Developing a data culture when talking is harder

March 18, 2020

Developing a data culture is no easy task. Having a culture where people look to the data to understand what’s going on is incredibly useful no matter whether it’s to address big-picture questions or the minutiae of individual situations. And typically organisations have a lot more data than they expect, so it’s quite realistic to assume that there is probably data that could answer a lot of questions, if only they have a way to get the answers.

I see a number of steps involved in establishing a data culture. Some involve technical hurdles and many are more directly cultural. It’s one thing to be able to demonstrate the quality of some data, but it can be quite another to have people understand and appreciate said quality. Being able to act on data first means knowing you have it.

As consultants (here at LobsterPot Solutions) who help our customers on the journey of developing data culture, we like to work from their sites, and make ourselves available for answering questions about the data. Ultimately, we want our customers to be able to get the answers themselves. Tools like Power BI can help this become a reality. But before they reach that point, it’s good to have them ask those questions of us. Not only does it help clue us up as to the things that we need to include in data models, but it helps demonstrate that they’re thinking about data. That their data culture coming along.

This willingness to ask questions has to filter through a large portion of any organisation trying to establish a data culture. It’s great to have senior management understand the significance of data, but if the people under them aren’t also making that same shift, then there’s trouble coming. What happens if the middle-manager reports up to senior management using an incorrect version of data that the senior manager can see. Having a ‘single point of truth’ for data is very important.

While they’re still on the journey to being able to get the answers themselves, the single point of truth for data might often be the team of people who are developing the analytics system – often the team which my staff or I are helping. We’re very used to having our days consist of the technical and design work that we’re doing towards a self-service reporting environment, as well as workshops and training we’re doing to develop the culture, and also a long list of practical questions about the data. The same questions that will get answered via the models we’re creating, but which are more time-critical and get answered in other ways – queries against a model or cube or warehouse, or maybe even against the operational system (taking care to avoid any kind of impact). These questions are important. Turning these people away prevents the development of a data culture, because their curiosity is extinguished. Their appetite for the answers is dampened, not whetted. I try to encourage everyone to have access to data, even if it’s through me.

Except that I’m not always there. And in a time when an increasing number of people are needing to work from home, maybe there’s nobody physically in the office who is working on the data.

Many organisations have a culture of asking each other questions via email, Teams, Slack, or whatever. But one of the biggest impacts I have is by being physically present, so that people ask who I am, find out that I’m not scary, and start to talk about the data. A lot of the culture around asking data-related questions is knowing how to ask those questions, so being physically present helps.

Suddenly this month, it feels like everyone is working from home.

Australia has reacted quite late compared to many countries, but is now banning large non-essential gatherings, and it wouldn’t surprise me if the restrictions only becomes more severe as time goes on until a solution is found. Organisations across the world are putting out their own advice, and telling people to work from home if they can.

Having teams spread out makes all kinds of communication harder, including those questions about data that I consider so important. If I’m not physically there, are people still wondering the same questions but not getting the answers? At what point do they simply stop wondering?

For me, I think this comes down to prompting the conversation about data. Find ways to tell stories about their data, and start communicating it – emails, SSRS subscriptions, whatever works, to get information out there to pique interest. With a standing invite to ask more questions. This can be put down to educating people about the opportunity with data, or to validate assumptions (this one is particularly useful because if you’re asking someone for help with it, then they’re more likely to look deeper and start to think about the potential), or to push the data through a “Quick Insights” tool to come up with ‘Did you know…?’-style trivia. The more the data can be discussed and be interesting, the more a culture around understanding data thrive.

Pretty soon, conversation should be able to flow more easily even if you’re not there, but communication channels need to be established. Self-service is great, but ideally you can make some sort of a forum for people to share their nuggets of insight. It’s the kind of thing that someone can share with a colleague in a “Hey – check this out” context. It demonstrates excitement about what they’re discovering. It demonstrates that they understand the business that they’re in, what the organisation does, and hopefully where there’s an opportunity for improvement. When people can’t walk up to each other in the same way, an effort might be needed to make this happen – Slack conversations, Team channels, whatever works to develop and maintain social interaction. Then find ways to incorporate data nuggets into the mix.

A data culture doesn’t have to be hard to instigate. It might require some work on the data itself, including data cleansing, modelling, and tools to let people get at the insight. But the key is to let interest in the data thrive, with enthusiasm firing, and to make sure there are ways to let that continue. Find ways to encourage conversation for the sake of your team, and for the sake of the data culture, find ways to encourage conversation about the data as well.


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