How to Foster a Data Culture in Organizations

19 abril 2018
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Category DAClub
19 abril 2018, Comments 0

We devoted the last couple of meetings of the Data & Analytics Club to debate about how to embrace and promote the data culture in organizations. To build a data culture is a key aspect for the long as well as the short term success of data valorisation projects and strategic steps. As any deep cultural shift in a company, many organizational and management related aspects must be carefully addressed. We highlight here some of which we consider most important for professionals with this responsibility.

It’s a common view that the best way to attract new believers into creating a data driven organization is through projects showing the great value and impact that (advanced) analytics can bring into the company. To make sure your initial efforts are worth, it seems like a good approach to evaluate all potential projects and ideas with respect to the “technical viability” vs. “value to business” matrix and decide accordingly. Also, make sure guys comfortable in both data and business are the ones leading these initiatives; they need to understand very well the business dynamics and implications. Note that these guys will need to “sell” internally the projects to other departments unless enough “innovation” budget has been assigned to them, which does not look like a common practice. And still, these have to be joint initiatives (business guys together with technical guys) to guarantee results that matter all.

Although many challenges arise, the main and typical complication of these projects is access to data, not only from a technical perspective but also from a political one. Indeed, it is often the case where data does not exist in the whished shape and completeness, and it is difficult to convince “traditional” data owners about the relevance to share it outside their domain area.

In our last blog post we pointed out that a direct reporting line with support from the CEO and a great alignment of business, IT and legal teams were critical in order to gain speed and overcome the obstacles on your path. Here, we extend the list with relevant aspects to keep in mind:

  • – Try to engage business departments from the beginning, find the analytic champions in the business areas. Try a light governance approach, where they are influenced but can also make their contribution.
  • – Clearly state the ROI and benefits of the different projects and communicate them across the organization together with an evolving roadmap.
  • – Keep your “technical viability” vs. “value to business” matrix updated, using it to manage well thought expectations and requirements.
  • – Have a business agile mindset, not only for project development but also for the entire strategy/roadmap evolution and related processes such as legal. You should be able to continuously keep shaping your strategy since you will be learning at high speed.
  • – Try to create a committed and active community among data professionals, organizing ways where information and success cases can be shared, cross department, cross region.
  • – CEOs should make sure that data literate top management executives are evaluating these efforts. They will be able to ask the right and tough questions in order to separate results that look good from the ones that are right.

 

Along the way, it is critical to begin building the foundations, both as IT infrastructures and tools, as well as processes, and it has to be started soon. With respect to the weight (time and money) devoted to each side of the story (meaning “new analytics projects” vs. “common/baseline tools/infrastructures projects”) each organization should find its right recipe and it will very much depend on the standing culture, legacy and who are the people leading these initiatives. Within these “building foundations” projects, three aspects attracted a lot of attention during our meeting:

  • – Common definitions: cross departmental decisions have to be based on agreed common understanding of the inputs and outputs. Yes, different definitions can coexist of one same concept, but there should be no doubt about which one corresponds at any time.
  • – Data integrity, availability and security: data must be trustable and available at all times.
  • – Testing and tracking: you will avoid a lot of time, money as well as wrong data-based decisions if you build testing and tracking mechanisms from the beginning.

 

While building a data driven organization though, one should not lose sight and think data and further analysis are the answer to everything. Although it is a critical asset and can bring light in almost all situations, data cannot model all. Data is not perfect, technology is not perfect and so are the resulting models. Going too far could lead to micro-analysed businesses losing agility and effectiveness. A sensible portion of intuition from experienced and capable professionals is, and will always be, another critical asset; unleashing the full potential of a company happens when both are cleverly combined.

 

Data & Analytics Club

By: Marc Torrent

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