Over the past 20 years, between large Enterprise Resource Planning (ERP) projects such as SAP/Oracle, Six Sigma, and other continuous improvement approaches, small and large companies have learned to think about process and key performance indicators. This effort is the fruit of a collective course, but chief information officers have been very much in the driver's seat, as the implementation and evolution of an information system are so structuring.

For a few years now, the vast majority of companies across all industries have been switching to the digital and data age, and the current pandemic has hastened this trend. Newcomers like startups are born with data as a cornerstone, without a legacy to deal with, and some manage to shake up or complement well-established companies in their market.

Non-tech companies are gradually moving towards the digitization of their business. It is clear that setting up new practices and reasoning about data is complex both in terms of mindset and operations. From an operation point of view, the diversity of the information system, its fragmentation, and very often the absence of a shared data language between IT tools within the same company makes this a challenging exercise. From a mindset point of view, there's a huge difference between understanding the importance of data and making it a priority in an organization and making decisions based on information built from data.

However, the benefits of guiding decision-making and business processes with data are numerous: improving operational efficiency and reduce costs, developing new business models, generating new revenue streams and expanding recurring revenue, optimizing customer interaction and service, and reducing time to market.

Let's take this in the right direction:

1) Treat data as a company asset, as a common good, not a vertical property.

2) To enable data-driven decisions, data must be relevant, reliable, and recognized as coming from verifiable sources. This requires data lakes, data warehouses, and pipelines. Converging toward a shared data language within your company is crucial, regardless of your information system's heterogeneity. Some sectors of activity try to do it for a whole profession, e.g., air freight. Of course, your critical data has to be managed through a master data management process. All this work is necessary to ensure the relevance of end-to-end analysis.

3) Like a tech company, identify the multitude of decisions made across your organization, along with ways data can improve these decisions. Next, prioritize data enhancement opportunities and determine who—humans, IoT automats, or bots—should make each decision in the future.

4) It's a priority to acculturate your key people/leadership team on the role and importance of data and acquire the skills and work methods to maximize the opportunity to make better decisions. Make leaders aware of how data is critical for their business performance, build a common base of data practices and behaviors, ramp up at successful scale initiatives, and encourage projects and initiatives.

5) Add new capabilities, such as data engineering to extract and create the data reliably, data science to design the appropriate analytical models, and decision intelligence to design and tune decision models and processes. Putting the right data in front of competent eyes is key, even if those same eyes were not looking for that data. It involves curation to guide users and help them interpret the data correctly.

So, there is a lot of work ahead to be a data-driven company. Data culture is a decision culture, and the starting point is at the C-Level and with board.