Data Strategy

Leverage your data ambition! In 3 steps.

Written by
DSL
Published on
August 14, 2024

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In our previous blog, we talked about what is a good data strategy?
This time I, Job Gawel data scientist and strategist, will take you through and address the phrase we often hear, “We need to do something with our data.”
Technological capabilities are sprouting like mushrooms today, and the competitive advantage companies gain by leveraging the value of data is huge.
The opportunities to become more data-driven seem to be there for the taking.
Yet this is proving easier said than done.
After all, how do you go from stand-alone innovative projects to a data-driven corporate culture?
We take you through how you can leverage your data ambition, in 3 steps.

Step 1.
From vision to tangible data goals

Take a moment of rest, zoom out and consider what your company derives its raison d’être from.
The best-performing organizations have a resilient, idealistic vision that is outward-looking.
Focused toward the future.
A vision describes an organization’s ambitions and the broader impact it wants to create.
A direction that is clear to everyone inside and outside the organization.
A direction that makes it easier to make decisions internally.

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From the vision emerges a mission: the tangible goals that an organization pursues.
Leveraging data is a means to achieve these goals.
Data, and data-driven work, are never an end in themselves.
Once the goals have been identified, they can be translated into data goals.
What is important here is that there must be a clear link to the mission.
For example, if as an organization you have the goal of becoming the largest online retailer, then real-time insight into acquisition or insight into the complete customer journey are appropriate data goals.

Step 2.
Create a healthy dynamic

How data in execution contributes to achieving goals varies.
However, there is an essential dynamic present in almost every successful data-driven organization: a push-and-pull between management (or other central body) and the executive branch (decentralized).
Management pushes from the top down to get more value out of data, and the executive branch pushes new ideas and capabilities up into the organization.
These dynamics need to be balanced.
For management, it is important to create space and maintain oversight.
Creating space here means room for creativity, room to experiment, room to fail, and room to arrive at unexpected insights.
Freeing up budget is an important aspect here.
To maintain an overview, we recommend setting up a team or department with a counter function.
Here, employees from other departments can share problems or innovative ideas, but the department itself can also actively pick up ideas from the business.
The department with the counter function then prioritizes and implements projects.
Decentralize the identification of supplies needed to execute projects.
When the business wants to work more data-driven, the requirements for this, such as tools or a digital platform, will surface automatically.

Step 3.
Let data flow through the organization

It is important to make good agreements about data within the organization, for example about authorization to certain sources.
Even more important is that data can flow ‘smoothly’ through the organization so that it ends up where it is most useful.
In other words, to those who can actually extract value from the data.
These are not only the data scientists or data analysts, but also your employees who, for example, use the results of a model or a dashboard.
If these employees are not given the opportunity to actually use the valuable data, the added value of data will never reach its full potential. “We have to do something with our data” is therefore actually a very good first thought toward a data-driven organization.
Once the vision and mission are clear, the data goals can be determined.
And with the chosen data goals comes a certain level of maturity in the field of data-driven work.
But how do you determine the current maturity of your organization?
And how do you determine what changes are needed to achieve the desired maturity?
You can read the answers to these questions in our next blog by my colleague Irene Schut!

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Questions?

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