In this blog, I want to look at how a poor data culture can impact on staff in your organisation. Most of the articles I write focus on what you need to do to implement data governance, but I had such a great response to my post on why the data governance business case is so hard to get approved, that I thought it was worth delving a little bit more into the topic of poor data culture. This could also be described as a lack of data literacy in your organisation.

All too often I come across organisations who have a very poor data culture. By that, I mean, that they don't really think about data at all. I see this improving all the time but awareness levels are still low. Some industries (those where they make, move and sell things) I can understand that perhaps the focus is on the thing that is being made, moved or sold and less about the data around these processes. However, for many service industries like financial services the products aren't tangible. The products don't exist in the real world, they are only data and yet many such organisations still suffer from a poor data culture.

It's absolutely vital that we get everyone in our organisations, whatever sector we work in, to start thinking about data, and the impact that poor data is having on our organisation. So in this blog I want to consider the impact a little more.

In my last blog, I advised you to look for your data quality issues. Maybe you found examples of where poor data quality has caused a loss. Identifying a measurable cost to your organisation is fantastic, but if you found examples of poor data culture how are you going to measure that? To be honest, it’s not something that often gets mentioned in a business case.

I can't tell you how many times over the years I've had people tell me that part of their job is to fix and cleanse data. I can think of one instance in particular when a student actuary was spending two weeks every quarter, cleansing and fixing a spreadsheet before it could be loaded into one of their complicated actuarial models.

I was aghast that he was wasting eight weeks every year fixing data in a spreadsheet. This person had been doing the role for 18 months and had been told that this cleansing and fixing the data in the spreadsheet was part of the process that had to be followed! Actuaries are very intelligent and possess impressive analytical and statistical skills. Do you think it's good value for them to be removing duplicates from spreadsheets or reformatting data in spreadsheets? I certainly don't.

This is just one example but I think it's fair to say that there are probably intelligent individuals doing monotonous routine tasks like this in most organisations What impact is this having? You have a company not able to fully benefit from these skills and added to that these individuals are going to get disenchanted with the role and be less productive or even worse may look to move to another organisation.

Sadly I see this on so many occasions across all sectors and business areas. Where individuals tell you that there's no point telling you about their data quality issues, because they've been there forever and nobody is ever going to fix them. This defeatist attitude not only creates a poor data culture, but soon impacts the culture of the whole of organisation.

Think how much more engaged and efficient your staff would be if they didn't have to fix broken data or poor quality data before they could do their ‘real job’.

I've come across loads of similar examples over the years, but I was keen to see if other people’s experiences were similar. I asked for input on LinkedIn earlier this month and had some great responses.

Obviously, this blog has to be a digestible length so I'm not able to include all of the examples but I wanted to share with you some of the impacts on culture that were disclosed. Don't forget to keep reading to the end because I've saved the best/worst one for last!

Many examples raised the common issue of a culture of tactical or short term fixes that create data issues and build or reinforce data silos. This means that organisations are then not able to take advantage of new technologies to use that data, some people shared examples of investment having been made in Artificial Intelligence or Machine Learning Tools to then find that the data wasn’t good enough to use them.

One example mentioned the care sector, one with a heavy dependency on people but which doesn’t take the time to train them in the importance of data. This results in well intentioned people but poor quality and poorly managed data. The management then can’t rely on that data so seek workarounds, perpetuating the poor data culture, increasing inefficiencies and increasing staff turnover.

And the final example is a sad but excellent example of what can happen if you neglect your data culture. Someone shared that a 3 year regulatory reporting project involving approximately 100 people and significant investment in technology had failed because of a lack of data culture. Data analysis and data quality had been de-scoped from the project and the end solution would not work because of poor data quality. The person described the situation as like trying to make a chocolate cake without any chocolate – an excellent analogy. This culture took its toll and the individual concerned ended up resigning taking their valuable skills to another organisation.

Please take these examples as a useful warning. It does not have to be this way, if you get it right a good data culture will empower your organisation to see data as an asset and managing data as an asset will enable you to use data to focus on and deliver value.

One of the respondents, to my request for input to this article, summed up the situation nicely:

Leaders need to work to create an environment that is conducive to a behavioural shift and that is what a good data culture does. It is the foundation of successful change.

Helping you improve the data culture at your organisation is a key part of the Data Governance Journey that Alex Leigh and I have created together, you can find out more about that here.