Digitalisation is a key driver amongst companies since the last 2 years. However, many companies forget that the oil for the digitalisation engine is data. Most companies have no data strategy in place or at least it is very blurry. A lot of digitalisation strategies fail, which is often due to the lack of proper treatment and management of their data. In this blog post, I will write about the most common errors I saw so far in my experience. Disclaimer: I won’t offer answers as of now, but it is relevant to give you an insight into what you should probably avoid doing šŸ™‚

Step 1: Hire Data Scientists. Really: you need them

Being a Data Scientist is a damn sexy job. It is even considered to be the most sexy job of the 21st century. So why should you not have one? Or two or three? Don’t worry – just hire them. They do the magic and solve almost all of your problems around data. Just don’t think about it, just do it. If you have no Data Scientist for your digitalisation strategy, it isn’t complete. Think about what they can or should do later.

In my experience, this happend a lot in the last years. Only few industries (e.g. banking) have experience with them, as it is natural for them. Over the last years I saw Data Scientists joining companies without a clear strategy. These Data Scientists then had to deal with severe issues:

  • Lack of data availability. Often, they have issues getting to the data. Long processes, siloed systems and commodity systems prevent them from doing so.
  • Poor data quality. Once they get to the data and want to start doing things with it, it becomes even more complex: no governance, no description of the data, poor overall quality.

So, what most companies are often missing out on is the counterpart each data scientist needs: a Data Engineer. Without them, they are often nothing.

But with this, I described actually a status which is almost advanced; often, companies hire data scientists (at high salaries!) and then let them just do BI tasks like reporting. I saw this often and people got frustrated. Frustration led to them leaving the jobs just after some months. The company had no learnings after that and no business benefits. So it clearly failed.

Step 2: Deliver & Work in silence. Let nobody know what you are doing

Digitalisation is dangerous and disruptive. It will lead to major changes in companies. This is a fact, not fiction. And you don’t need science to figure that out. So why should you talk about it? Just do it, let other units continue doing their job and don’t disrupt them.

Digitalisation is a complex topic and humans by nature tend to interpret. Also, they will start to interpret things from this topic to fit to their comfort zone. This will lead to different strategies and approaches, creating even more failed projects and a lot of uncertainty.

The approach here should be to be consistent about communication within the company and to take away fear from different units. Digitalisation is by nature disruptive, but do it with the people, not against them!

Step 3: Build even more silos

Step 2 will most likely lead to different silos. A digital company should be capable of doing and solving their digital products, services and solutions on their own. There is always a high threat that different business units will create data silos. This leads to the fact that there will never be a holistic view on all of your data. The integration is though later on and will burn a lot of money. For businesses, it is often a quick win to implement the one or another solution, but backwards integration of these solutions – especially when it comes to data – is very tricky.

A lot of companies have no 360 degree view of their data. This is due to the mere fact that business units often confront IT departments with “we need this tool now, please integrate”. This leads to issues, since IT departments are anyway often understaffed. So, a swamp in the IT landscape is created, leading to an even bigger swamp of data. Integration then never really happens as it is too expensive. Will you become digital with this? Clearly no.

Step 4: Build a sophisticated structure when the company isn’t sophisticated with this topic yet.

Data Scientists tend to sit in business units. For a data driven enterprise, this is exactly how it should be. However, only a small percentage of companies are data driven. I would argue that traditional companies aren’t data driven, only the Facebooks, Googles and Amazons of our world are.

However, traditional companies now tend towards copying this system and Business units hire data scientists – which are then disconnected to other units and only loosely connected via internal communities. A distributed layout of your company in terms of data only makes sense once the company reached a high level of maturity. In my opinion, it needs to be steered from a central unit first. Once the maturity is going to improve, it can be step-wise decentralised and then put back fully into business units.

One thing: put digitalisation very close to the CEO of the company. It needs to have some fire power as there will always be obstacles.

In my experience, I’ve seen quite a lot of failures when it comes to where to place data units. In my opinion, it only makes sense in a technical unit or – if available – in the digitalisation unit. However, it should never be in business functions.

Step 5: Don’t invest into people

Last but not least, never invest into people. Especially Data Scientists – they should be really happy to have a job with you, so why would you also invest into them and give them education?

This is also one challenge I see a lot in companies. They simply don’t treat their employees well, and those that are under high demand (like Data Scientists) tend to leave fast then. This is one of the key failures in Data driven strategies. Keeping the people is a key to a successful strategy and a lot of companies don’t manage this well.

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