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Towards a data-driven organization: 6 prerequisites for success

12 Mar 2019
Putting data first requires a transformation that will only succeed if there's a vision, leadership, and a change in terms of culture. What are the 6 main prerequisites for successful data management?

Putting data first requires a transformation that will only succeed if there's a vision, leadership, and a change in terms of culture. What are the 6 main prerequisites for successful data management?

It's all about data: prerequisites for meaningful benefits

Virtually all organizations concern themselves with data. With the ever-increasing digitalization of products and services, data has grown from a waste product of business processes to the basic ingredient for a well-oiled company. The amount of information expands, resulting in a pressing need to quickly process, integrate, and analyze the available data so it can be translated into insights which can serve as input for all decisions. After all, when handled correctly, data helps optimize processes and understand customer behavior based on empiricism rather than gut feeling.

No wonder every self-respecting company participates in the race to become a data-driven organization. But it's easier said than done. Despite investing in hardware, software, and people, many companies fail to grasp the business opportunities associated with data. A recent McKinsey survey found that only 8 percent of 1,000 respondents with analytics initiatives engaged in effective scaling practices.

Usually, such companies tend to skip 6 critical prerequisites which should be met to see meaningful benefits from data-related investments. Curious to know what they are? We've set them out below.

1. Detailed data strategy

A common pitfall is to treat data analytics from a mere technical perspective. What you need to do is consider its contribution to your business objectives. A long-term roadmap including clearly defined value-adding use cases can help you stay on track when walking the development path to a mature data-driven organization.

2. Adequate data landscape

The architecture, hardware, and tooling you use should be robust and future proof, matching the desired balance between costs, flexibility, and speed. If, for example, every employee should ultimately be able to explore on an organization-wide data platform, you'll have to take this into account when setting up your data landscape.

3. Solid metrics and data

It is paramount to have one shared vision of the truth based on unambiguous definitions of quantities and KPI's. Likewise, data should be correct, complete, and topical. To achieve this, the entire organization needs to be on board – everyone should understand how data is created, processed, and changed throughout the process as well as prevent and check data errors. They need to understand that not having data insights is less harmful than working with erroneous insights.

4. Agile development approach

Through an explorative, pilot-based strategy, you can rapidly gain insights that are truly valuable to decision-makers. Perform short-cycle experiments that involve users and offer room to fail. This allows you to quickly validate what works and capitalize on it. Moreover, there are benefits to involving end users in the development process: they become skilled in handling data insights, and you work on improving your organization on a continuous basis.

5. The right capabilities

Knowledge, competences, and experience are required to make data part of your way of working. To become a real data-driven organization, employees throughout the organization need to understand how to properly use data, and they should be able to deliver on actions suggested by the data and models. Not just leaders and analysts, but front-line employees as well.

6. Supportive company culture

Organizing the above prerequisites is not enough. Possibly, the most important yet difficult-to-achieve prerequisite is having a culture that fully supports data-driven decision-making. Unfortunately, too many companies consider data management to be an IT project rather than a strategic change program. To make the transition to a data-driven organization successful, management should express its support and act accordingly, allowing every employee sufficient time to invest in data management.

Hopping on the data train: a necessity

The road to a data-driven organization is a long one. Yet disruption lurks in the shadows, forcing you to get on your feet and go on a trek. First things first: meet the 6 prerequisites to build a solid foundation. Then, you can start thinking about more advanced steps, such as Artificial Intelligence and Machine Learning.

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