Today, many organizations are struggling to keep up with the rapidly increasing volume and complexity of data being generated by their operations. Some organizations are still relying on manual data collection and analysis processes, which can be time-consuming, error-prone, and limit the ability to make data-driven decisions in real-time. Additionally, there may be a lack of standardized data definitions and governance policies, which can lead to inconsistencies in data quality and make it difficult to trust and utilize the data effectively. Moreover, many organizations show a lack of alignment across teams regarding key objectives and use cases, almost rendering it impossible to jointly work towards strategic goals and objectives.

Many teams, departments and companies are thus seeking to improve their data-driven decision-making capabilities, but simply are not sure where to start.

• Is there the need to invest in technology first?
• How can we make sure our data and analytics support our objectives?
• Do we have the right people aboard?
• Do the teams have the necessary skills & capabilities?

 

Here, it is an important first step to thoroughly assess the maturity of the organization in relevant dimensions. This involves evaluating an organization’s current state of data strategy, identifying strengths and weaknesses, and laying the groundwork for a roadmap for improving data practices.

Accordingly, this blog article leads through the following steps:

1. Defining the Scope and Objectives of the Assessment
2. Collecting Data About the Organization’s Current Data Practices
3. Evaluating the Organization’s Data Maturity Level
4. Developing a Roadmap for Improving Data Practices
5. Summary & Conclusion

 

Defining the Scope and Objectives of the Assessment

So, let’s assess. Before we start, however, it is important to define scope and objectives of the maturity assessment properly:

• What is it we need to explore?
• Who do we need to talk to?
• How do we bring it all together to evaluate findings and to generate valuable insights?

The good news: We at FELD M have developed a comprehensive maturity assessment framework that guides you through this process. It is based on our data strategy framework (see Fig. 1), designed to provide a full view of relevant elements to be considered in the design and implementation of a data strategy:

1. The Business Layer – Objectives & Use Cases: Deriving the objectives of a data strategy from the overarching corporate strategy and collecting the relevant current and planned use cases.
2. The Technology Layer – Data, Tools, Analytics: Evaluation of the existing tech stack, available data, and analytics capabilities in relation to the business objectives and use cases.
3. The People Layer – Organization & Enablement: Definition of the organizational requirements as well as the enablement of all relevant stakeholders regarding data literacy and data-oriented thinking.

 

Collecting Data About the Organization’s Current Data Practices

Now we can start collecting the data for the assessment. Here we can use a combination of different methods: we usually do a select number of in-depth interviews with the relevant stakeholders hinted by the data strategy framework. Meaning, we will talk to the tech experts responsible for the data and tech infrastructure, as well as business stakeholders accountable for the overall objectives and use cases. In addition, it is often helpful to review all internal governance policies and procedures, and to look at the measurement framework and related KPIs.

Maybe needless to say, having a multitude of access points into the organization is crucial to create a complete perspective of the status quo. For this reason, it is important to carefully select the interview partners throughout the organization. The questions are then thoughtfully crafted to address the pertinent aspects identified within the objectives and use cases. In the interviews themselves, however, we do not focus on our pre-written script alone since it is equally important to understand the individual stakeholders from a human perspective.

 

Evaluating the Organization’s Data Maturity Level

Next comes the structuring of the input collected throughout the review process. Based on our experiences, we have crafted a framework enabling us to capture the different voices and put them into an assessment board that directly puts structure into the results. This enables us to not only create a fair and unbiased analysis, but it also results in a meaningful benchmark that helps us and our clients to compare against their industry and the market.

 

Developing a Roadmap for Improving Data Practices

The final step in the data strategy maturity assessment process is to translate the results into a roadmap for improving data practices. This may involve implementing new data policies and procedures, improving processes and outcomes with new data management technologies, and providing training and support for staff to enhance data literacy and analytical skills. The areas for improvement are clearly highlighted in the results of the assessment, so it is usually obvious where to focus and where to start. In addition, we give individual recommendations for each area based on insights created from the interview and review process.

 

Summary & Conclusion

• Organizations are struggling to keep up with the volume and complexity of data being generated, and many are seeking to improve their data-driven decision-making capabilities.
• It is important to assess an organization’s current state of data strategy to identify strengths and weaknesses and develop a roadmap for improvement.
• FELD M has developed a comprehensive maturity assessment framework that includes evaluating the business, technology, and people layers.
• The results are structured into an assessment board, laying the groundwork for developing a roadmap towards increasing maturity.

The assessment not only shows gaps in infrastructure & technology, but also gives a clear view on how well objectives are shared cross-functionally and the alignment of prioritization of use cases across the organization. Usually, this is already the first key step towards becoming more data-driven and creating a data mindset amongst the teams.

 

If you’re now eager to find out more about our work within the Data Strategy Team and learn more about our Data Strategy Framework, you can discover more details here. We also recorded a free webinar on our FELD M Data Strategy Framework for you and wrote about our collaborative work with one of our clients, Jobcloud, on their Data Strategy.

 

In case we left you with any questions or if you’d like to exchange with FELD M’s Data Strategy team, please feel free to approach us by mailing to datastrategy@feld-m.de