth.” In other words, costs are much higher than they should be, and they have been rising for the past 20 years. Clearly, we are in need of some smart, data-driven thinking in this area. And current incentives are changing as well: many insurance companies are switching from fee-for-service plans (which reward using expensive and sometimes unnecessary treatments and treating large amounts of patients quickly) to plans that prioritize patient outcomes As the authors of the popular Freakonomics books have argued, financial incentives matter – and incentives that prioritize patients health over treating large amounts of patients are a good thing. Why does this matter? Well, in the previous scheme, healthcare providers had no direct incentive to share patient information with one another, which had made it harder to utilize the power of analytics. Now that more of them are getting paid based on patient outcomes, they have a financial incentive to share data that can be used to improve the lives of patients while cutting costs for insurance companies. Finally, physician decisions are becoming more and more evidence-based, meaning that they rely on large swathes of research and clinical data as opposed to solely their schooling and professional opinion. As in many other industries, data gathering and management is getting bigger, and professionals need help in the matter. This new treatment attitude means there is a greater demand for big data analytics in healthcare facilities than ever before, and the rise of SaaS BI tools is also answering that need. Obstacles To A Widespread Big Data Healthcare One of the biggest hurdles standing in the way to use big data in medicine is how medical data is spread across many sources governed by different states, hospitals, and administrative departments. Integration of these data sources would require developing a new infrastructure where all data providers collaborate with each other. Equally important is implementing new online reporting software and business intelligence strategy. Healthcare needs to catch up with other industries that have already moved from standard regression-based methods to more future-oriented like predictive analytics, machine learning, and graph analytics. However, there are some glorious instances where it doesn’t lag behind, such as EHRs (especially in the US.) So, even if these services are not your cup of tea, you are a potential patient, and so you should care about new healthcare analytics applications. Besides, it’s good to take a look around sometimes and see how other industries cope with it. They can inspire you to adapt and adopt some good ideas. 12 Big Data Applications In Healthcare 1) Patients Predictions For An Improved Staffing For our first example of big data in healthcare, we will look at one classic problem that any shift manager faces: how many people do I put on staff at any given time period? If you put on too many workers, you run the risk of having unnecessary labor costs add up. Too few workers, you can have poor customer service outcomes – which can be fatal for patients in that industry. Big data is helping to solve this problem, at least at a few hospitals in Paris. A Forbes article details how four hospitals which are part of the Assistance Publique-Hôpitaux de Paris have been using data from a variety of sources to come up with daily and hourly predictions of how many patients are expected to be at each hospital. One of they key data sets is 10 years’ worth of hospital admissions records, which data scientists crunched using “time series analysis” techniques. These analyses allowed the researchers to see relevant patterns in admission rates. Then, they could use machine learning to find the most accurate algorithms that predicted future admissions trends. Summing up the product of all this work, Forbes states: “The result is a web browser-based interface designed to be used by doctors, nurses and hospital administration staff – untrained in data science – to forecast visit and admission rates for the next 15 days. Extra staff can be drafted in when high numbers of visitors are expected, leading to reduced waiting times for patients and better quality of care.” 2) Electronic Health Records (EHRs) It’s the most widespread application of big data in medicine. Every patient has his own digital record which includes demographics, medical history, allergies, laboratory test results etc. Records are shared via secure information systems and are available for providers from both public and private sector. Every record is comprised of one modifiable file, which means that doctors can implement changes over time with no paperwork and no danger of data replication. EHRs can also trigger warnings and reminders when a patient should get a new lab test or track prescriptions to see if a patient has been following doctors’ orders. Although EHR are a great idea, many countries still struggle to fully implement them. U.S. has made a major leap with 94% of hospitals adopting EHRs according to this HITECH research, but the EU still lags behind. However, an ambitious directive drafted by European Commission is supposed to change it: by 2020 centralized European health record system should become a reality. Kaiser Permanente is leading the way in the U.S., and could provide a model for the EU to follow. They’ve fully implemented a system called Health Connect that shares data across all of their facilities and makes it easier to use EHRs. A McKinsey report on big data healthcare states that “The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.” 