1. What is Big Data? Why Is Big Data Different? (from data mart, data warehouse) 2. What Are the...

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General Management

1.    What is Big Data? Why Is Big DataDifferent? (from data mart, data warehouse)
2.    What Are the Benefits of Big Data?
3.    Some of the potential business benefitsfrom implementing an effective big data analytics
4.    How can organization leverage Big Data?For example, Big Data can be used to develop the nextgeneration of products and services. For instance,manufacturers are using data obtained from sensors embedded inproducts to create innovative after-sales service offerings such asproactive maintenance to avoid failures in new products.
5.    Traditionally, factories estimate that acertain type of equipment is likely to wear out after so manyyears. Consequently, they replace every piece of that technologywithin that many years, even devices that have much more usefullife left in them. Big Data tools do away with such unpractical andcostly averages. The massive amounts of data that they access anduse and their unequalled speed can spot failing grid devices andpredict when they will give out. The result: a much morecost-effective replacement strategy for the utility and lessdowntime, as faulty devices are tracked a lot faster.
6.    What can you say about how aviationcompanies are utilizing Big Data – check your reading of IBM whitepaper and how OEMs are transforming the aviation industry.

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1ANSWER A Definition of Big Data SAS perfectly captures Big Data as a term that describes the large volume of data both structured and unstructured that inundates a business on a daytoday basis But as SAS points out the amount of data is not as important as what organizations do with it analyzing Big Data results in the insights you need to make better business decisions and strategic moves Lisa Arthur Teradata Applications CMO and Forbes contributor explains that Big Data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis She asserts that traditional data must be included in Big Data because it is an important piece of the Big Data picture Indeed incorporating data from all sources is key to optimizing the insights gained with Big Data In recent years there has been a boom in Big Data because of the growth of social mobile cloud and multimedia computing We now have unprecedented amounts of data and it is up to organizations to harness the data in order to extract useful actionable insights But because traditional systems cannot store process and analyze massive amounts of unstructured data organizations are turning to Big Data management solutions to turn unstructured data into the actionable data needed for gaining key insights into their business and customers Big data is different because of following reasons These days many people in the information technology world and in corporate boardrooms are talking about big data Many believe that for companies that get it right big data will be able to unleash new organizational capabilities and value But what does the term big data actually entail and how will the insights it yields differ from what managers might generate from traditional analytics There is no question that organizations are swimming in an expanding sea of data that is either too voluminous or too unstructured to be managed and analyzed through traditional means Among its burgeoning sources are the clickstream data from the Web social media content tweets blogs Facebook wall postings etc and video data from retail and other settings and from video entertainment But big data also encompasses everything from call center voice data to genomic and proteomic data from biological research and medicine Every day Google alone processes about 24 petabytes or 24000 terabytes of data Yet very little of the information is formatted in the traditional rows and columns of conventional databases Many IT vendors and solutions providers use the term big data as a buzzword for smarter more insightful data analysis But big data is really much more than that Indeed companies that learn to take advantage of big data will use realtime information from sensors radio frequency identification and other identifying devices to understand their business environments at a more granular level to create new products and services and to respond to changes in usage patterns as they occur In the life sciences such capabilities may pave the way to treatments and cures for threatening diseases Organizations that capitalize on big data stand apart from traditional data analysis environments in three key ways They pay attention to data flows as opposed to stocks They rely on data scientists and product and process developers rather than data analysts They are moving analytics away from the IT function and into core business operational and production functions 1 Paying attention to flows as opposed to stocks There are several types of big data applications The first type supports customerfacing processes to do things like identify fraud in real time or score medical patients for health risk A second type involves continuous process monitoring to detect such things as changes in consumer sentiment or the need for service on a jet engine Yet another type uses big data to explore network relationships like suggested friends on LinkedIn and Facebook In all these applications the data is not the stock in a data warehouse but a continuous flow This represents a substantial change from the past when data analysts performed multiple analyses to find meaning in a fixed supply of data Today rather than looking at data to assess what occurred in the past organizations need to think in terms of continuous flows and processes Streaming analytics allows you to process data during an event to improve the outcome notes Tom Deutsch program director for big data technologies and applied analytics at IBM This capability is becoming increasingly important in fields such as health care At Torontos Hospital for Sick Children for example machine learning algorithms are able to discover patterns that anticipate infections in premature babies before they occur The increased volume and velocity of data in production settings means that organizations will need to develop continuous processes for gathering analyzing and interpreting data The insights from these efforts can be linked with production applications and processes to enable continuous processing Although small stocks of data located in warehouses or data marts may continue to be useful for developing and refining the analytical models used on big data once the models have been developed they need to process continuing data streams quickly and accurately The behavior of credit card companies offers a good illustration of this dynamic In the past direct marketing groups at credit card companies created models to select the most likely customer prospects from a large data warehouse The process of data extraction preparation and analysis took weeks to prepare and weeks more to execute However credit card companies frustrated by their inability to act quickly determined that there was a much faster way to meet most of their requirements In fact they were able to create a readytomarket database and system that allows a marketer to analyze select and issue offers in a single day Through frequent iterations and monitoring of website and callcenter activities companies can make personalized offers in milliseconds then optimize the offers over time by tracking responses Some big data environments such as consumer sentiment analysis are not designed for automating decisions but are better suited for realtime monitoring of the environment Given the volume and velocity of big data conventional highcertitude approaches to decisionmaking are often not appropriate in such settings by the time the organization has the information it needs to make a decision new data is often available that renders the decision obsolete In realtime monitoring contexts organizations need to adopt a more continuous approach to analysis and decisionmaking based on a series of hunches and hypotheses Social media analytics for example capture fastbreaking trends on customer sentiments about products brands and companies Although companies might be interested in knowing whether an hours or a days changes in online sentiment correlate with sales changes by the time a traditional analysis is completed there would be a raft of new data to analyze Therefore in big data environments its important to analyze decide and act quickly and often However it isnt enough to be able to monitor a continuing stream of information You also have to be prepared to make decisions and take action Organizations need to establish processes for determining when specific decisions and actions are necessary when for example data values fall outside certain limits This helps to determine decision stakeholders decision processes and the criteria and timeframes for which decisions need to be made 2 Relying on data scientists and product and process developers as opposed to data analysts Although there has always been a need for analytical professionals to support the organizations analytical capabilities the requirements for support personnel are different with big data Because interacting with the data itself obtaining extracting manipulating and structuring it is critical to any analysis the people who work with big data need substantial and creative IT skills They also need to be close to products and processes within organizations which means they need to be See Answer

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