As of 2018, most large multinationals have started their journey to become a more data-driven organization, usually as part of their digital transformation. For most of them, it is also clear that starting a data journey requires funding: a team needs to be created and a data infrastructure needs to be available (cloud or on-premise). The first pilot projects will be selected, usually together with several of the business areas. If the pilot is successful, it will be put into production to obtain the data benefits on a structural basis. For instance, a churn pilot gets a customer dataset from the marketing department and predicts with Machine Learning techniques what customers are at risk of leaving the company, and then tries to retain them. The number of retained customers can be translated into retained revenues. Putting this into production means that the marketing dataset is provided every week or month, the algorithms are executed automatically and the result is fed into the appropriate marketing channels to reach out to the customers.
At the beginning of the journey, there is usually not too much discussion on who pays for what. The important thing is that things are happening and moving forward. But when the team grows, more pilots see the light and need to be put into production, questions about funding arise.
Should the corporation continue to invest in the team? Should the business pay all or part? If the corporation keeps funding it, should the business be charged? At what rate should the charge be? If the work involves a third-party company, who is paying for them? Moreover, multinationals are usually formed by different legal entities and doing things for “free” is not easy to handle from a tax and anti-competition perspective.
There are no unique answers to those questions, but what we can see is some patterns depending on the “data maturity” of the organizations. In general, as illustrated in Figure 1, corporate funding is available in the beginning, and over time, with increasing data maturity, central funding goes down, and business funding goes up. Usually, a small part of central funding remains to explore and test new, innovative technology and use cases.
A specific application of this funding strategy is that the corporation funds the central initiative for a few years so the businesses get used to it, and from a certain decision point, joint funding happens, as illustrated in Figure 2. The advantage of this joint funding model is that the corporation can still stimulate strategically relevant local investments, but businesses also need to invest, avoiding the pitfall that “gifts” are easily accepted but not put into practice.
Looking at the different stages of a data initiative: pilot, deployment, production, there are two main models where corporate funding diminishes over time, as illustrated in Figure 3 and Figure 4.
In earlier stages of the data journey, the corporation might fund the data initiative in pilot and deployment stage, and the business takes care of funding the production part (Figure 3).
However, it is more common that the corporation only funds the pilot part, which will be reusable among many businesses, whereas the deployment and the production part are fully funded by the business as they are business specific and not reusable (Figure 4). The latter strategy is also more acceptable from a tax perspective, keeping only the Group functions at the corporation.
As a creative funding approach, the corporation can use the availability of free data assets to stimulate the business to step up their efforts in the data journey by, for instance, investing in data quality, governance or adopting a centrally developed data model. This implies that the corporation continues to fund data initiatives, but businesses can only take advantage if they comply with the corporate data strategy and standards. This model is illustrated in Figure 5.
There are, however, also situations where the first funding comes from the business, and the corporation steps in at a later stage. This is the case when a leading business in the Group explores a data initiative on its own account, and the result is considered a best practice. In this case, the business has funded the pilot and the deployment, and then the corporation steps in to turn the successful initiative into an asset that can be reused (deployed and put in production) by the other businesses of the Group. This situation is illustrated in Figure 6.
Don’t miss out on a single post. Subscribe to LUCA Data Speaks.
