Excellence through data

Data & AI Strategy

Leveraging data for real-life results

title: Data & AI Strategy header: Excellence through data subtitle: Leveraging data for real-life results type: service serviceTag: data-strategy

When developing AI and Machine Learning Algorithms, you need an effective data strategy. This allows you to effectively acquire, store, process and derive value from your data to improve your business processes. An effective data strategy takes into account the organizations strategic direction and objectives in order to leverage data as a valuable corporate asset.

Principles of a good Data Strategy

No 1. Business objectives - An effective data strategy must be driven by an understanding of how data and insights can improve a business process, and how this contributes to the organization's strategic objectives and direction. When dealing with data and information, it is crucial to quantify the benefits and business outcomes of leveraging data. These business outcomes should support the overall strategic goals of the organization.

No 2. Sourcing and acquiring (critical) data assets - Within an organization, data is usually specific to an application, interaction, business process, transaction, etc. Critical data is associated to these characteristics:

  • It is linked to a critical business aspect of the organization (e.g., product, customer, marketing, finances, etc.);
  • It is captured, created and used across multiple systems and business processes

It is unlikely that an organization will contain the entire dataset that is needed to develop an algorithm or useful insights. Most often, data is also required from external sources (via procurement or third parties) or still has to be created.

For all data, both internally and externally obtained, a well-defined strategy is required, as well as a comprehensive understanding of the attributes and access needs.

No 3. Data ecosystem (IT and data infrastructure) - Design and build a flexible and scalable data architecture, taking into account the existing organizational systems and data resources. Next to an IT architecture, there is also a need for a business architecture to define core data capabilities and to envision the flows of data across systems and processes.

No 4. Data governance (people, tools and processes) - Ultimately, the implementation of a data strategy is not an end project, but rather an ongoing process that must be governed. This involves establishing the necessary procedures, standards, processes and responsibilities to create consistent and secure data management and sharing. Data governance aims to bring everyone in the organization on the same page.

No 5. Data security - Data management and security are a crucial part of a data strategy. Especially now with GDPR and EU law restrictions, which forces every organization to store personal data securely.

No 6. A clear roadmap - A roadmap from the current state to the future state including all of the steps that need to be executed to reach and maintain or update that state. This also contains a timeline, showing the progress and prioritized activities.

Data Governance as an integral part of a Data Strategy

Notice from the 4th principle mentioned above, that data governance is an integral part of data strategy. Data governance focuses on the organizational and behavioral aspects of the data strategy, the formalization of accountability for the management of data, improvements in organizational data literacy (enabling people to recognize and treat data as a valuable asset), the reduction of data-related risks, and assuring that data rules, both internal and external, are followed. More information on the critical elements for effective data governance can be found on our dedicated page.

Turn your Data into Insights

When gathering, processing, and analyzing data, make sure you capture the human knowledge of that data. Understanding the context and domain of the data will help you eventually to understand the results of the models and algorithms that are created, and will lead to better decision making.

Data visualization is also key to understand model results and to present your analysis to business stakeholders. Visualizations and the right way of story-telling will enable your stakeholders to interpret and understand the results and see the added value of your analysis, leading to a wide-spread adoption across the organization.


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