What makes a data-driven company

The term “data-driven company” is on everyone’s lips and more and more companies are setting themselves the goal of being data-driven. Even if the general interpretations of this term are rather vague, there is consensus on one thing: data-driven companies make better decisions based on complete information.

While this consensus is certainly correct, there is much more to the concept of a data-driven company. In this article, we take a detailed look at the characteristics of such an organization as well as its potential and the requirements that must be met in order to make a company “data-driven”.

These are data-driven companies

What exactly are data-driven companies? One thing is clear: it is not enough for management to be provided with a business intelligence dashboard that shows a few nice diagrams every month, on the basis of which decisions can then be made. A data-driven company consists of much more:

• Data: Of course, the focus must be on the data and ensuring its quality. Strong data governance is the only way to ensure that the data used is truly reliable and correct. This is the absolute groundwork for the development of a data-driven company.
• Processes: What is often missing is transparent documentation of all data processes in the company – a clear picture of the interdependencies is important in order to identify potential synergies and recognize important connections.
• Technology: Continuous optimization of the information supply chain is important in order to develop data processes sustainably and to be able to meet future requirements. In particular, central data management systems such as ERP, PIM, CRM and DAM should be regularly put to the test in order to identify inefficiencies at an early stage and counteract them effectively.
• People: An important component of data-driven companies is a so-called data culture. In addition to the technological and process-related aspects, the focus is also on people, who must be integrated into the developments by means of suitable measures. This requires education and training to build up the necessary data skills – across all positions and levels. It requires unconditional backing from management and a clear commitment from the entire organization to view the topic of data as a fundamentally important one. This often goes hand in hand with a genuine mindset shift, meaning that change management plays a fundamental role.
• Analytics: Once the system and process landscape has been brought under control and data governance has been anchored in all work processes, it can be assumed that the data is reliable and correct and that analytics results will actually provide usable insights. Here again, the concept plays an important role – decision-makers must think carefully in advance about what exactly they want to measure, i.e. which key performance indicators (KPIs) are relevant and actually allow valuable statements to be made over time that support the design of business strategies.
• Responsiveness: In addition, of course, mechanisms are also needed to translate these findings into adjustments to processes or data – this requires rules and responsibilities, which in turn need to be regularly reviewed. This brings us to a fundamental rule of any data-driven organization:

Principle: Established rules and processes are the biggest hurdles for data-driven companies. A willingness to change and constant reflection enable innovation and sustainable growth.

The potential

The development towards a data-driven organization is therefore tantamount to a genuine transformation of all areas of the company. This requires investment – not only in new technologies and software, but also in the implementation and integration of solutions, in overarching process design and in training and change management.

Understandably, many companies are reluctant to take such measures. This makes it all the more important to compare the effort involved with the opportunities that companies have:

• Operational efficiency: Data-driven companies have the opportunity to quickly identify inefficiencies along their digital value chain and take appropriate measures to eliminate them.
• Innovation: The ability to react quickly combined with transparent product development processes and competitive intelligence enable shorter development and innovation cycles.
• Competitiveness: An innovative product strategy and targeted product communication make it easier for companies to assert themselves in the market – even under high competitive pressure.
• Resilience: Data-driven companies identify external risks such as market changes or more difficult economic conditions earlier and can more easily translate these into adjustments to their own business strategies. This makes them more resistant to crises and other external factors.
• Employee satisfaction: An often overlooked benefit of a data-driven organization is that employees are much better able to see how their work contributes to the success of the company.
• Business success: Those who continuously optimize their product, marketing and sales strategies on the basis of valid information have the best prospects for higher sales figures, greater margins and the development of completely new market potential.

How do you become a data-driven company?

But where to start? If you want to become a data-driven company, you face a number of fundamental challenges. In order to tackle these as effectively as possible, we list some best practices here that our many years of project experience have shown us:

• Commitment: A transformation of this kind needs the backing of management and all specialist departments. It requires a clear and overarching commitment to the project goals.
• Concept: Without proper planning, there is a high risk that companies will quickly get bogged down in such a comprehensive project. Those responsible must identify the central business and data processes, assign them to the various organizational areas, managers and system solutions and thus draw a transparent map with all technologies, processes and participants. On this basis, priorities can be defined – where is the greatest potential for optimization? Which quick wins can be tackled in order to get employees on board in the long term?
• Documentation: Centralized and transparent documentation of every project step, every decision and every success analysis is essential for the success of such a project. Learnings and best practices ensure increasingly successful sub-projects and those responsible ensure that no important requirements fall by the wayside.
• Market knowledge: Applications, software solutions and tools are a dime a dozen. At the same time, companies’ processes and requirements are so individual that there is no one-size-fits-all solution. This means that expertise is needed that can compare the individual company situation with the potential on the software market and make tailor-made recommendations. This applies in particular to central software applications such as ERP, PIM or CRM.
• Taking stock: Regularly comparing the current status with the project goals is important in order to monitor and optimize the effects of a software implementation in the long term. A tried and tested method is the so-called Return on Operational Technologies (ROOT) from the analyst firm The Group of Analysts (TGOA), to which we have dedicated an entire issue of The Latest Think!