3) Real-Time Alerting Other examples of big data analytics in healthcare share one crucial functionality – real-time alerting. In hospitals, Clinical Decision Support (CDS) software analyzes medical data on the spot, providing health practitioners with advice as they make prescriptive decisions. However, doctors want patients to stay away from hospitals to avoid costly in-house treatments. Analytics, already trending as one of the business intelligence buzzwords in 2019, has the potential to become part of a new strategy. Wearables will collect patients’ health data continuously and send this data to the cloud. Additionally, this information will be accessed to the database on the state of health of the general public, which will allow doctors to compare this data in socioeconomic context and modify the delivery strategies accordingly. Institutions and care managers will use sophisticated tools to monitor this massive data stream and react every time the results will be disturbing. For example, if a patient’s blood pressure increases alarmingly, the system will send an alert in real time to the doctor who will then take action to reach the patient and administer measures to lower the pressure. Another example is that of Asthma polis, which has started to use inhalers with GPS-enabled trackers in order to identify asthma trends both on an individual level and looking at larger populations. This data is being used in conjunction with data from the CDC in order to develop better treatment plans for asthmatics. 4) Enhancing Patient Engagement Many consumers – and hence, potential patients – already have an interest in smart devices that record every step they take, their heart rates, sleeping habits, etc., on a permanent basis. All this vital information can be coupled with other trackable data to identify potential health risks lurking. A chronic insomnia and an elevated heart rate can signal a risk for future heart disease for instance. Patients are directly involved in the monitoring of their own health, and incentives from health insurances can push them to lead a healthy lifestyle (e.g.: giving money back to people using smart watches). Another way to do so comes with new wearables under development, tracking specific health trends and relaying them to the cloud where physicians can monitor them. Patients suffering from asthma or blood pressure could benefit from it, and become a bit more independent and reduce unnecessary visits to the doctor. 5) Prevent Opioid Abuse In The US Our fourth example of big data healthcare is tackling a serious problem in the US. Here’s a sobering fact: as of this year, overdoses from misused opioids have caused more accidental deaths in the U.S. than road accidents, which were previously the most common cause of accidental death. Analytics expert Bernard Marr writes about the problem in a Forbes article. The situation has gotten so dire that Canada has declared opioid abuse to be a “national health crisis,” and President Obama earmarked $1.1 billion dollars for developing solutions to the issue while he was in office. Once again, an application of big data analytics in healthcare might be the answer everyone is looking for: data scientists at Blue Cross Blue Shield have started working with analytics experts at Fuzzy Logix to tackle the problem. Using years of insurance and pharmacy data, Fuzzy Logix analysts have been able to identify 742 risk factors that predict with a high degree of accuracy whether someone is at risk for abusing opioids. As Blue Cross Blue Shield data scientist Brandon Cosley states in the Forbes piece: “It’s not like one thing – ‘he went to the doctor too much’ – is predictive … it’s like ‘well you hit a threshold of going to the doctor and you have certain types of conditions and you go to more than one doctor and live in a certain zip code…’ Those things add up.” To be fair, reaching out to people identified as “high risk” and preventing them from developing a drug issue is a delicate undertaking. However, this project still offers a lot of hope towards mitigating an issue which is destroying the lives of many people and costing the system a lot of money. 6) Using Health Data For Informed Strategic Planning The use of big data in healthcare allows for strategic planning thanks to better insights into people’s motivations. Care mangers can analyze check-up results among people in different demographic groups and identify what factors discourage people from taking up treatment. University of Florida made use of Google Maps and free public health data to prepare heat maps targeted at multiple issues, such as population growth and chronic diseases. Subsequently, academics compared this data with the availability of medical services in most heated areas. The insights gleaned from this allowed them to review their delivery strategy and add more care units to most problematic areas. 