ANALYTICS FOR EVERYONE AT ACHMEA 

Scaling analytical capabilities across the organization

 With a history dating back to the 19th century Achmea is a leading insurer in the Netherlands, serving over 10 million customers across various brands. Achmea’s goals is to be there for its customers during moments that really matter. To reach this goal, investing in data is indispensable. However, to tap value from data, engaging all employees in the field of data analytics is a critical key to success. 

 ACHMEA’S CHALLENGE 

 Equipping employees from every level in the organization with the right data & analytics skills. 

 THE SOLUTION 

 Analytics for Everyone: We jointly developed learning journeys for each talent at every level of the organization. 

 OUR APPROACH 

 To determine which learning journeys are best suitable for which employees, we took a step-wise approach: 

  1. Identify key skills for driving results with analytics.
  2. Determine which roles are involved in analytical projects.
  3. Design tailorized learning journeys for each role.

Approach

  1. Identify key skills for driving results with analytics.

    To identify key skills, we took the analytical cycle for problem solving as a starting point. This cycle shows the five necessary steps that are required to move from a business problem or challenge to initiatives that deliver results.

    Exhibit 1: The analytical cycle for problem solving

    Step 1: Define and structure a business challenge – This is the starting point of an analytical assignment and should always be related to a clear business challenge. This step usually requires a deep business knowledge and solid problem-solving skills.

    Step 2: Collect and organize data – Collect and organize all relevant data to test hypotheses and ideas. This step can be quite challenging as data may come from various sources and formats from both in and outside the organization. Depending on the complexity of the case, required skills may vary from basic spreadsheet operations to coding and ETL building.

    Step 3: Data analysis & Machine Learning – After the data has been collected and prepared, the analysis phase can start. This phase delivers the required insights and prediction models from data. A good understanding of data exploration, applied statistics and machine learning is required in this step.

    Step 4: Present opportunities and solutions – In this phase of the cycle results from the data analysis step are synthesized and shared with key stakeholders. The goal of this phase is to ensure decisions are made and follow-up steps are defined. To do this successfully requires clear storytelling, data visualization and presentation skills.

    Step 5: Implement results and drive change – The final phase in the cycle ensures the recommendations from the earlier phases are implemented. This could vary from a simple  policy change to building new products and services. Often a new process is started in which multi-disciplinary teams design and build the solution, usually in an Agile way-of-working. This step usually requires a range of skills such as teamwork, stakeholder management, time management and (project) leadership.

  2. Determine which roles are involved in analytical projects

    After we had identified the most important skills for solving business problems with analytics, the next question we addressed was which typical profiles do we distinguish during this process. We roughly made a distinction between three key profiles: Decision Makers, Analytics Translators and Data Experts.

    Exhibit 2: Target audiences we distinguish in the analytical cycle

  3. Design tailorized learning journey for each role.

    Based on the necessary skills per step in the analytical cycle, combined with the key roles that usually are involved in that process, we developed various learning journey per profile. Per profile we identified to what extent they should be able to contribute to each step of the cycle and based on that designed the required learning journeys.

    Exhibit 3: The desired skill level per step in the analytical cycle for each profile

    Although the learning journeys are tailored to the needs of specific profiles, there is something that ties them together: The Analytical cycle for Problem Solving. All the steps of the problem-solving cycle are addressed in each journey. This is done to create a common language of problem-solving with data and helps each employee to understand where and how to contribute to the cycle. Some examples of learning journeys we have developed are Analytics for Translators, Analytics for Decision Makers and Analytical Leadership for Data Consultants.

     

Impact

After pilots with each of the learning journeys, Achmea has rolled out the learning journeys all over the organization. More than 250 participants have already enrolled into one or more of the learning journeys.

We ask participants to bring in a real-life business challenge at the start of their learning journey. In this way participants bring their newly acquired skills into practice immediately. Furthermore, they contribute to solutions for the firm’s challenges by identifying new growth opportunities. This makes the workload during the learning journeys a bit challenging, but at the end both the participants and the BAYZ team are proud of their achievements.

8,2

Overall score

85%

Recommendation rate

Up to 10x

Return on investment

Manager Customer Centric Interaction

Yory Wollerich

Thanks to the fantastic help from BAYZ, we are able to make everyone within Achmea think and work data-driven. With a practical approach and use cases from everyone’s own working environment, they ensure that we both level up on competencies and create value for Achmea.

Lead Achmea Analytics Acadeny

Sietske Snoeck

I experience the collaboration with BAYZ as very valuable. The training courses they provide for us definitley add value for both our employees and our customers. BAYZ thinks along with our wishes and together we continue to develop continuously.