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Simplify our analytics strategy

  1. Simplify Your Analytics Strategy
  2. 1Challenges 2 Accelerate The Data 3Next-gen Bi And DATA Visualization 4 Data Discovery 5 Analytics Applications 6Machine Learning and Cognitive Computing OUTLINE
  3. Companies are facingchallenges…. While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate. Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing
  4. Pursue a Simplerpath To overcome this, companies should pursue a simpler path to uncovering the insight in their data and making insight-driven decisions that add value.
  5. Accelerate the DATA…. Fast Data Fast Insights Fast Outcomes
  6. Accelerate the DATA…. Liberate and accelerate data by creating a data supply chain built on a hybrid technology environment — a data service platform combined with emerging big data technologies. Real-time delivery of analytics speeds up the execution velocity and improves the service quality of an organization.
  7. An Example: A U.S. bank adopted such a technology environment to more efficiently manage increasing data volumes for its customer analytics projects. As a result, the firm experienced improved processing time by several hours, generating quicker insights and a faster reaction time.
  8. Waysto delegate the work to your  Delegate the work to your analytics technologies. Uncovering data insights doesn’t have to be difficult.  Next-Gen Business Intelligence (BI) and data visualization is extensively useful in delegating work to your analytics technologies.
  9. Next-Gen BIand datavisualization At its core, next-gen business intelligence is bringing data and analytics to life to help companies improve and optimize their decision- making and organizational performance. by turning anBI does this organization’s data into an asset by and displaying in the right visual form (heat map, charts, etc) for each individual decision-maker, so they can use it to reach their desired outcome.
  10. An Example: A financial services company applied BI and data visualization to see the different buckets of risk across its entire loan portfolio. The firm identified the areas in the U.S. where there were high delinquency rates, explored tranches based on lenders, loan purposes, and loan channels, and viewed bank loan portfolios. Users were also able to interact with the results and query the data based on theirneeds.
  11. Data discovery Through the use of data discovery techniques, companies can test and play with their data to uncover data patterns that aren’t clearly evident. When more insights and patterns are discovered, more opportunities to drive value for the business can be found.
  12. An Example: Aresources company was able to predict which pipelines are most risky atypical discovery from both physical and threats through data techniques. Due to the insights gained, the firm was able to prioritize where they should invest funds for counter- failure measures and maintenance repairs.
  13. AnalyticsApplications: Applications can simplify advanced analytics as they put the power of analytics easily and elegantly into the hands of the business user to make data-driven business decisions. They can also be industry-specific, flexible, and tailored to meet the needs of the individual users across organizations — from marketing to finance, and levels from C-suite to middle management.
  14. An Example: An advanced analytics app can help a store manager optimize his inventory and a CMO could use an app to optimize the company’s global marketing spend.
  15. Machine Learning & CognitiveComputing
  16. Machine Learning & CognitiveComputing With an influx of big data, and advances in processing power, data cognitive software science and technology, intelligence is helping machines make even better-informed decisions.
  17. Each path to Insight isunique…. Recognize that each path to data insight is unique. The path to insight doesn’t come in one single form. There are many different elements in play, and they are always changing — business goals, technologies, data types, data sources, and then some are in a state of flux.
  18. TwoApproaches:  First-  For a known problem with a known solution — such as customer segmentation and propensity modeling for targeted marketing campaigns  — the company could take a hypothesis-based approach by starting with the outcome Second- For a known problem area, fraud for example, but with an unknown solution, the company could take a discovery-based approach to look for patterns in the data to find interesting correlations that may be predictive
  19. Name – Saurabh Sethia Mail id- saurabhsethia12@g