Research Technology (ResTech)

May 16, 2013

Are You Burning Away Your Data Fuel?

As data-driven approaches take organizations farther than ever before, the need for well-designed databases is more relevant than ever.

Patricio Pagani

by Patricio Pagani

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By Patricio Pagani

The ‘data is the new oil’ metaphor is overused these days. And it’s not particularly apt. After all, we aren’t drowning in an excess of fossil fuels, are we?

But if data is the fuel that powers your business, then we have a problem.

We’re setting fire to our fuel with the inefficiency of 19th century colonials in a Kauri forest.

If your data is oil, a well-designed database is your motor

jet engine 08 - graphic

Just like setting fire to our fuel can give us short-term warmth, a primitive approach with data management gives us haphazard results. We’re not seeing the fullest picture which our data could give us.

Five tell-tale signs that your organization is wasting its data fuel:

  1. You sit through meetings where oodles of meaningless data sprawls over endless PowerPoint bar charts.
  2. When the somnolent audience finally speaks up, it’s to question something which looks wrong.
  3. The speaker can’t explain the mistake.
  4. Information-sharing initiatives are launched, the quietly fade away…
  5. Management prefers to make decisions based on instinct, because using data-driven insight is just too hard.

For most of us, the word ‘database’ conjures images of the clunky interface of early versions of MS Access, mysterious acronyms and lines and lines of dull-as-dishwater raw data. Hearing the word makes people either go into a panic that they don’t have database skills, or switch off because it sounds out-moded.

Perhaps a better way of thinking of databases is as a well-organized storage facility or knowledge bank: an organized structure in which to store all that valuable data.

It’s curated by skilled data managers. You can dip into it whenever you want, and the data is ready to use when you do. In this ideal world, you won’t have to repeat a huge research study because you couldn’t access the study that your predecessor (or somebody in a different business unit) run. In this ideal world, all the research studies that your company has purchased over the years are available at your fingertips. And before you decide to ask to burn new fuel, you will definitely check that the tank is empty,

Locked in silos

world-of-data

Most large businesses have several agencies supplying them with consumer insight data, often in many styles. But marketers need to look at the bigger picture. Keeping your data-fuel in silos (be it PowerPoint decks of study results or even an Excel dashboard with prettily-formatted tables) doesn’t help marketers drive brands forward. It shouldn’t really matter to marketers where the insights come from. They are agency-neutral. By bringing the data together into a single knowledge bank, you can:

        Mine and compare consumer insights across studies from one country or many countries

        Find new patterns and relationships in your data

        Get more value from your existing research and save money because you don’t need to repeat old questions just because they aren’t accessible.

 Speak a common language

dataneversleeps1

But in order to be able to extract all that value from your investment in consumer insight, the first step must be to plan the way you want to look at the answers. We call this the ‘data architecture’. The best architecture for your data is one that is aligned with your business.

It’s tempting to organize data by source, or by the internal team who commissioned the project. But what if we organized it by the kinds of questions which marketers ask? For example, ‘Where are our greatest opportunity segments?’ or ‘What impact do our campaigns have on awareness?’.

Don’t settle for ‘researchey’ labels like ‘Grouped socio-economic class of HH’ on your variables. Take the time to align them with internal language. The outputs will look less intimidating to the non-researchers you want to engage.

Even more importantly, align the labels of common variables between studies. For example, you have a weekly behavior tracker and a monthly brand tracker. Wouldn’t it be great to overlay brand performance on purchase behavior? Net up the weeks into a new month variable in your behavior tracker in order to easily look at the two studies side-by-side.

Harness the power

harness

As data-driven approaches take organizations farther than ever before, the need for well-designed databases is more relevant than ever. If competitors are able to harness the power in their data fuel, then they will be the ones to go places with it, leaving us in the dust.

Let’s stop being gas-guzzlers, and start being disciplined data consumers. We’ll cover more distance when we use our fuel in a finely-tuned motor.

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The views, opinions, data, and methodologies expressed above are those of the contributor(s) and do not necessarily reflect or represent the official policies, positions, or beliefs of Greenbook.

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