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Solving Data Problems: The Cost of Poor Data in Your Organization

Industry experts discuss the impact of unclean data on decision-making and the importance of clean data for advanced technologies like TPO and AI. They address the foundational nature of data, integration issues, the need for a different approach to data, types of data and analytics, key performance indicators (KPIs), building trust in data, and the benefits of improved data for predictive modeling and AI.

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Solving Data Problems: The Cost of Poor Data in Your Organization

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Presentation Transcript

  1. How to Solve for the Millions Your Poor Data is Costing Your Organization

  2. Industry Experts Rodger Fromm Senior Systems Analyst/ Architect Sequoya Mike Marzano Director Business Technology Sysco Corp Rahi Khandelwal Frmr Head of Global Enterprise Analytics Team, Del Monte Foods Gerard de Bruijn Global Excellence Director Head of US Practice Visualfabriq

  3. The “Clean” Truth • Unclean data leads to inaccurate tool output and analysis. Data is foundational to accuracy. • When data isn’t cleansed, harmonized and structured decisions are made using faulty assumptions, which negatively impact processes, opportunities & cost • Companies are moving to TPO and AI, and it’s critical that companies have clean data moving into these technologies.

  4. Q 1: What do each of you consider “foundational” data? & What is the cross-functional impact & financial implication when companies haven’t mastered the data?

  5. Q 2: What is “clean” data? & Organizations may have “silo’s” of clean data from various sources. Is lack of integration of that data causing downstream issues ?

  6. Q 3: Why do we need to look at the data differently today?

  7. Q 4: What type of data are you procuring today? & What type of analytics are you able to create with the data?

  8. Q 5: What are the KPI’s that your organization or clients are using today to drive increased accuracy in trade spend, Customer Product P&L, broker commissions & demand forecast etc. ?

  9. Q 6: How do you get users to trust the data, process and technology? & What change management steps do you take to gain buy-in from the users and drive adoption?

  10. Q 7: What is the benefit of improved data to predictive modeling, machine learning and AI? & Explain how machine learning and AI are deployed today

  11. Q&A

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