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

Industry Experts Rodger Fromm Mike Marzano Rahi Khandelwal Senior Systems Analyst/ Architect Sequoya Mike Marzano Director Business Technology Sysco Corp Let’s dive into why cleansing and staging data is critical to success and how these best in class companies have approached data cleansing and management to drive increased profitability Rahi Khandelwal Frmr Head of Global Enterprise Analytics Team, Del Monte Foods Gerard de Bruijn Global Excellence Director Head of US Practice Visualfabriq

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. Let’s dive into why cleansing and staging data is critical to success and how these best in class companies have approached data cleansing and management to drive increased profitability

Q 1: What do each of you consider “foundational” data 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? Gerard: The capability to unlock and process data from internal company and external consumer and competitor data sources at commercial plan level (account/SKU/week)

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 ? Gerard: Clean data is accurate and complete and requires dedicated work to realize and maintain this. It is important that companies certify that the data is accurate and cleansing happens on a continuous basis.

Q 3: Why do we need to look at the data differently today? Gerard: Zero touch planning is possible with todays technology. You will see technology capabilities moving from predictive to prescriptive (voice recognition). As a result, data alone is not enough. The ability to perform applied AI, connected to proven workflows, that will fundamentally change the business processes and drive more effective and efficient planning.

Q 4: What type of data are you procuring today Q 4: What type of data are you procuring today? & What type of analytics are you able to create with the data? Gerard: Especially external consumer data and competitor data (Nielsen, IRI, FSI data, Coupons) besides the internal company data

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. ? Can you share some effective practices on how you leverage KPIs, both internally and collaboratively with customers? Focus on the few big win KPI’s, and don’t get caught in analysis paralysis.

Q 6: How do you get users to trust the data, process and technology 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? Besides trusting the data: trust the machine generated predictions, make it visible, urgent, make it happen and stick to it

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 Gerard: The capability to process data for your daily work process is foundational, not the data itself . This data must be available and sustainable to apply Artificial Intelligence. Unclean data leads to wrong measures to train the AI models. TPO should be part of the end to end process, not an extension. TPO takes place at the moment of planning and requires applied AI as integral functionality. This is a foundational requirement to be future fit.

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