7) Big Data Might Just Cure Cancer Another interesting example of the use of big data in healthcare is the Cancer Moonshot program. Before the end of his second term, President Obama came up with this program that had the goal of accomplishing 10 years’ worth of progress towards curing cancer in half that time. Medical researchers can use large amounts of data on treatment plans and recovery rates of cancer patients in order to find trends and treatments that have the highest rates of success in the real world. For example, researchers can examine tumor samples in biobanks that are linked up with patient treatment records. Using this data, researchers can see things like how certain mutations and cancer proteins interact with different treatments and find trends that will lead to better patient outcomes. This data can also lead to unexpected benefits, such as finding that Desipramine, which is an anti-depressant, has the ability to help cure certain types of lung cancer. However, in order to make these kinds of insights more available, patient databases from different institutions such as hospitals, universities, and nonprofits need to be linked up. Then, for example, researchers could access patient biopsy reports from other institutions. Another potential use case would be genetically sequencing cancer tissue samples from clinical trial patients and making these data available to the wider cancer database. But, there are a lot of obstacles in the way, including: • Incompatible data systems. This is perhaps the biggest technical challenge, as making these data sets able to interface with each other is quite a feat. • Patient confidentiality issues. There are differing laws state by state which govern what patient information can be released with or without consent, and all of these would have to be navigated. • Simply put, institutions which have put a lot of time and money into developing their own cancer dataset may not be eager to share with others, even though it could lead to a cure much more quickly. However, as an article by Fast Company states, there are precedents to navigating these types of problems: “…the U.S. National Institutes of Health (NIH) has hooked up with a half-dozen hospitals and universities to form the Undiagnosed Disease Network, which pools data on super-rare conditions (like those with just a half-dozen sufferers), for which every patient record is a treasure to researchers.” Hopefully, Obama’s panel will be able to navigate the many roadblocks in the way and accelerate progress towards curing cancer using the strength of data analytics. 8) Predictive Analytics In Healthcare We have already recognized predictive analytics as one of the biggest business intelligence trend two years in a row, but the potential applications reach far beyond business and much further in the future. Optum Labs, an US research collaborative, has collected EHRs of over 30 million patients to create a database for predictive analytics tools that will improve the delivery of care. The goal of healthcare business intelligence is to help doctors make data-driven decisions within seconds and improve patients’ treatment. This is particularly useful in case of patients with complex medical histories, suffering from multiple conditions. New tools would also be able to predict, for example, who is at risk of diabetes, and thereby be advised to make use of additional screenings or weight management. 9) Reduce Fraud And Enhance Security Some studies have shown that this particular industry is 200% more likely to experience data breaches than any other industry. The reason is simple: personal data is extremely valuable and profitable on the black markets. And any breach would have dramatic consequences. With that in mind, many organizations started to use analytics to help prevent security threats by identifying changes in network traffic, or any other behavior that reflects a cyber-attack. Of course, big data has inherent security issues and many think that using it will make the organizations more vulnerable than they already are. But advances in security such as encryption technology, firewalls, anti-virus software, etc, answer that need for more security, and the benefits brought largely overtake the risks. Likewise, it can help prevent fraud and inaccurate claims in a systemic, repeatable way. Analytics help streamline the processing of insurance claims, enabling patients to get better returns on their claims and caregivers are paid faster. For instance, the Centers for Medicare and Medicaid Services said they saved over $210.7 million in frauds in just a year. 10) Telemedicine Telemedicine has been present on the market for over 40 years, but only today, with the arrival of online video conferences, smartphones, wireless devices, and wearables, has it been able to come into full bloom. The term refers to delivery of remote clinical services using technology. It is used for primary consultations and initial diagnosis, remote patient monitoring, and medical education for health professionals. Some more specific uses include telesurgery – doctors can perform operations with the use of robots and high-speed real-time data delivery without physically being in the same location with a patient. Clinicians use telemedicine to provide personalized treatment plans and prevent hospitalization or re-admission. Such use of healthcare data analytics can be linked to the use of predictive analytics as seen previously. It allows clinicians to predict acute medical events in advance and prevent deterioration of patient’s conditions. By keeping patients away from hospitals, telemedicine helps to reduce costs and improve the quality of service. Patients can avoid waiting lines and doctors don’t waste time for unnecessary consultations and paperwork. Telemedicine also improves the availability of care as patients’ state can be monitored and consulted anywhere and anytime. 