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Building an Experimental Enterprise

February 22nd, 2018

Originally published on LinkedIn in December 2015.

Building an Experimental Enterprise

At Silicon Valley Data Science, we believe in building an Experimental Enterprise that is capable of fantastic learning and growth. In my work with our team of data experts, as I interact with the senior management of our current and future clients, we discuss important considerations and traps to avoid as they become more sophisticated at using data to drive their businesses. We focus on business decisions and philosophy, rather than on individual technology choices. This post provides a window into some of the frequent topics of these discussions.

Questions to Ask

If you are interested in building your own Experimental Enterprise, then consider the following questions. If you answer “no” to any of them, then that’s an area you’ll need to work on.

Do I avoid making gut decisions?

In facing any business decision, your first instinct should be to look for relevant data in your business systems to help you evaluate the options. Take a look at past decisions that you may have made on instinct alone, and examine the data that was available at the time. How might that data have given you insight into the eventual outcome? Use this research to understand how you can better inform yourself in the future, so that you can make decisions based on information, rather than on luck. Especially when you’re considering complex or controversial business decisions, using data and a fact-based view to help discuss objections or obstacles will keep the deliberation from being derailed by competing anecdotes.

Do I truly value failure?

Paying lip service to fast failure doesn’t help. Ultimately, the reason that failure is quite hard on companies and individuals is that it challenges the established mindset that success is the only desired outcome. To test a hypothesis properly in business, you must have a course of action in mind for both success and failure. Plan ahead so that, if you don’t succeed, you know what you need to do next in order to try again, instead of throwing up your hands. As long as you have a good mindset in this way, then even if your experiment fails, you can still move forward to the next branch in the decision tree. You may have to revisit your assumptions, or even take a first-principles view on evaluating an opportunity, which means: don’t be constrained by the things everyone has established as constraints. But have a plan for failure and understand what you need to do next to make that failure a valuable learning opportunity.

Do I evaluate my own ideas?

Bubba Murarka notes that the ultimate skill of a good product manager is to have a high rate of idea validation, not just a high rate of execution or experimentation. Achieving this requires a mindset shift from aimless exploration (“What is the data telling me?”) to purposeful fact-finding (“What do I want to learn and how do I collect or generate the data to support my thinking?”). In addition to evaluating the outcomes from business experiments, you must also evaluate your specific hypotheses and what led you to create them. To have a high rate of idea validation, you have to question your questions, not just your data.

Pitfalls to Avoid

In becoming an Experimental Enterprise, there are numerous traps that may hinder your progress or force you into sub-optimal decisions. Watch out for the following.

Starting with a particular technology solution.

There are no magic tools or platforms that solve everything. We recommend starting with clear intent on a set of business objectives (what do you actually need to accomplish in order to move your business forward?). Then move with purpose to understand how the data you have now, or will have in the future, can assist you in accomplishing those objectives — and what tools you’ll need to wrangle that data. Your choices of technology and analytic platforms should be driven by current and future business needs, not by current fads or legacy constraints.

Looking for perfect answers (they don’t exist).

We live in a world of probabilistic data. Data elements are generated from so many sources and with so much inherent lack of quality that attempting to construct perfect views is a fool’s errand. We used to talk about “analysis paralysis” in the past, but it manifests even more strongly now that data is more ubiquitous. You must avoid becoming paralyzed while waiting for perfect data to arrive to validate any decision. Perfect answers don’t exist. Instead, use the data you have to make a more informed decision — as long as you’re also careful not to conveniently tailor the facts to your hypothesis.

Assuming that data alone will help you innovate your business.

Fifteen years ago, I used to ask clients to just give us their data in the hopes of finding analytic gold. Though this approach can be helpful at times, I’ve come to the conclusion that it’s usually not. Your data can’t provide innovation for your business on its own. It can provide insight into what is happening, or help you predict what might happen in the future, but data only leads to benefit if you have a plan. You must independently develop business intent and ask good questions. There will be times when it’s worth mining your data to inspire yourself, though: sometimes starting small (with an incremental increase in sales, improvements to margin, or a lift in customer engagement) will help you to understand bigger potential actions as well as where your data can help, before you start to dream even bigger.

In Conclusion

The world of data is changing. The skillsets of the people who use it are changing; consumer expectations are changing; even employee expectations are changing. In the conversations I have with current and potential clients, they recognize this: the recruits they’re talking to are eager to work with data in their decision-making processes. The fundamental relationship of people to data has changed. To truly take advantage of this fact — to thrive — your business must adapt.

An Experimental Enterprise is, fundamentally, an organization that thrives on change, and that uses data as a catalyst. Becoming an Experimental Enterprise means reshaping the way you and your company see things like failure, the role of technology, and your own gut instinct. But the benefits are potentially limitless.

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