I am talking a lot to different people in my domain – either on conferences or as I know them personally. One thing most of them have in common is one thing: frustration. But why are people working with data frustrated? Why do we see so many frustrated data scientists? Is it the complexity of the job on dealing with data or is it something else? My experience is clearly one thing: something else.
Why are people working with Data frustrated?
One pattern is very clear: most people I talk to that are frustrated with their job working in classical industries. Whenever I talk to people in the IT industry or in Startups, they seem to be very happy. This is largely in contrast to people working in “classical” industries or in consulting companies. There are several reasons to that:
- First, it is often about a lack of support within traditional companies. Processes are complex and employees work in that company for quite some time. Bringing in new people (or the cool data scientists) often creates frictions with the established employees of the company. Doing things different to how they used to be done isn’t well perceived by the established type of employees and they have the power and will to block any kind of innovation. The internal network they have can’t compete with any kind of data science magic.
- Second, data is difficult to grasp and organised in silos. Established companies often have an IT function as a cost center, so things were done or fixed on the fly. It was never really intended to dismantle those silos, as budgets were never reserved or made available in doing so. Even now, most companies don’t look into any kind of data governance to reduce their silos. Data quality isn’t a key aspect they strive for. The new kind of people – data scientists – are often “hunting” for data rather than working with the data.
- Third, the technology stack is heterogenous and legacy brings in a lot of frustration as well. This is very similar to the second point. Here, the issue is rather about not knowing how to get the data out of a system without a clear API rather than finding data at all.
- Fourth, everybody forgets about data engineers. Data Scientists sit alone and though they do have some skills in Python, they aren’t the ones operating a technology stack. Often, there is a mismatch between data scientists and data engineers in corporations.
- Fifth, legacy always kicks in. Mandatory regulatory reporting and finance reporting is often taking away resources from the organisation. You can’t just say: “Hey, I am not doing this report for the regulatory since I want to find some patterns in the behaviour of my customers”. Traditional industries are more heavy regulated than Startups or IT companies. This leads to data scientists being reused for standard reporting (not even self-service!). Then the answer often is: “This is not what I signed up for!”
- Sixth, Digitalisation and Data units are often created in order to show it to the shareholder report. There is no real need from the board for impact. Impact is driven from the business and the business knows how to do so. There won’t be significant growth at all but some growth with “doing it as usual”. (However, startups and companies changing the status quo will get this significant growth!)
- Seventh, Data scientists need to be in the business, whereas data engineers need to be in the IT department close to the IT systems. Period. However, Tribes need to be centrally steered.
How to overcome this frustration?
Basically, there is no fast cure available to this problem to reduce the frustrated data scientists. The field is still young, so confusion and wrong decisions outside of the IT industry is normal. Projects will fail, skilled people will leave and find new jobs. Over time, companies will get more and more mature in their journey and thus everything around data will become part of the established parts of a company. Just like controlling, marketing or any other function. It is yet to find its place and organisation type.