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Simplify and Strengthen Your Approach to Data Visualization

Rolf Olsen, Chief Data Officer, Mindshare
Rolf Olsen, Chief Data Officer, Mindshare

Rolf Olsen, Chief Data Officer, Mindshare

Yes, I once sat through a 212-page data-driven, bar graph-riddled deck from a vendor and lived to tell the tale. Barely. That’s one of the many reasons why I now spend most of my time challenging assumptions of how to visually share data with our clients and our own people.

Data Visualization has the opportunity to go beyond how we digest a piece of information and become a business strategy game-changer. We live in a world where speed matters. When we shorten the time from insight to action we can exploit market opportunities in the most agile fashion. In short, speed equates to growth.

Let’s start by accepting we have become a colossally inattentive lot: If we haven’t nailed the crux of a problem and solution in the first 10 slides–or 10 slides total–we’re not delivering what’s possible and what’s critical to know.

We come to every project with well-established habits and biases for how to present information–mostly based on how it was done last time. Too often, this has come to represent snapshots in time–often looking backwards—versus real-time influencers of how to run your business tomorrow. By adding live data to models we can look forward, introducing fast-moving variables that not only show why you should care but also what you can do about it.

Think context and color.

The best data visualization centers on two things: Context and Color. Context boils down to one question: “Why should I care?” The effective use of color makes context come alive. What would be  simple, universal example of this? Red, yellow, green. We know red means stop and green means go.

  ​The best data visualization centers on two things: Context and Color.   

While there is beauty in the simplicity of this solution–and one I use often–there’s so much more we can do. Some early inspiration came from David McCandless’ “Information Is Beautiful”, where he  explains how context and color work together and why we crave it. Our eyes subconsciously take in massive amounts of information and they’re incredibly sensitive to variations of color, shape and pattern. Data visualization combines the language of the eye with the language of the mind, which is about words and numbers and concepts, so you start speaking two languages simultaneously, each enhancing the other.

It’s no wonder, given all the data we’re asked to digest, we’ll stop dead in our tracks to stare at something that artfully and thoughtfully combines these languages for us. So are you ready to kick the bar chart to the curb? Here’s how to start to change your company’s approach to sharing and acting on data:

1. Own your frustration.

That’s your secret trump card. Deep down, we all know we’re getting too many reports. Use that to your advantage. Make it the trigger for change. If there is nothing interesting in that detail, nothing new, why belabor it? Stop talking about slides that don’t matter. Less is more.

2. Gain the trust of your peers.

Understand that even though everyone hates lifeless, rote reporting, people naturally hate change, too. Get all of the stakeholders on board by showing the benefit of scrubbing out reporting biases. While YOY is good for reporting mortality rates, for example, there’s probably a better way to show what’s really going on with the business, and ultimately if we should take action or not.

During this process, you’ll hear people say, “This is how we always do it” or “This is how the boss likes it”. lf you have a cleaner, more compellingly visual solution, won’t they want that more?  For one company we threw away the rule book, which constituted of a lot of siloed reports, and created an easy to digest scorecard which made this much simpler to take action across a whole portfolio of brands and associated metrics. Instead we now share it in a way that has become the corner stone of an agile business practice.

Again we used color in two different ways to help elevate what it is:

3. Put different skills sets together.

The best data visualization is the work of left-brain and right-brain people. The unicorn who has both rarely exists. Most of us are hardwired in one direction or another, which is why you’ll be most successful working in teams.

A visual designer can be clearer and more cohesive in how best to represent data, while an analyst can simultaneously focus on the technical aspects of the assignment. By blending these skills together, you can create true storytelling that is concise and engaging and avoid the drudgery of reports and death by PowerPoint.

4. Start small. Stay small.

Don’t overreach. Get a small team of the right people on board. Probe them for a wish list of new concepts they’d like to explore. Ask questions: “What’s the reason for this color combo? Be vocal: “I don’t like this chart. Do you?” Convey to everyone that we’re ultimately going to slim down what we need regularly. The rest of the information will exist somewhere in case we need it down the road, but it’s not going to be the focus. It’s called an addendum.

For another company, we had a very in-depth analytics engagement and this would often result in a 100+ page deck. We evolved the focus to split the deliverables into three clear outcomes:

• An infographic focused on key learnings, outtakes, and actions
• A workbook that has all of the detail for that detail-hungry individual
• A scenario planner that makes it easy to optimize plans moving forward

Creating this process makes it easier for people to engage and really get to the meat of the insights and what it means to the business. Yet it still satisfies key stakeholders in case they want more detail. We layer it so people at all levels of the organization can look at just the right amount of data, told in a way that telegraphs what to do next.

And even though this sounds complicated, it’s not–and it takes less time to produce so you get better answers faster, which is important as speed means money!

Ultimately, it’s about knowing your audiences. How do you meet their different needs with visually-driven data that answers three simple questions: What am I learning I didn’t know before? What are the actions I can take? And, what are my next steps?

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