This approach not only provides insight into the current maturity level, but also into the connection between ambition, strategy and realisation.

Customer demand

GVB, Amsterdam's public transport company, has the ambition to embed data-driven working broadly in the organisation. After an earlier Data Driven Decision Management (DDDM) scan in 2021, GVB asked IMPROVEN again at the end of 2024: where are we now and what is needed to further strengthen our data strategy?

Our approach

IMPROVEN carried out a comprehensive evaluation built on three tracks:

  1. A renewed DDDM scan;
  2. An assessment of the digitisation strategy;
  3. An analysis of programme plans, architecture and governance elaborations.

Result

IMPROVEN produced a series of concrete products, namely:

  • Updated results by pillar and an overall picture in a spider diagram with explanations;
  • A visual roadmap with next steps and prioritisation for further growth and achieving ambitions;
  • A set of concrete recommendations for strategy, organisation and implementation;
  • An overview of risks and mitigating measures invested in internal owners.

These products help GVB further operationalise and secure data-driven working within the organisation.

Why IMPROVEN?

Our strength lies in the combination of substantive expertise and power to change. With the DDDM scan, we not only chart the current situation, but also provide direction for growth. This is how we help organisations like GVB to really make data-driven working work.

The scan is based on the DELTA Plus model, which distinguishes seven pillars of data maturity:

  1. Data
    Data availability, quality and governance. This is about how well data is managed, how reliable and accessible it is, and whether there are clear agreements on ownership and use.
  2. Enterprise
    The extent to which data-driven working is embedded in the organisational culture and processes. Consider alignment between departments, collaboration around data and the ability to support data initiatives organisation-wide.
  3. Leadership
    Leadership's vision, direction and commitment to data-driven work. This includes communicating a data strategy, setting priorities and creating support within the organisation.
  4. Targets
    Formulating clear, measurable targets for data initiatives. Targets ensure that efforts around data are linked to concrete business goals and that progress can be monitored.
  5. Analysts
    The availability and competences of data professionals within the organisation. This concerns both quantity and quality of people who can analyse, interpret and translate data into action.
  6. Technology
    The technical infrastructure and tooling needed to effectively collect, process and exploit data. Think data platforms, integrations, visualisation tools and security.
  7. Analytical Techniques
    The application of analytical methods such as statistics, machine learning and predictive modelling. This pillar looks at the level of analytical maturity and the ability to extract insights from data.

In doing so, we combine quantitative and qualitative methods:

  • Interviews with key figures;
  • Workshops with risk analysis on strategy and implementation;
  • An employee survey;
  • Document research on strategy, architecture and governance.

This approach not only provides insight into the current maturity level, but also into the connection between ambition, strategy and realisation.

For more questions on this topic

Roos Hijwegen Maarten Franken

This approach not only provides insight into the current maturity level, but also into the connection between ambition, strategy and realisation. Customer demand GVB, Amsterdam's public transport company, has the ambition to embed data-driven working broadly in the organisation. After an earlier Data Driven Decision Management (DDDM) scan in 2021, GVB again turned to IMPROVEN [...] at the end of 2024.