Maturity Scan: how mature is your organization? — Data Science Lab (2024)

How mature is your organization when it comes to data?
Perhaps a crazy question when you read it this way, but one that will give you many insights.
In the previous blog, you were able to read about the importance of having a data ambition and what this can mean for your organization.
But knowing where you currently stand is just as important.
For this purpose we have developed a Maturity Scan where we evaluate and benchmark the maturity of data capabilities within your organization.
With this extensive scan you will quickly gain insight into where you stand and how improvements can be made in your organization.

From traditional to fully data-driven

In order to map data maturity, we distinguish five different levels in the Maturity Scan, from traditional to fully data-driven.
In principle, there is no right or wrong maturity level: the desired level depends on the data strategy determined for this purpose.
For example, the goal of valuable dashboards and reporting requires a different maturity level than advanced Artificial Intelligence applications.
The maturity level is assessed in the Maturity Scan on six different streams (streams): data, data products, tooling, data professionals, business and leadership.

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Streams

Data The first stream we examine lies at the heart of data strategy, namely data.
Within the data stream, several aspects are important to determine the level of maturity.
We look at to what extent the data is available and to whom.
We assess the quality but also the diversity of different types of data sources and whether data governance is in place. Products The second stream is about the ambition, quantity and diversity of data products.
This can range from dashboarding and reporting to advanced AI solutions such as neural networks.
Another equally important aspect is how the products are incorporated within the organization.
Is control centrally arranged and do they align with strategic goals?
Do the products actually make an impact within the organization, or do they never come to implementation? Tooling The last of the technical streams is tooling.
By this we mean the tooling available to data professionals to execute projects and put them into production.
An analyst who can only use Excel or who can and may only work in one particular programming language because that fits within the IT architecture can deliver less fast and less advanced data products than a data professional who has a variety of languages and software at his disposal. Business Whereas the above streams are somewhat easier to define and measure, the business stream is more difficult.
This is about the extent to which the business recognizes and applies the value of data and analytics.
Are decisions now made based on gut feeling and domain knowledge or based on data?
Is the data and analytics department routinely involved at the start of new projects?
All issues that indicate a certain level of maturity of the business in terms of data. Leadership Experience shows that no matter how good the intentions are, a data strategy does not get off the ground properly without support from leadership.
Importantly, data strategy should be among management’s top priorities.
One indicator of data maturity from leadership is that there are organization-wide goals on data.
Perhaps a Chief Data Officer has even been appointed to give even more weight to this. Data Professionals If you want to grow in maturity then attracting, retaining and growing the right mix of data professionals is essential.
Data professionals are broadly defined in this case: data analysts, engineers, data scientists, as well as emerging positions such as analytics translators and data evangelists.
As the desired maturity grows, the need for these professionals, from junior to senior, increases.
Examining these streams provides a clear picture of where an organization is on the path to becoming a data-driven business and where the key areas for improvement lie to get to the desired level.
Curious about how your organization scores on the above points?
Please contact us! Next time, an interview with Jeroen, data strategy manager, about his role as a bridge builder and the one who gives organizations insights into what they already have and where they can grow.

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Maturity Scan: how mature is your organization? — Data Science Lab (2024)

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