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Implementing bi in proof of concept techniques


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Implementing bi in proof of concept techniques

  1. 1. ABOUT THE AUTHORSMark Kromer is a senior product manager atMicrosoft specializing in database applications andbusiness intelligence. He manages productdevelopment for Microsoft Services BI solutionswithin the Industry Solutions GroupDaniel Yu joined Microsoft in 2006 as a productmanager and currently manages businessdevelopment for Microsoft Enterprise Cube BIsolutions within the Industry Solutions Group
  2. 2. Data collection requires enormous efforts that take considerable time THE PREMISEand specialists from many groups, including databaseadministrators, developers, business analysts, and data warehouseexperts the business units responsible for driving business performanceHowever,and lines of business, in most instances , fail to realize the value of thisdata Between 70% to 80% ofThough the technology and software that make business intelligence corporate businesspossible have existed for decades, the enterprise wide adoption (andbenefit) of business intelligence has yet to materialize intelligence projectsThe problem is compounded when different departments make fail, according to researchconflicting financial or marketing decisions that may be confusing to by analyst firm Gartnercustomers A combination of poor communication between IT and the business, the failure Communication to ask the right questions or to think about the real needs of the business IT departments make the mistake of looking at BI as BI viewed as an engineering problem that an IT issue requires a specific package solution ROI Its better to spread that investment over a broader number of users, raising the*Source: ROI for each user. only on analytical users expensive and wasteful.
  3. 3. THE BASICS The databases store IT departments usually keep A cube aggregates the facts information about business OLAP databases isolated in each level of each transactions, plus other from OLTP databases. dimension in a given OLAP data such as employee OLAP databases use fewer schema records tables and a different type Because the cube contains These databases of schema. In addition, they all of your data in an OLAP OLTP CUBES execute transactions, meani usually keep the number of aggregated form, it seems ng that they add, update, or joins to a minimum by to know the answers in delete groups of records at arranging tables a star advance the same time schema Speed: the largest cube in Data extraction difficult The joins between the the world is currently 1.4 because: dimension and fact tables terabytes and its average -OLTP databases contain a allow you to browse response time to any query large number of tables through the facts across any is 1.2 seconds number of dimensions, as Users can view cube data -OLTP databases constantly well as up and down any update their data with any valid number of hierarchies tool, including -OLTP databases usually OLAP databases make spreadsheets, Web store individual records heavy use of indexes pages, the Cube Browser in because they help find Analysis Services 2000, or records in less time graphic data browsers such as Microsoft Data Analyzer ETL: There needs to be a way to move the data to the OLAP database, combine that data into useful aggregations, and then populate the tables. That process is often called Extract, Transform, and Load (ETL) SQL Server has a built-in utility called Data Transformation Services (DTS) that performs the ETL tasks.*Source:
  4. 4. METHODOLOGY A proof of concept (POC) is a demonstration whose purpose is to verify that certain concepts or theories have the potential for real-world application. POC is therefore a prototype that is designed to determine feasibility, but does not represent deliverables In the field of business development and sales, a vendor may allow a prospect customer to trial the product. This use of proof of concept helps establish viability, technical issues, and overall direction, as well as providing feedback for budgeting and other forms of internal decision making processes* The resulting spreadsheet serves as This phase leads to the catalyst to present data gathering, data the business value to the modeling, and project sponsors and Proof of concept: A executives small scale data producing the results mart and analysis through an OLAP A pre-implementation engine into an Excel phase of such projects cube is created based on this data spreadsheet is scoped for gathering sample data from data sources required for a customer segmentation solution*Source:wikipedia
  5. 5. CASE: CUSTOMER SEGMENTATION PROJECTInitial phase• Identified the needed resources and key stakeholders• Spent four weeks gathering and analyzing their campaign data and purchase logs The top 1 percent of base subscribers Findings of proof of concept technique accounted for 43 percent of all revenue. By implementing a segmentation business intelligence solution for their marketing organization, the company could achieve a return on its marketing investment of 110 percent Revenue champions: Distribution curve of customer revenue—when segmented properly—is not a normal one, but rather very skewed toward a few selected segments. The purchasing patterns of these top-line customers were different from regular users: they bought products and services at different times during the day and were influential to the overall product’s success among regular users Comparative study: Without the revenue champions, company would have certainly had a low ROI
  6. 6. CASE: CUSTOMER SEGMENTATION PROJECTSegmented Marketing• Sent the same product offering to two sample groups of 20000 customers each• Observation for 2 weeks• After setting specific goals for the campaign, we separated the expected contribution from the overall marketing campaign• Used standard data mining techniques, and ran hundreds of Monte Carlo simulations and simulation optimizations and produced a set of actionable marketing activities that would help the service provider target the right customer at the right time through the most effective channel BI suites used:
  7. 7. CASE: CUSTOMER PROFITABILITY PROJECT Because of the company’s strong base After extracting the data from the existing data subscriber growth, the enterprise did not put a warehouse, they were able to cleanse and format priority on discovering insights into which the data into a small data mart and single cube customer accounts were most profitable, or which was used for processing which accounts were too expensive to aggregations, dimensional hierarchies, and data maintain based on service plans and handsets summaries of customer profitability metrics Methodology used by subscribers. including average revenue per user (ARPU), average revenue by handset, revenue by geography etc Lack of planning resulted in lost revenue because some customers had more operating Utilized the capabilities of the BI presentationContext costs than revenue due to factors including software to export the results of the trial directly roaming and old handsets. from SharePoint graphs in ProClarity to PowerPoint presentations
  8. 8. Thank You !!