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  1. What are the critical success factors, dos and don’ts, for developing a Global Data Strategy to enhance the application of Advanced Analytic Methods?

    Analytics is the bedrock on which robust growth engines can be built. However, the question is how does one drive the analytics agenda in a way that it is pragmatic, effective and scalable? Analytics is a spectrum that ranges from basic “what happened?” analyses to “what can and is about to happen?” Lately, we all want to get to the “high end” stuff but do not have much patience nor tolerance to pay attention to the fundamentals. Advanced analytics at scale can generate growth via predictive applications. However, scaling advanced analytic applications are a challenge primarily due to non-existent, at worse, and poor, at best, coherent data strategies. In a nutshell the do’s and don’ts of a Global Data Strategy are as follows:

    • Have a coherent data strategy

    • Don’t base that strategy on the number of zeroes in the spend line; in my experience I have seen zero covariance between the spend and the quality of the outcome. In fact the more spending power you have the more likely you are to not have a “coherent” approach

    • Cross enterprise data assets need to be built with a “reusable from the get-go mindset.” One should not always need to find a Data SME to figure how to leverage these data assets.

    • Figure out a way for businesses to stand up and own Master Data Management. Often times this is the purview of IT functions and this needs to evolve.

    • Finally, use the pareto approach to data strategies and applying advance analytic solutions

  2. What major trends in the Analytics industry have evolved?

    • Data and technologies have evolved and driving significant improvements in our ability to communicate and influence, that is for sure – but I think the fundamentals of building and managing relationships are still the same.

    • ‘About 20 years ago, most BIG CPGs were in the initial stages of evolving from Shipment-based data and analytics to Syndicated POS. Consumer focus was limited with relatively small scale market research and focus groups which arguably represented the overall market. Consumer advertising was at mass scale, mostly one way (TV, Radio etc.) and not really traceable.

    • Then BIG CPGs started building Direct Retail feed POS databases with access to store level granularity and started coupling these with geospatial data sources. This enabled better localization capabilities. Local assortments, pricing etc.

    • Now with the accelerated adoption to the ever fast changing digital world, it’s the age of channel explosion, collecting personally identifiable information and turning this information into meaningful communications and relationships with consumers.

  3. How does value work in the CPG world today? How effectively it is used as a strategic level.

    We spent a lot of time digitizing data assets, optimizing Media and Trade budgets etc I feel the next frontier is a deep enterprise-wide understanding of how pricing actually works and how to activate it as a legitimate long-term growth lever vs. the “knee-jerk”, “close-the-profit-hole” approach it currently enjoys in most organizations. Cost plus, competitive based, and close the gap to my business model were very effective ways to tackle this in the past. This has led to dysfunctional practices that are accepted as normal – like the one where pricing actions are tied to fiscal planning cycles vs. real-time opportunities, for example like commodity basket movement

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