Spark 2023 Recap Part 3: From Data to Outcomes
Our Spark attendees agree: The world of data has evolved into a realm where there is no longer necessarily such a thing as an “enterprise data strategy.” Rather, data has become a foundational piece of corporate strategy. Data underlies everything, and its value lies in how you make use of it across the enterprise.
Yet data can be a double-edged sword. It’s resource-intensive to collect, and if it isn’t driving business value and outcomes, it can be a liability. With so much of it floating around in enterprises, the question is rarely “do I have the data I need?” and far more often “do I know which data I need?” Indeed, data is often compared to blood and oil, and it needs to be used with purpose in much the same way. As one of our participants noted, strong data strategy means clarity of objectives – so start by identifying the most valuable questions that your organization isn’t asking or the most important decisions that need to be made, and then build the processes and tools you need to turn your data into action.
As prevalent as data is throughout every company, if it’s scattered and unmanaged, it’s going to be hard to use effectively. The need for centralized data platforms was a common theme for our participants – these platforms can collect, store, process, analyze, and share data across sources and applications. Perhaps most importantly, they enable strong data governance – ensuring quality, consistency, and clarity in your data while protecting sensitive information. Without a data platform, it’s difficult to consider your organization truly “data-driven.”
Strong data strategy means clarity of objectives – so start by identifying the most valuable questions that your organization isn’t asking or the most important decisions that need to be made, and then build the processes and tools you need to turn your data into action.
Turning buzzwords into a reality is not always easy. AI/ML-powered data analysis can be highly successful at identifying the “next best actions,” but offering up too many of those can have a paralytic effect on the organization and dilute their perceived value. Boiling those down to just a few at a time makes that data-driven insight more readily actionable.
Change management plays a major role here as well. After all, we’ve been running numbers and creating predictions since long before AI, so replacing those wholesale can create a significant backlash. One idea that’s seen success already – and that several of our attendees said they plan to bring back to their organizations – is to present machine-generated predictions in parallel with human-generated ones, allowing your people to compare the two prediction sets directly and ultimately get accustomed to the new reality. As one participant said, “winning hearts and minds,” from stakeholders on down, is the hidden key to using data effectively.
Read more takeaways from Spark 2023:
Part 1: The Generative Generation
Part 2: The Great Cloud Reckoning
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