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Data Strategy


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Data Strategy

  1. 1. FOUDROYANT ANALYTICS Data Strategy Developing a Modern Enterprise Data Strategy Sai Venkatesh Attaluri by
  2. 2. Data Doldrums • Big Data • Data Science • Data Mining • Data Marting • Data Warehouse • Data Clusters • Data Integrity • Data Security  Data Bases  Relational  Structured Data  Unstructured Data  Semi Structured Data  ETL  Data Integration  Resources  Data Scientists  Data Engineers  Data Architects  Chief Data Officer  The list goes on.......
  3. 3. The Big Mystery • Big data and analytics have climbed to the top of the corporate agenda. • Together, they promise –To transform the way companies do business –Delivering the exceptional performance gains –Assuring competitive differentiation • But what is required to fully exploit data and analytics?????
  4. 4. Courtesy: Silicon Valley
  5. 5. Courtesy: Silicon Valley
  7. 7. 10 THINGS A DATA STRATEGY SHOULD INCLUDE Courtesy: Silicon Valley
  8. 8. Courtesy: Silicon Valley
  9. 9. 4 PRINCIPLES OF A SUCCESSFUL DATA STRATEGY • How does a Data Generate Value? • What are our Critical Data Assets? • What is our Data Ecosystem? • How do we Govern Data?
  10. 10. Philosophy • Prioritize for highest business value when using emerging technology. • Provide highly productive teams of data scientists and engineers. • Design with outcomes in mind. • Be agile: deliver initial results quickly, then adapt and iterate. • Collaborate constantly with cross functional teams.
  11. 11. Revealing The Mystery • It requires three mutually supportive capabilities 1.Identify, Combine & Manage Multiple Sources Of Data. 2.Capability to build Advanced Analytics Models for Predicting And Optimizing Outcomes 3.Management must possess the muscle to transform the organization so that the data and models actually yield better decisions.
  12. 12. MAKE SURE IT’S FLEXIBLE • Technology moves incredibly fast, and competitive landscapes are highly dynamic. • Your data strategy should be a living document, revisited often and revised as conditions change.
  13. 13. MAKE SURE IT’S ACTIONABLE • If it isn’t clear how you’re going to execute your strategy, then you don’t have the right one. • Must work within the realm of the possible and practical.
  14. 14. Business Value Addition Courtesy: Silicon Valley
  15. 15. Data Strategy Checklist Courtesy: Silicon Valley
  16. 16. Clear Strategy • A clear strategy for how to use data and analytics to compete. • What the data can do for you? • How it can yield business results to the organization? • Which deployment of the right technology and capabilities to be applied?
  17. 17. Choose The Right Data • Leaders should invest sufficient time and energy in aligning managers across the organization in support of the mission with a clear vision of the desired business impact –Data sourcing –Model Building for Panoramic and Granular View –Discovering the Invisible –Organizational Transformation.
  18. 18. Approach • Source Data Creatively • What decisions could we make if we had all the information we need? • What’s the least complex model that would improve our performance? • Get The Necessary IT Support by Build models that predict and optimize business outcomes – Business leaders can address short-term big-data needs by working with CIOs to prioritize requirements. – The most effective approach to building a model usually starts, not with the data, but with identifying a business opportunity and determining how the model can improve performance. – Although advanced statistical methods indisputably make for better models, statistics experts sometimes design models that are too complex to be practical and may exhaust most organizations
  19. 19. Approach • Transform your company’s capabilities – Manage Resistance – Resistance arises due to a mismatch between an organization’s existing culture and capabilities and emerging tactics to exploit analytics successfully. – Provide a clear blueprint for realizing business goals – Avoid Complex Tools designed for experts rather provide simpler ones for the frontlines. – Bottom line: using big data requires thoughtful organizational change, and three areas of action can get you there. • Develop business-relevant analytics that can be put to use – By necessity, terabytes of data and sophisticated modelling are required to sharpen marketing, risk management, and operations.
  20. 20. Turning Data into Useful Information
  21. 21. Transforming Information into Knowledge  Focus on a specific process as a starting point.  Create common business language across the enterprise.  Embed that language in tools, systems and processes to create new business capabilities and agility.  Create governance and change management programs to leverage these capabilities in day- to-day work processes.  Apply accepted practices to unstructured content processes to promote better information hygiene  Measure the information mgmt process.  Measure business impact of new practices.  Repeat on a department by department basis.
  22. 22. Approach • Embed analytics in simple tools for the front lines – Separate the statistics experts and software developers from the managers who use the data- driven insights. – Bottom line: To give frontline managers intuitive tools and interfaces that help them with their jobs. • Develop capabilities to exploit big data • Adjusting cultures and mind-sets typically requires a multifaceted approach that includes training, role modelling by leaders, and incentives and metrics to reinforce behaviour.
  23. 23. Conclusion • Executives should act now to implement big data and analytics. • Rather than undertaking massive change, executives should concentrate on targeted efforts to source data, build models, and transform the organizational culture. • Such efforts help maintain flexibility. That’s essential, since the information itself—along with the technology for managing and analyzing it—will continue to grow and change, yielding new opportunities.
  24. 24. Thank You