Show me the DATA!

In the now iconic scene from the movie "Jerry McGuire", the character played by Cuba Gooding Jr. goads Tom Cruise playing Jerry McGuire to "show him the money". In today's data-driven world, it might well be "Show me the data!". In fact, "show me the data" needs to be the rallying cry of every executive in charge of making go-to-market and other business decisions today. Here's why:

1. Customer data analytics has changed. Besides more data sources being available, what we can do with the data in terms of analytics and inference making has also evolved considerably. No decision with major impact to an organization financially and/or operationally should be made without inference to data today. There is no longer a need for “seat-of-the-pants” decision making even when it comes to white space and pioneering areas. There are data sources that were simply not available before, if only companies know where to find them and how to best harness them.
2. Steve Job's famously declared he did not rely on focus group data. The story is somewhat apocryphal, and he did not mean that to imply his executives do not rely on data. Across the global Apple organization, there are tons of roles with titles such as Customer Insights, Customer Analytics etc. with some holding PhDs. Do a search on LinkedIn and see for yourself.
3. Traditional large organizational structures have data siloed and with the best of intentions, have not been able to create effective structures to disseminate the data widely across lines of business. Many providers have tried to solve this problem over the years and yet our executive opinion interviews still reveal this to be a major challenge. In analyzing the solutions out there, one thing that has become clear to us is that part of the problem lies in the fact that the solutions in the market have been developed by software companies. These companies know how to build software tools but are far afield from the data and insights needed (and most importantly in what consumable formats) for executive decision making. To this end, the company we just acquired and rebranded e361.ai® is going to leverage AWS Bedrock with dashboards built in AWS Quicksight to solve this data dissemination problem. We plan to launch this offering in late 2024.
4. Another point is the data in reports from outside research and data suppliers and from internal data analysis staff are not in formats best suited for decision makers to consume. Again our Quicksight-based dashboards with role-based and fully customizable views will solve this problem.
5. Data is everywhere and we often year customers say: "we are drowning in data but starved for insights". Which data will serve the particular decision to be made best? This is where a good data architect, either internal or with your external supplier, comes in. The number one skill required for this is the ability to effectively "bridge" between the business objective and the ability to put together the right data collection map. There are three major components to this, with many variations depending on both the nature of your business and industry, and the nature and volume of data generated and being available to you.
   1. In the case of primary target audience research or competitive research, this would be the survey instrument or qualitative discussion guide.
   2. In the case of internal data collection, it would be the right customer engagement data and/or telemetry data (website and app engagement data and heat maps, abandoned trials, etc.).
   3. In the case of third-party data, being able to put together a bill-of-materials to include (but not limited to) social data, syndicated data sources, business or individual census data, and other data sources (weather, seasonality, geo-location etc.).
6. Last, but not least, and you knew this was coming, all of the above changes with AI. Our research data indicates a lot of companies delving into AI this year. However, upon a deeper drill down, data science leaders we have surveyed recently as part of our FireHose™ Executive Opinion insights series tell us the bottleneck is the cleanliness of the data. Never before have companies faced such a challenge to clean up their data to train large language models as they do now. In fact, a testament to how "unclean" this data has been so far is the often-used term to refer to this data: "data exhaust.” All of the above mentioned best-practices that we have been advocating apply even more urgently to unblock impediments to successful harnessing of the power of AI. Yes, the tools are available and ready to implement, the data is not.

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   Alan Nazarelli is Founder & CEO of Silicon Valley Research Group. Based in San Jose, CA with offices in Seattle and New York, the company works with the world’s most innovative brands to provide timely and actionable market intelligence and strategic guidance to enable them to make well-informed decisions to positively impact revenues and profits and to achieve their growth targets. Connect with Al on Linked in