11) Integrating Big Data With Medical Imaging Medical imaging is vital and each year in the US about 600 million imaging procedures are performed. Analyzing and storing manually these images is expensive both in terms of time and money, as radiologists need to examine each image individually, while hospitals need to store them for several years. Medical imaging provider Care stream explains how big data analytics for healthcare could change the way images are read: algorithms developed analyzing hundreds of thousands of images could identify specific patterns in the pixels and convert it into a number to help the physician with the diagnosis. They even go further, saying that it could be possible that radiologists will no longer need to look at the images, but instead analyze the outcomes of the algorithms that will inevitably study and remember more images than they could in a lifetime. This would undoubtedly impact the role of radiologists, their education and required skill set. 12) A Way To Prevent Unnecessary ER Visits Saving time, money and energy using big data analytics for healthcare is necessary. What if we told you that over the course of 3 years, one woman visited the ER more than 900 times? That situation is a reality in Oakland, California, where a woman who suffers from mental illness and substance abuse went to a variety of local hospitals on an almost daily basis. This woman’s issues were exacerbated by the lack of shared medical records between local emergency rooms, increasing the cost to taxpayers and hospitals, and making it harder for this woman to get good care. As Tracy Schrider, who coordinates the care management program at Alta Bates Summit Medical Center in Oakland stated in a Kaiser Health News article: “Everybody meant well. But she was being referred to three different substance abuse clinics and two different mental health clinics, and she had two case management workers both working on housing. It was not only bad for the patient; it was also a waste of precious resources for both hospitals.” In order to prevent future situations like this from happening, Alameda county hospitals came together to create a program called PreManage ED, which shares patient records between emergency departments. This system lets ER staff know things like: • If the patient they are treating has already had certain tests done at other hospitals, and what the results of those tests are • If the patient in question already has a case manager at another hospital, preventing unnecessary assignments • What advice has already been given to the patient, so that a coherent message to the patient can be maintained by providers This is another great example where the application of healthcare analytics is useful and needed. In the past, hospitals without PreManage ED would repeat tests over and over, and even if they could see that a test had been done at another hospital, they would have to go old school and request or send a long fax just to get the information they needed. How To Use Big Data In Healthcare All in all, we’ve seen through these 12 examples of big data application in healthcare three main trends: the patients experience could improve dramatically, including quality of treatment and satisfaction; the overall health of the population should also be improved over time; and the general costs should be reduced. Let’s have a look now at a concrete example of how to use data analytics in healthcare, in a hospital for instance: This healthcare dashboard provides you with the overview needed as a hospital director or as a facility manager. Gathering in one central point all the data on every division of the hospital, the attendance, its nature, the costs incurred, etc., you have the big picture of your facility, which will be of a great help to run it smoothly. You can see here the most important metrics concerning various aspects: the number of patients that were welcomed in your facility, how long they stayed and where, how much it cost to treat them, and the average waiting time in emergency rooms. Such a holistic view helps top-management identify potential bottlenecks, spot trends and patterns over time, and in general assess the situation. This is key in order to make better-informed decisions that will improve the overall operations performance, with the goal of treating patients better and having the right staffing resources. Our List of 12 Big Data Examples In Healthcare The industry is changing, and like any other, big data is starting to transform it – but there is still a lot of work to be done. The sector slowly adopts the new technologies that will push it into the future, helping it to make better-informed decisions, improving operations, etc. In a nutshell, here’s a short list of the examples we have gone over in this article. With healthcare data analytics, you can: • Predict the daily patients income to tailor staffing accordingly • Use Electronic Health Records (EHRs) • Use real-time