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There is high demand within the consultancy industry when it comes to offering services to help companies and organisations increase their digitalisation. However, the messages from the consultants providing these services are often fluffy and vague about "what" it means to become digital. In many cases, it primarily concerns helping the customer become a little more "efficient" and hardly that the customer becomes digital. And efficiency improvements are something all organisations have worked on throughout time. So what is new about this?
Let us take RPA (Robotic process automation) as a concrete example. Right now, we see a multitude of consultancy organisations offering knowledge and system support in the form of RPA to help customers become "digital". But fundamentally, RPA is about adding automation to manual routines and eliminating and simplifying steps in the customer's work processes. This thus creates both efficiency improvements and increased data quality. But does that still not mean that the customer becomes digital?
The same question can be related to the implementation of voice control or a so-called chatbot. This means adding technology that can save a lot of working time for the customer while significantly increasing the availability of service to the customer. Thus positive for both parties. But again, does that mean that the customer becomes digital?
The above should not be interpreted as objections against either RPA or other technology that can simplify the customer's processes. These technologies are welcome and will contribute to improvements. However, the fundamental problem remains that many of the underlying applications and databases are not developed for the changes we see ahead concerning data volumes and structures. They tend to build new technology on top but hesitate to "clean up" the existing landscape with old applications and awkward information flows.
Although it is positive that many organisations review their processes and work routines and try to find tools to become more efficient, we see a limitation in how far one can go with digitalisation without rebuilding the basement and foundation for their information management. Everything points to the information volumes we handle today being only a fraction of what we will need to manage in a few years. Beyond increased personalisation and the introduction of IoT, several organisations will want and need to manage data from more data sources, both internal and external, to gain access to a larger information base that provides opportunities for what is called predictive analytics. The sum of a fragmented landscape of old systems, the lack of a homogeneous data and information model, and the absence of a service-oriented architecture leads to difficult obstacles in reaching the next generation of system landscapes.
It is of course not necessary to become digital overnight. But looking at the rate of change now prevailing in business and the competition starting to come from previously completely unknown competitors in the market, there are substantial reasons for several organisations to review their current situation and make a plan for how to move to a future-proofed platform of applications. And then it is not enough to complement the IT environment with RPA and other tools. That is only a small step on the way.
