For many companies, data in some shape or form has been at the top of the agenda the last decade. Despite the large amount of attention on the data domain, many companies are yet to see the full range of expected benefits. One of the reasons for the missing impact is the lack of sufficient budget allocation to reach a fully professional data ecosystem.
Many senior executives face the same question: what is the value of our data ecosystem, and why do we even bother investing?
So how do we clearly communicate the value of data to the rest of the organisation, and how do we do that in a way everyone is familiar with?
In this article, we introduce a structured approach to the valuation of data. We have found inspiration in the private equity world where the ability to make good valuations of a range of assets is a core competency.
A note on data use cases
As you might have read in the previous article “A use case-driven approach to AI adoption”, we are keen to use data use cases throughout our work with data.
The emphasis on business value and being able to prioritise accordingly should steer our every move, and a simple method for achieving this is exactly what we will present in this article.
Indirect and direct effects of being data-driven
One of the reasons that many companies find it difficult to associate a specific value with investments in data infrastructure is the different levels on which being data-driven can increase efficiency of the organisation.
More specifically, we can derive value by looking at the relations between the raw insights (which may not create value in or of itself), through establishing a new mindset/culture and ultimately arriving at the business value creation – either top-line growth or cost reductions. Acknowledging that there are different types of value, and – more importantly – that insights do not necessarily generate value is the initial step towards our suggested route (see figure 1).