Four years ago, Havard Business Review  wrote that Everybody wanted to be one, everybody claimed to be one. It had become the unbeatable pick-up line at trendy Silicon Valley venues.

I have always resisted calling myself so; not because I wanted to differ from the trend, nor did I want to play the false-humility card, but working with scientists at Stanford University had really put things in perspective. I could see and experience what “rocket science” meant to data, and how math and statistics heavy the domain was. I saw how experimental it could be.

While I appreciate that the term has made data appealing to a larger community, I feel it doesn’t represent what the discipline requires to lead a data revolution. Yes, science applied to massive data sets is changing the world. Yes, XYZ company’s research uncovered great findings thanks to data. For most business organizations though, the game changer lies elsewhere.

Companies cannot rely on a small group of individuals to change their game.

If algorithms can find new nuggets in the business with a high degree of confidence, the hardest part lies in understanding, acceptance, execution and the monitoring of those subsequent projects set to harvest these opportunities. Companies are comprised of people with a broad variety of aspirations and qualifications. They run on thousands of processes that focus on delivering values to customers while controlling the cost of production or service. Even when science delivers clear directions, there is still a whole challenge ahead: business transformation that will these outputs concrete.

Today, I have tools that can monitor my sales in real time and see which customer profiles buy what, but how can my sales representative ingest this continuous flow of information? I can pinpoint very refined clusters in my customer base, but what can my marketing people do to properly target hundreds of them? I can seize an inventory reduction opportunity, but how will it play out in particular markets with legal constraints? I can draw complex diagrams and deliver bulletproof math, but how far can I go if my stakeholders can’t find an application to their field, let alone trust what they’ll see as a black box?

Companies need “data CEOs”—individuals with leadership on their data, their process, their contributors and internal clients, and on their technical solution design. Regardless of position, catching the data wave calls for an extension of responsibilities to ensure that the information flow we are accountable for is sustainable, valid and useful to the organization. Leading with data will take more than hard skills. Soft skills and business expertise often prevail sooner that we expect, as change and innovation are pushed in the organization. These qualities are much more commonly found than math and statistic knowledge. Properly grown and nurtured, they can create the pillars for a data-driven culture.

Most of us are way past due to begin a Ph.D. in math, and I doubt my cerebral capabilities would be up to the task anyway. However, I firmly believe that all of us can grasp the foundations of what it takes to become a Data CEO. After 12 seasons of teaching graduate, undergraduate and professionals at Stanford Continuing studies, I have witnessed the transformation of my students who’ve become inspired and pragmatic data driven professionals. It just takes open-mindedness, curiosity and eagerness to be challenged (and maybe a great instructor ;)?)

There is nothing complex in data processes for businesses, from transactions to insights. Once equipped with basic understanding and the proper lenses, analytics can become a very standard process which can be easily reproduced. Volumes and data types will vary, sources will change, processing may become more complex, but the foundation will always remain the same. The key is knowing who to call in IT, Data Science or in Finance & Operations and when to avoid building a “house of cards” solution and “sorcerer’s apprentice” math.

By mastering our core assets, (systems, data, people and processes) we can ensure better control of our field of responsibility and accountability while being a valuable counterpart to IT, Data scientist and our peers to whom we can clearly express our business needs, ideas and constraints. By knowing our options and trade-offs, we can initiate a true partner relationship that is often missing between business and technology teams.