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Your Data-Driven Decisions Are Often Wrong: Here's How to Fix That

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About this talk

“Data-driven” marketing, product development, and anything else in business has been particularly en vogue over the last decade. Entire market spaces and high-value software companies have been created over the idea that more data is good, and we should let the data speak for itself. Unfortunately, most organizations aren't making meaningfully better decisions than they were without those tools, and many are making worse decisions because critical thinking has been masked by data.

The ubiquity of dashboards and quantitative data promised to bring better decisions, but our ability to think critically as leaders and make informed decisions haven’t meaningfully improved. Strategic direction relies on understanding how to best integrate data, experience, and diverse perspectives, and can be improved by understanding how to categorize the feedback and data we receive. The importance of leveraging dissenting opinions from all levels of a marketing organization are integral, as a lack of context around market/audience, competitive landscape, and nuanced consumer behavior trends is easier to overlook when relying exclusively on quantitative performance data. Hiding behind too much data can mask a lack of lack of a product, marketing, or company strategy or research -- temporarily.

Paired with the rising trend of firing executives more quickly (especially CEOs and CMOs), the pressure for accountability and quantifiable outcomes is at its peak. Which is why some executives who find themselves under such pressure, that they extrapolate quantitative data to make strategic decisions based on data that doesn’t suggest what they imply it does, hoping that the agreement from the Board and other executives based on the data they present will distribute

Applied well, data-driven decision making is game-changing. It allows for incremental and constant optimizations, driving sometimes intuitive decisions while giving marketers the freedom to take risks and run experiments at scale, knowing that users’ responses will be visible and iterated.

But like any overly simplistic sweeping trend or trendy paradigm, the temptation to blanketly apply a term or concept without solid methodology can have the opposite effect. The misapplication of the growth mindset mentality is one such example - people draw inferences based on the title of the methodology without relevant research, and make misinformed decisions.

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