When it comes to getting in shape, you don’t merely go to a gym for a month, get up to speed and stay fit forever. We all know that if you want to continue being healthy, you have to keep exercising or you’ll go back to being a couch potato.
Oddly enough, a similar problem is facing companies today. With the arrival of AI, machine learning and automated everything, everyone is talking about data readiness. We constantly hear that we need to have clean, reliable and available data to reap the benefits of the new technologies.
This is often called data readiness, and while it can be a complicated topic, it’s good to understand where it comes from. Traditionally, when data scientists had a question, they would go out and get the data they needed to answer it. That way, they would be certain that the data was in a format that was useful. Today, companies are often collecting a lot of data without a question in mind. Then when they do want to gain insight from it, they have to get their data “ready” for analysis. This can be something as simple as removing duplicate records or as complex as reformatting it entirely.
Unfortunately, many brands are treating this as a one-off technology problem. They purchase the right tech, acquire the right data, clean it up and make it available. But then something gets lost in translation. According to a recent study, 99 percent of marketing leaders believe data is critically important but 62 percent are unable to turn it into insights or action.
Part of the problem is that the way we look at data readiness is misleading. In the first place, getting the most from your data is not a one-and-done job. It’s not solely a technology problem. Data readiness is a process, a system, a culture. It’s a mindset that makes a conscious effort to get the whole organization to respect the value of using data and creating true, data-driven processes.
This might seem like an overwhelming task, but there are three basic steps to move toward a more data-ready organization.
Recognize that reporting is not analytics
This is an essential first step. Most organizations have reports, which are valuable. However, reporting only answers the question, “How are we doing?” Analytics answers the question, “What did we learn?” The former is essential to running your business; the latter is where you gain real value from data.
For example, a media performance report for a hotel tells us how an ad campaign has performed. It shows us how many people opened an email, how many clicked on a call to action and how many additional rooms were booked. That’s good to know, but it doesn’t inform business decision-making beyond whether to do another campaign or not.
Analytics, on the other hand, might uncover the insight that most hotel bookings are made three weeks before the actual stay. Now we have something with which we can work. That can tell us when to start spending if we know we have a soft patch in the calendar. We also know when to begin advertising for Labor Day and so on. An insight enables you to adjust strategy and tactics, it does not merely provide you with information.
Operationalize insights
In our new data-driven world, we often hear about actionable insights. But as we just saw, true insights call on you to change your approach. They might suggest that you should focus more on mobile or Amazon, that customers prefer email to notifications or that they eat out more on Tuesdays than Wednesdays.
One issue with these insights is that they are fleeting in most organizations. We get insights during a campaign, apply them once and then forget them forever.
The solution to this is to create a central repository for strategic insights from all parts of a business. Brands should organize this information, socialize it and then institute a way to bring it into daily processes. That way, all of your channels and partners can work off the same knowledge base and get long-term value from your analytics.
Put the ‘action’ into actionable
Almost all insights are theoretically actionable. But as we saw, most organizations are not able to turn them into action.
This generally boils down to a lack of planning. Imagine, for example, that you are running a campaign for a company selling video content subscriptions. The content comes in three categories: movies, nature and sports. The brand’s existing customers are split roughly across these three types, so you create a campaign and institute a media buy that treats all of them equally.
A few weeks into the campaign, however, you learn that sports is the only one of your categories driving subscriptions. This is a powerful insight and should lead you to change direction. However, you had a limited budget and spent all of it getting the campaign to market. You now have nothing left to tweak the creative or the targeting.
Avoiding such an outcome means throwing traditional thinking overboard and getting serious about coordination and planning for change. Brands have to invest in organizational alignment and training to be able to respond to actionable insights when an opportunity presents itself.
We all need to start realizing that data readiness is not merely an IT problem. Using data insights requires us to think, plan and execute differently across every channel. Otherwise, we’re only solving for one moment in time rather than settling in for the marathon endeavor that real data readiness is.
