Mar 06 2015
Big Data has been a buzzword for several years now, but the hype isn’t misplaced: we’re still only at the beginning of what this revolution in information will mean for business, for governments, and for society.
Nevertheless, the excitement about Big Data has often given the false impression that data is enough. “Make the data streams converge in one place,” the logic goes, “And we’ll have an ocean of knowledge.” True. But then what?
By now, most people understand that data is meaningless unless it serves a practical purpose. And finding a purpose for all your data takes a lot more than simply compiling a database. First, you need to be able to see it in a form that makes sense.
No matter how your company acquires its analytics, converting operations to a data-driven approach represents a significant investment of time and energy. When the data starts to pour in, it looks impressive, until you start tallying the return on all the work that went into getting it. Data is often transfixing, but it isn’t always useful. If you don’t know why you want your data, you can easily get stuck simply collecting it.
The Key to Efficient Analytics—Don’t Freeze in the Big Data Headlights
The solution to data paralysis, as well as the next step along the path of the Big Data revolution, is data visualization. Initially, dataviz seemed like a panacea for companies seeking to obtain more benefit from their analytics. Make the data visually friendly, we’re told, and the insights will spring naturally from the more intuitive presentation.
While that’s certainly true in some regards, it isn’t enough. Savvy business leaders exploring these tools quickly realized that dataviz has the same limits as Big Data: it doesn’t just work on its own. Dataviz demands engagement.
In other words, visualization is a means to an end, not an end in itself.
In order for data visualizations to be useful, business decision-makers need to adopt a specific mindset. Dataviz isn’t just about making vast amounts of information more easily and quickly consumable by the people who need it, it’s about initiating and catalyzing certain behaviours.
What key behaviour can data visualizations promote?
Asking questions and finding answers. When something isn’t working, employees with access to strong visualizations know they can start looking for solutions by interrogating the data.
How P&G Turned Data Visualization into Business Advantage
Among large retail brands that understand this principle, Proctor & Gamble is unquestionably leading the field in using powerful data visualizations to help their leaders and managers achieve better market penetration and improve end-to-end decision-making.
P&G understands the impact of Big Data. The company averages about 4 billion transactions daily, a figure that represents only a fraction of the amount of data that the company utilizes. Their basic goal in collecting all of this data is to make better decisions faster.
For that, P&G groups its data according to models that answer three key questions:
1) What’s happening?
No matter how good your data is, it’s meaningless if it isn’t available to the people who need it most. Decision-makers need to all see the same data at the same time in order to be effective. P&G demonstrates this principle by making its data accessible to more than 50,000 of its employees on its Decision Cockpits, a dashboard that supports a variety of trackers, business health monitors, and projection tools. These data visualizations give information about things like shipments, sales, and market share. Not only do shared analytics give employees the tools they need to move quickly and efficiently, they also eliminate the torrent of reporting that clogs so many schedules. “With the success of the decision cockpit, P&G has been able to do away with more than 80% of the company’s standardized business intelligence reports,” says Filippo Passerini, P&G’s Business Services Group President and CIO, in an article in Information Week. Of course, the most immediate benefit of accelerating all that reporting to real-time awareness is spotting exceptions as early as possible. Data visualizations are most effective for bringing anomalies to the fore and helping business leaders clearly see weak spots in the market.
2) Why is it happening?
When it comes to actually using Big Data and not simply looking at it, context is everything. Once the exception has been recognized, companies need to know why it’s happening. P&G uses data visualization models that allow decision-makers to zoom in on details at store and shelf level, or zoom out to region and territory overviews to get the bigger picture. Managers and executives explore this info in a futuristic boardroom environment, with giant screens wrapping around the room, which they call Business Spheres. Decision-makers interact with the data together on the wall and follow up on specific questions and ideas on their personal screens. That interactivity is key to making visualization useful. As data expert Michael Schrage wrote in the Harvard Business Review, “Until visualizers embrace the design imperative that their visualizations should be as much about facilitating interaction as conveying information, they’re doomed to be high-resolution underachievers.”
3) What can we do about it?
Data has authority. When all parties are interacting with the same data, decisions can be made much more quickly and easily. The next step is implementing those decisions. Insights derived from visualization models allow executives and marketing teams to understand things like the effectiveness of advertising and promotions. When properly conceived, these tools make it clear which levers to pull to effect real changes, from pricing to product assortment. To picture your company’s data along all three models of engagement—from accessibility to interactivity and finally to implementation— check out the Askuity retail intelligence platform.