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DATA VISUALISATION: WHY SEEING IS BELIEVING


18 June 2019

Dave Naylor    Dave Naylor


Data visualisation… It’s just about making data look good, right? Wrong. Dave Naylor, our Chief Technical Officer, believes it’s much more than that. How you visually output data can make or break whether the data influences strategic business decisions.


Data visualisation emerged as a key discussion point when I chaired a roundtable recently on ‘Real World Analytics’ at Think Data Summit. We know it can be challenging for clients to find the best solution, but working closely with an agency is the best approach. That way you’ll get something that’s properly considered and fit for purpose for everyone involved.

Have a look at our bitesize video for a quick sixty second run down.

 

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From charts and infographics, to monthly health reports and real-time dashboards, there are lots of ways to visualise data, but let’s not get ahead of ourselves by jumping straight to the output. First thing’s first, it needs to be considered upfront at the briefing stage. It shouldn’t be a bolt-on at the end of the process.

What do you want to get out of the data? What are the KPIs of the research team? This could be response rates to surveys, % positive brand feedback, or most popular discussion topics. And how does this feed into the wider plan, at board level, what’s the ultimate goal or business objective? Perhaps increased revenue, brand awareness, footfall, basket spend or even predictive AI driven analytics.

We find that all clients have slightly different requirements, but the more we know at the briefing stage, the better we’re able to advise. There isn’t a one size fits all solution for data visualisation, it could be really simple, or very complex.

In fact, sometimes it’s better to keep it simple. When you’re thinking about set up, it’s tempting to go down the all singing all dancing route, but you may end up with something that’s so complicated it’s off-putting and confusing to use. Focus on the ‘must haves’ to get up and running, make sure user-adoption is good, that the outputs are useful, and then build from there. Trust me, if technology platforms are too complicated there’s a risk that they inhibit the process and even slow it down, which is the opposite of what we want.

Every research team is different, some want to see topline info in a standardised format, and others want to access the detail so they can filter and manipulate it in different ways. A good data visualisation tool allows you to do all this within the platform. No more exporting data into excel and working with it offline. Once you take data out of the central platform it becomes disconnected, out of date, no longer part of the whole.

[Data visualisation dashboard showing real-time statistics for an insight community.]

You need a single source of truth. A shared repository where the data lives and breathes. It needs to work in a way that clearly benefits the people who are using it. This allows us to go way beyond functional to become an enabler, helping research teams to understand and act on those precious insights quickly and easily.

The quicker the data can be outputted and disseminated to the right people, the more likely it will have an impact. Bombarding board level stakeholders with too much data and stats is not helpful, but not giving them enough, or sitting on the data for too long can be just as detrimental. All that hard work has gone into setting the research briefs and collecting the data, you don’t want to fall at the final hurdle.

That’s why the briefing stage is so important, always come back to what the objectives are. What do key stakeholders want to see? Set up the data visualisation to automate hourly, daily, weekly, or monthly as required. Some data can turn stale if you don’t act on it straight away. Cut out as much manual labour as possible and let the platform do the hard work for you.

Any data visualisation tool will live or die based on the quality of the data pipeline. This was another key take away from the Think Data Summit. Everyone involved in the roundtable discussion agreed that a robust, secure and predictable data transformation pipeline is the key building block for any visualisation tool. We must start with this and have data governance and clear data lineage built in from day one.

In today’s fast-paced environment, businesses need to collect, understand and act on data more quickly than ever before. If decision makers see something of interest, something that could put them ahead of the curve with competitors, they may release more research budget or help to steer the direction of follow up projects. The quicker and more seamless the process becomes, the more they will start to relate to the data in a tangible and helpful way, embedding the value of consumer research across the wider business.

As CTO, I view technology as an enabler for us and for our clients. It should make people’s lives easier not harder. A good tech stack is the foundation of a good overall workflow but each part needs to work seamlessly together. I read many articles and have listened to many talks around ‘digital transformation’. Myself and many of my peers view the 4th Industrial Revolution as a ‘data transformation’. As a group we put ‘data driven strategy’ at heart of what we do, and we’re passionate about delivering on this every day. Data visualisation forms part of the 360-degree life cycle of a project and can help to close the feedback loop for new and more targeted briefs.

Yes, data visualisation is about making data look good, but it’s a lot more than that too. If it’s considered upfront it can have a massive impact on how insights are disseminated, received and acted upon throughout the wider business. Seeing is believing, so be sure to make it a bigger focus on your next project.

Get in touch if you want to learn more about Data Visualisation.