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Data Strategy - Executive MBA Class, IE Business School

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For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.

Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.

This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.

Published in: Data & Analytics
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Data Strategy - Executive MBA Class, IE Business School

  1. 1. 1 HIGHLIGHTS FROM THE IE BUSINESS SCHOOL CLASS Data Strategy March 2019 Gam Dias, First Retail
  2. 2. IE Digital Transformation Program: Data Strategy Module Part 1: Achieving Common Understanding of Data Strategy Part 2: Examples of Data Strategy Across Different Industries Part 3: Co-Creation of a Data Strategy
  3. 3. How does your organization use data? Reporting History Trends & Anomalies Forecasts Predictions As part of the product As the product
  4. 4. Data changes everything “Our future competition will likely not be another mining company, and they will compete with us by making better use of data” Scott Singer Head of Global Business Services
  5. 5. Business and Data OPERATIONS: RUNNING THE BUSINESS AT A TRANSACTIONAL LEVEL MANAGEMENT: MONITORING PERFORMANCE, TACTICAL INTERVENTIONS STRATEGY: PERFORMANCE IMPROVEMENTS, OPTIMIZATIONS, BUDGETING, COURSE CORRECTION TRANSFORMATION: NEW LINES OF BUSINESS, NEW MARKETS, NEW PRODUCTS TRANSACTIONAL APPLICATIONS AUTOMATE PROCESSES AND COLLECT DATA BUSINESS PERFORMANCE MANAGEMENT M.I.S REPORTS, ANALYTICS, DATA WAREHOUSES, DASHBOARDS BUSINESS PERFORMANCE STRATEGY: DATA QUALITY, GOVERNANCE, KPI DEVELOPMENT, BALANCED SCORECARDS STRATEGIC USE OF DATA: DATA PRODUCT DEFINITION, DATA DRIVEN PROCESS TRANSFORMATIONS, DATA VALUE CHAIN EXTENSIONS WHAT THE BUSINESS IS DOING HOW DATA SUPPORTS THE BUSINESS
  6. 6. Where is our data?
  7. 7. Digital Transformation What does it mean to you?
  8. 8. Digital Transformation “Adapting business models to be effective in a world that is growing richer in data” Gam Dias
  9. 9. The Strategic and Tactical Application of Data
  10. 10. What is your definition of Data Strategy?
  11. 11. Googling Data Strategy
  12. 12. What is Data Strategy? In 2015 I asked Quora. The answers had the following 3 themes: 1. The management of data to generate business value, control I.T. costs and ensure compliance 2. To ensure that data is proactively managed to create the best platform for analytics and data science 3. The technical management of the complete data lifecycle to maximize availability to the business processes https://www.quora.com/What-is-Data-Strategy BUSINESS FOCUS TECHNOLOGY FOCUS
  13. 13. From HBR
  14. 14. Data Strategy “A strategic plan for treating data as a corporate asset” Gam Dias
  15. 15. Is data a strategic asset? Assets that are needed by an entity in order for it to maintain its ability to achieve future outcomes. Without such assets the future well- being of the company could be in jeopardy.
  16. 16. Simplified Approach to Data Strategy 1. Baseline 2. Opportunities 3. Data Sourcing 4. Data Preparation 5. Analytics Enablement 6. Socializing 7. Feedback Loops 8. Governance 9. Transformation
  17. 17. Data strategy stories from the field
  18. 18. General Approach DATA STRATEGY REPORT PRIORITIZED ROADMAP EXECUTIVE PRESENTATION BUSINESS STAKEHOLDER INPUTS SENIOR EXECUTIVE INPUTS TECHNOLOGY EXECUTIVE INPUTS GATHERING FACTS AND FIGURES ANALYSIS
  19. 19. General Insights from our Data Strategy Practitioners • Business stakeholders must own and sponsor individual projects from inception to adoption into the business process • Obtaining data feeds is always more difficult than anybody expects, so start this process as early as possible • Projects should be short-cycle to deliver business value within a 4-6 week timeframe, create iterative cycles wherever possible for larger scoped projects • Data science and analytics is a business function supported by M.I.S., not the other way around • Data politics must always be a consideration – certain analytics render processes and business problems transparent, this may cause resistance or obstacles in certain cases
  20. 20. Client’s Executive Insights: Mining Company • Follow the company’s ‘Value Chain’ – different stages require different tactics, for a mine this is: Exploration: huge CAPEX, so the analysis will determine the life of a mine, use of AI to determine predictions, crowd-source the analysis Build: making sure that the mine has a complete digital twin Operate: Importance of technology to democratize the analysis, this was better given to the business rather than a centralized IT. • When operationalizing any data in the organization, there is a parallel journey where the organization needs to mature in its acceptance and use of data • We come at these problems from a 1’s and 0’s perspective, yet mining is a dusty physical business. Dust gets everywhere and renders the data invalid. Keep a foot in the real world • We are dealing with multi-variate, multi-process data across organizations, a ‘trader mentality’ can see through the fog
  21. 21. Client’s Executive Insights: TV Network • We developed a better method for determining television viewing by leveraging the rich data being collected by the company’s own set top boxes • We were able to prove that this was a more accurate metric than the global industry standard TV viewing panel • Yet, the job of the advertising sales team was to sell to advertisers, a group that relied on the industry standard metric as they bought ads’ on other TV networks • Despite being more accurate, as the incumbent provider had the market sewn up, it was impossible for one company with better technology to penetrate • Since the project was completed, the global industry standard provider has gone on to acquire set top box data and has developed similar products to the model we built
  22. 22. Client’s Executive Insights: Invoice Automation Startup • In AI, maintaining your competitive advantage is hard, ML models will allow you to increase cost efficiency but there is a limit • Building AI models may be very interesting, but what really matters is having better data than the competition • Use the machine learning models to keep acquiring more data • And start using that data to provide more insights to your clients • For combination of data, determine the value of that data to the client’s business REF: https://www.kdnuggets.com/2019/01/your-ai-skills-worth-less-than-you-think.html
  23. 23. Data Strategy Co-Creation Class Exercises
  24. 24. Live Workshop Exercises #1: Opportunities in your own organizations What opportunities do you see for leveraging data better inside your own organization? How will your organization benefit if you could act on those opportunities? #2: Obstacles to realizing those opportunities What are the obstacles that you know of or expect to making something happen? Do you understand why those obstacles exist? Are they legacy thinking or are they infeasible because of other reasons? #3: Alternative paths around, over or under the obstacles If they are immovable obstacles, how can we find a creative solution to overcome or to turn the obstacle into an advantage? #4: Inter-company Data Sharing If you could share data with another external organization, what data would you share and what would you or they do with it that would be beneficial?
  25. 25. Reading Recommendations
  26. 26. 26 info@firstretail.com Thank You

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