From data confusion to data-driven success:

Read Dion Angelo Valente's first blog post about the use of data. The post is the first in a series of 3 posts about data.

At a time when AI and digitalization are more prevalent than ever, I hear many companies talking about leveraging artificial intelligence to create business value. But it often turns out that the most prominent and valuable step is closer to home: getting a handle on the data your organization already has. Data is the foundation of any AI solution, but many organizations quickly discover that their biggest challenge isn't implementing AI - it's making sense of their existing data.

How many members do we have? It sounds like a simple question, but in many membership organizations, there is no clear answer. The finance department has one statement based on billing, while marketing has another based on campaigns and sign-ups. HR, member services, and IT may also have their own versions. The problem often lies in the fact that data and business logic are fragmented across systems and departments.

Another symptom of not being in control of your data is a perceived lack of trust in the data. Many organizations try to solve the challenge using manual Power BI reports, Excel sheets, and local databases. This may work in the short term, but it creates a confusing and vulnerable structure where essential knowledge depends on individuals, and reporting is inefficient and prone to errors.

The solution is a data platform that gathers and standardizes data and business logic from different systems and makes it available in a consistent and user-friendly format. With a data platform, the organization can:

• Centralize data and business logic from membership, CRM, and finance systems.

• Automate reporting and ensure accurate, up-to-date data.

• Give employees easy access to relevant data when making decisions.

• Minimize manual errors and free up time for more value-adding work.

Organizations can remove bottlenecks and ensure a common understanding of business-critical data by building a strong data platform. Better insight into data is also the foundation for increasing data quality in areas where data quality is a barrier to data-driven decisions.

When data becomes structured and accessible, new opportunities quickly arise. Here are two concrete examples of how member organizations can benefit from a strong data strategy:

• Optimization of courses: By aggregating data from different systems, organizations can gain insight into which members attend courses, how they are evaluated, and what costs are associated with them. This allows you to customize course offerings and ensure members get the most out of them.

• Strengthened membership statistics: A unified and structured approach to data makes it possible to get an accurate overview of membership numbers, resignations, and segmentation. This creates a basis for targeting communication and offers, which can increase member satisfaction and retain more members.

The initial benefits of structuring data are often eye-opening. When the organization sees the value of data-driven decisions, the desire to go even deeper arises. This is where a strategic approach to data becomes crucial.

To take the next step, the organization can:

• Develop a data strategy, that defines clear goals and priorities.

• Invest in skills development, so employees can work efficiently with data.

• Implement governance structures, that ensures data quality and compliance.

A mature approach to data allows initiatives to scale and integrate more advanced technologies such as AI, forecasting (advanced analytics) and is often a catalyst for automation initiatives

My personal view is that becoming a data-driven organization is not a quick fix—it's part of creating a mature and professional organization. It's not just about technology but also about changing mindsets and workflows. The good news is that you don't have to do everything at once. Small, targeted steps can quickly create value and lay the foundation for bigger ambitions.

Just as a well-tended vegetable garden requires time, care, and the right skills, a data-driven organization requires structure, strategy, and continuous development of organizational maturity. But with the right approach, data can become one of a company's strongest assets - and the key to future growth.