Data Strategy sets winners apart

In recent years, companies are exposed to frequent changes in their social and economic environment, they faced many challenges such as:

  • Increasing complexity of business transactions, due to customization of products and services, innovation and global distribution,
  • Accelerating rate of change in business models due to fierce online and international competition,
  • An expanding regulatory framework, forcing companies to prove they fully understand  their operations and they are compliant with applicable regulations and laws,
  • A growing dependency on information technology and the increasing thread of technology-related risks.

The purpose of your data strategy is to provide the solutions for these challenges and mitigate the related risks.

Companies need to have a solid data strategy that will help them get the most out of their investments in data, business intelligence and artificial intelligence. This enables them to achieve meaningful business benefits and identify new revenue opportunities.

With a solid data strategy, board and management lay the foundation for a culture of data-driven thinking and working. According to a PWC study, highly data-driven companies are three times more likely to have successful returns on their investment than companies that show no progress in their data decision-making. A data strategy allows companies to do things like:

  • Recognize the type of data being generated and how to best store it for effective use,
  • Creating a framework for analyzing the right data and gaining valuable insights,
  • Selecting the right business intelligence and analytics software,
  • Improving decision making to drive business development and value creation.

What is an effective Data Strategy?

A data strategy is still seen in many organizations as a technical elaboration of technical challenges. In a modern data strategy, people, processes, and technology are aligned with the focus on achieving business objectives.

When developing a data strategy, business leaders need to consider three crucial aspects:

  1. What facilities are needed to provide employees with secure and reliable access to data and information,
  2. How processes should be set up that ensure that high-quality data is reliable, accessible and available,
  3. How the right technology can best be used for the optimal collection, storage, sharing and analysis of data.

Once these aspects have been properly and completely described, a good picture of the current situation must be built up. That starts with an inventory or baseline measurement of the current data landscape.

In that landscape it must be made clear which data is located where and how data moves between the various applications. It is therefore necessary to create both a physical and a logical view, which makes it clear what the sources are and how they are used for the applications, analyses, reports and dashboards.

Now we have clearly defined the old and new situation, we can write out the strategy how we want to build the new data landscape that makes all set goals possible for the organization.

What is the effect of your Data Strategy?

When starting up a company or organization, the people, tools and resources are selected and deployed that are appropriate at that time for the products, services and internal processes of the company.

Many organizations are now convinced that they can optimally use the available data for insights with which the company can be better controlled and the vision and strategy for the future can be better substantiated.

The next step is for data, in the form of a digital asset, to essentially become the product the organization needs to thrive in the competitive marketplace, and that requires a major overhaul of the people, the resources they work with, and the way they work. This applies to all layers in the organisation, i.e. the board of directors, management and the employees in the departments.

Now we have determined the relationship between people, proces and technology that is needed to succesfully execute our data strategy, let’s discover the effects on each of these topics.


We need people in all layers of the organisation to design, build, execute and maintain the data strategy. it is clear that the leadership in the organisation has to provide not only the resources, but also the financial means to educate them and provide them with the effective tools and time to execute.

To execute the data strategy, the data steward, scientist, analyst, architect and engineer must work closely together. All assigned people must be available and competent. This requires an assessment of the knowledge, skills and competences of the existing employees, possibly resulting in further training or even insourcing of additional personnel.

The decisions for deploying in-house staff, hiring new staff or hiring third parties are not easy to make. Apart from the financial and time-consuming aspects, the scarcity of skilled and competent personnel on the market can also lead to project delays and high hiring costs.

It is clear that a successful data strategy requires a coordinated approach from IT, HR, Procurement and leadership departments in the organization.


Existing processes and policies probably need a re-design or update to continue support for business request and requirements. New processes and policies might be necessary for collecting, storing, distributing, and accessing data, in order to properly manage and govern the data flows between users, applications, and sources. Think about authentication, and autorisation for users of the self service portals, BI and AI tools, modern applications and more.


For the same reason the technical infrastructure or IT need to be assessed to determine what changes and amendments need to be made in order to continue and optimize support for business strategy, data strategy and related architectures. All aspects in the ISO model, related to technology and IT need to be evaluated and adapted to the requiremenst of the data strategy, so that all user data requirements can be met effectively. Obvious examples are migration to the cloud, sourcing pure technology or ready-to-use services, more bandwith to process more data, new tooling for BI, AI, and analytics application, faster mobile infrastructures to support more mobile devices and users, security features for home offices and much more.

Finally, we want to prepare you that you will never be done with all these improvements in people, technology and processes, because once you make the data strategy successful, it will automatically lead to more data usage, forcing you to adjust your data strategy, leading to … .