alerting for instant care • Help in preventing opioid abuse in the US • Enhance patient engagement in their own health • Use health data for a better-informed strategic planning • Research more extensively to cure cancer • Use predictive analytics • Reduce fraud and enhance data security • Practice telemedicine • Integrate medical imaging for an broader diagnosis • Prevent unnecessary ER visits These 12 examples of big data in healthcare prove that the development of medical applications of data should be the apple in the eye of data science, as they have the potential to save money and most importantly, people’s lives. Already today it allows for early identification of illnesses of individual patients and socioeconomic groups and taking preventive actions because, as we all know, prevention is better than cure. To onboard the data analytics train and start building your own healthcare reports, you should give our 14-day free trial a go! #1. To find exactly what we look for in the internet Maybe you have never thought that Google, Yahoo, Yandex, Bing and other search engines work with big data when they pick results in response to your search queries, but in fact they do. Search engines need to cope with trillions of network objects and analyze online behavior of billions of people to understand what exactly they are looking for. It’s only natural that these giants became pioneers of data analysis in many spheres and produce numerous big data related products. #2. To ride through a city without traffic jams For example, when Yandex Company sharpened its skills in data analysis, they decided to look at their data from another perspective. That’s how Yandex.Traffic solution was born. This technique analyzes information from different sources and shows a map of real time traffic conditions in a city. It’s an amazing solution for large cities, where traffic jams become a real pain in the ass. Have you ever been in Moscow? A heartfelt advice: if you are going to, be sure to give Yandex.Traffic a try, as even at this very moment it helps millions of Moscow drivers. #3. To save rare animals, catching poachers Poachers hunt for endangered Indian tigers to make medicines from their bones that are very popular among superstitious Chinese. They know every nook and cranny in the tigers habitat area and it would be very hard to catch them without… big data. #4. To make our cities green New York City had been rather dangerous because of old trees that had been falling on citizens heads and property until the authorities found the solutions. Now, big data tells them how to maintain the ‘city forest.’ #5. To understand why Indian cuisine is unique Scientists mined into a bunch of recipes and found out that food-pairing hypothesis works well for any cuisine in the world — except Indian one. #6. To fight malaria epidemics in Africa A great project sponsored by Google uses big data technology to solve a global health problem. Many Africans do have a mobile phone even in remote locales. They can text data about what medications they’re taking to let scientists track the spread and treatments of the disease. #7. To grow ideal Christmas trees Scientists will connect genetic, physical, and environmental data from more than 15 major plant databases to create tools for growing better crops, plants and ideal Christmas trees. #8. To understand that our languages are filled with happiness As it turns out, world languages contain more positive words than negative and are predisposed to happiness. #BigData finds human languages exhibit a clear positive bias – — DaveO (@storagesport) February 10, 2015 #9. To make sport shows even more interesting Elite sport coaches use big data to develop strategies, training and eating programs, and even fan interaction in the chase for better performance on the field. How #BigData is changing #basketball: – helps coaches determine how players perform. — Antivia (@Antivia) March 29, 2015 #10. To improve job conditions Bosses know everything. Or at least they’ll know if that employee is going to quit — big data will tell them and advise how to improve in job conditions to keep employees. #11. To enhance relationship The last but not least comes a special case that was recently mentioned by media. Data analysis can be used to solve global problems as well as very intimate ones. Be sure to read the story of an online-dating data analyst who decided to examine her own relationships in terms of statistics. Did you know that 90% of stored big data is dead weight? So called Dark Data are bits and pieces of data that seem useful and take a decent place in your storage, but in general you fail to use day to day. This is good news, as this fact shows great potential of data mining and analyses. The dark data wait for a curious mind to bend it. So if you are thinking where to send your child to study, think about this opportunity. Serious, funny and even surprising cases of #Big Data use for numerous purposes. Enjoy! #12 Tweet That’s it for today. Next week we are going to publish another post about big data projects. More specifically, about big data helping to save lives and catch criminals. Stay tuned! sTUDY AND RESEARCH BY: : MUKESH KUMAR ,CHAIRMAN,IT & ITes SUB COMMITTEE ( BIA) MOBILE NO. 9431074202,E-MAIL: [email protected],

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