Data-driven versus data-informed

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In today’s world, data has become an invaluable resource. As Alistair Croll and Benjamin Yoskovitz point out in Lean Analytics , data is so powerful that it can be addictive, especially when businesses rely on it to make every decision. However, this over-reliance on data, or over-analysis, can lead us to miss the bigger picture. After all, we don’t need an A/B test to decide what to wear each morning—if we did, we’d be paralyzed at the door.

One of the biggest criticisms of the Lean Startup approach has been its over-orientation towards data. Many argue that we should view data as a useful tool, not an end in itself. Croll and Yoskovitz pick up on this afghanistan phone number list and argue that companies should be “data-informed,” not “data-driven.” Otherwise, there is a risk of optimizing only one part of the company, without considering the overall impact.

The Balance Between Data and Intuition
The authors remind us that automatic process optimization can be dangerous if done without human oversight. One example cited in the book is the case of Orbitz, a travel agency that found that Mac users were more willing to book more expensive hotel rooms than PC users. Based on this information, Orbitz was able to adjust its pricing strategy for this segment of users. However, an algorithm that optimized based solely on this information could have had negative consequences: a possible negative reaction from customers or a perception of discrimination.

Another relevant case is that of Omniture, a marketing analytics company that found that ads featuring scantily clad women increased clicks on the ads. While the data indicated that this strategy was effective in the short term, the company realized that the approach could harm its reputation in the long term. Therefore, human judgment was key to balancing decisions and opting for images that reflected the brand’s values.

The Importance of Overcoming “Local Maxima”
In mathematics, the concept of a “local maximum” refers to the highest point within a specific environment, but not necessarily the highest possible point. That is, by optimizing a specific process, maximum performance can be achieved within a set of conditions. However, as Croll and Yoskovitz explain, this approach can lead a company to miss out on larger opportunities.

This is where human judgment plays an essential role. While a machine can find the best outcome in a defined scenario, it lacks the ability to think outside those boundaries and question the frame of reference. As Richard Dawkins explains in his book River Out of Eden , evolution creates different eyes for different species, but it cannot “go back” to redesign the eye of an already developed species. Optimization driven solely by data faces this same limitation: it maximizes local outcomes, but does not foster disruptive innovations.

Innovation as a Combination of Data and Vision
Many entrepreneurs interviewed by Croll and Yoskovitz recognize the value of data, but are also wary of leaving all decisions to quantitative metrics. Like them, Croll and Yoskovitz say that a holistic view of the business and the market is essential to making decisions that truly drive a company’s growth and sustainability. Optimizing every aspect of a business can lead to improvements, but true innovation comes from combining data with human creativity.