Business and data professionals in therapy:
how to achieve a perfect marriage?
Thorough data expertise in your company does not yet guarantee strong results. The power of data lies not so much in the technical ingenuity behind it, but in its applicability to your business. And that requires a common language. The better the interaction between your data team and business professionals, the more successful you can work with data. But why, in reality, does communication still regularly falter? Ormit Talent sits down with both parties and provides tips on how to set up an effective collaboration.
Why is there such a big communication gap between business and data experts?
They speak a different language
Data professionals have their own data lexicon, which managers usually shrug off. Moreover, data terms regularly grow into container terms, resulting in interpretation problems. When companies take the step to a data-driven business, they let data play a central role in the decision-making process. This does not necessarily mean that everyone needs to understand Python fluently. It means that there is a need for a shared working language.
Business professionals underestimate & overestimate the potential of data
Business professionals often do not know exactly how the data processing process works. Thus, they form their own idea of what specialty each data profile has and what kind of insights the data team can share with the business. As a result, the outcome does not always match expectations. What may seem like a simple task to a manager on paper, in reality requires sophisticated analysis techniques or is sometimes not even feasible at all.
But the reverse is equally true. Data science offers a range of opportunities to automate processes in your organization. Yet few managers are aware of this or shun it from certain assumptions. For example: AI was initially presented in the technology sector as a hyper-efficient alternative to time-intensive human tasks. In practice, the results turned out to be rather disappointing. Those experiences created in many companies an aversion to working with AI in data analysis. And that’s a missed opportunity. AI offers a valuable rational complement to the decision-making process… If you know how to use it.
Data team struggles to properly capture business questions
Managers often formulate their assignments for the data team in terms of challenges, goals or ideas. To do this, they rely on their own intuition, observations and questions from the market, among other things. Data professionals are usually less in touch with the field, so the concrete expectations of the client are not easily defined. As a result, there is a risk that a data project responds to only part of the demand and thus falls short for the stakeholders. The result? Growing frustration between the data and business departments. In short, time for a good conversation!