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Data dashboards: 5 practical tips

A recurring theme of the recent London Digital Transformation Conference was the importance of utilising data to make objective business decisions. In this article, we share some of the tips and advice we have learned by helping countless clients to harvest, visualise and utilise their business data to make better decisions.

Huddle recently attended the Digital Transformation Conference where there were some fantastic speakers from the Post Office, Nationwide, Marie Curie and Kingfisher group, among many others.

A recurring theme was the importance of having accurate and digestible data to inform key business decisions. In today’s data-driven world, this may seem obvious, however there are still many businesses who don’t have a clear idea of the data that is driving their business, and how this can inform key strategic decisions.

This inspired us to summarise 5 tips and insights we have gleaned from our experience enabling clients to use data to prioritise and understand what’s really going on both with their team and their customers.

After all – a decision without data is subjective guesswork, and data dashboards offer myriad benefits: they enable you to make strategic use of the facts, build resilience, plan for the future, celebrate successes and reinforce an ‘outcomes over outputs’ mindset.

1. Start with the outcome first, not the data sources

When trying to do this yourself, many guides will teach you to list out your data sources first in order to know what platforms you are working with, and start to make a plan to unify and present the data in one place.

However, in our experience this approach encourages you to focus on far too much data, collecting all the data together in one place, before actually deciding which of the data is actually important to you. It often means you spend time connecting up data sources which provide little to no value.

Instead, start with the outcomes first. What metrics do you want to see on the dashboard? Which numbers are actually valuable to you and your stakeholders? Which data is just for vanity and isn’t actually a sign of real progress? Get your assertive hat on and question stakeholders who ask for specific metrics – why do they need that?

Try to get as few metrics as possible on the dashboard – people can only focus on so much. Once you have the final list of metrics, then look at ways in which you can get that data in one place using tools like Databox or Looker Studio. If you need to add more later, that’s fine, but keep the dashboard focused and streamlined for now.

2. We prioritise that which is measured

There is another effect of measuring data, too: it will influence the direction of work across your entire team.

By carefully selecting the metrics that will show on your data dashboards, and focusing key stakeholders on those metrics, you are implicitly telling the team that those metrics are more important than others. Since those are the metrics that will be used to measure success, those are the metrics that people will focus on (of course).

If you’ve ever forgotten to track your activity when going out for a run and found yourself putting in less effort as a result, you’ll know that it is human nature to prioritise that which is measured, and deprioritise that which is not.

So choose wisely – not only are you giving visibility to data to senior stakeholders, you are influencing the behaviour and priorities of tactical teams as well.

3. Both numbers and feelings are important

Both quantitative data (tangible numbers and figures) and qualitative data (more freeform text insights) are valuable and can lead to useful insights.

However, it can be tempting to focus solely on quantitative data – because it is much easier to present in a data dashboard, using numbers or charts. This means qualitative data is often overlooked.

Advances in AI can really open up qualitative data and make it much easier to scan and analyse long form text for key insights. Try giving ChatGPT some text-heavy data and asking it for insights, or use a specialised tool like MonkeyLearn.

4. Stakeholders need a story, not raw data

It can be tempting to set up a real-time Looker Studio dashboard (for example), share it with stakeholders, and then call it a day. After all, everyone now has access to all the key data any time they want, don’t they?

How many of us have felt decision paralysis when faced with the sheer amount of thumbnails on Netflix? It can sometimes feel impossible to make a decision. In contrast, when going to the cinema there are only a handful of options on any given day – meaning it’s much easier to focus, compare, and decide how to take action.

It’s the same with business data – sharing all data at all times with no direction means you will likely find little to no engagement with the data. When you have access to everything everywhere all at once, it can be easy to see little to no value in any of it.

If you are looking for buy-in and investment from stakeholders, you need to use the data to tell the ongoing story of what is happening within the business – the truth behind the numbers.

A good story is not experienced by gaining access to a database of information on the characters, setting, and plot points, and figuring it out yourself.

It needs to be told carefully, in chapters as it unfolds, with emphasis placed on the more meaningful and relevant aspects of what’s going on.

5. Often, the true story is not the one you expected

It is common for a team to think they know intuitively where they are doing well and where improvements are needed, but to then completely change their thinking when presented with the data.

That’s okay! A common result of measuring data and using it to make decisions is that you will often discover things that you didn’t expect. This can mean you are in a position where you need to make a decision that goes against your team’s conventional wisdom i.e. habits formed in your comfort zone.

The important thing here is to put egos aside, listen to the data, try something new, and then continue to measure and iterate.

This last step is crucial – the first time you listen to the data it may not be the silver bullet your stakeholders are expecting, and that’s not the point of this. The point is to keep learning, keep refining, until you are a fluid flexible team that can adapt and respond to data in an agile manner.

By Tom Parson