With all the talk about big data, one thing is very clear: the vast majority of us have very little insight into how to actually find insight in it. We have neither strategies nor experience, mostly because the use of data at scale is relatively new. Sure, humans have been using data for thousands of years to tell stories, pass along ideas, record history and, in more modern times, produce ROI and eliminate functions with high spend and low return.
But, big data is unlike data of the past. Not necessarily in its use. Data certainly still tells stories, passes along ideas, records history, produces ROI and saves money. But its sheer size makes it completely different than any data set humanity has managed to date.
We are no longer working with a Rosetta Stone size of information (neither the actual stone nor the modern day disks), or even spreadsheets of data that can in turn be put into a semi-useful pivot chart. We are dealing with massive, and I repeat massive, scales of data. Umbel’s Digital Genome alone collects, analyzes and visualizes 18,446,744,073,709,600,000 data points per person in less than one second.
That’s 18 billion, billion ― a tough number to even process, let alone find insights within it, and then take action based on those findings. The ability, or ease, to do so is what is called data literacy. It affects us all, and will be the most important driver of business success over the next decades.
However, with the exception of data scientists, most of us today are woefully data illiterate. To increase data literacy, we can wait for schools and universities to implement new programs that will educate youth on the use, meaning and impact of big data on business functions. Many universities are already doing this. Yet, big data is a disruptor that waits for no one, and relying on the next generation to take its reins will leave us far behind any competitors that embrace data literacy now.
A more proactive option is to invest in intuitive technologies that can simplify the vast complexities of data for the regular business user. These new technologies “bake in” a data scientists understanding of where to begin and what strategies to apply. So executives and marketing leaders can focus on how to take informed, decisive action – without worrying about algorithms and data sets.
Visualizations are key here, as are layers of actionability. Take Google Analytics. Google Analytics is a fantastic visualization platform. Digital businesses of all sizes use the platform to visualize site traffic, referrers and the like. The result is a better understanding of their audience, their behavior and how to potentially affect said behavior based on traffic source, visitor behavior and drop off points.
Of course you can’t take any action from Google Analytics itself. You can find some great insights or find questions on which to base A/B tests. But you cannot take action on your findings within the platform itself. And that’s fine. It’s useful for the insights – but it does not help to increase data literacy across all departments and teams.
Let’s look at the other side of the coin: Krux is a great tool when it comes to actionability. The platform allows you to segment users and then send out ads to those segments. This can provide significant CPM lift, as well as increase page views and engagement. Yet Krux lacks transparency and visibility. It works like a spreadsheet and those oh-so-infamous pivot charts. So finding unknown adjacencies for those with low data literacy is difficult. You just don’t know where to start, and after the ad tech use case, your ability to further leverage consumer data is pretty limited.
To increase data literacy across an entire organization, you need something often referred to as data democratization. That is, a data platform that works equally well for the marketing, sales and editorial teams. Each of these departments have varying needs of actionability, and to find the proper points of interest on which to act, each of them needs varying points of visualization.
For editorial teams, tagging systems work well. Where are people clicking on site and how do these clicks correlate to the content they just consumed or are about to consume? How are they consuming it (mobile, tablet, web)? How can we use those insights to increase overall engagement that will drive higher click-through rates or, even better, find us more relevant branded content sponsors?
For marketing teams, customer or user affinity visualizations work well. What other brands does our audience like and follow online? How big are their social circles? What are their demographics? How does that compare to another segment of our audience who likes completely different brands or competitors? How can we use that information to increase brand awareness and conversion making our site feel personalized and responsive to the interests and desires our audience no matter who they are?
For sales teams, a combination of the above is extremely helpful. Who is our audience and what are their other interests? What do they click on when they come to our site? Where are they lingering and what is making them bounce? How can I use that information to formulate a personalized outreach program for a prospect that recognizes their problem and positions me as their solution?
These departments are not your technical teams, but they have some very technical needs. A lack of data literacy is hindering their ability to use big data to truly revolutionize their current jobs. But, with the right tools, big data can become their favorite new coworker, an asset on which to rely in order to make them better at what they do every single day.
In the end, I’ll leave you with this: data literacy is built upon data democratization and the user experience. If a platform is difficult, it won’t be used. If a platform doesn’t serve all teams equally well, it will not be adopted across the organization. If a platform isn’t architected to bridge the gap between regular people and the data scientists creating the algorithms, then the era of data-driven anything will fail to materialize.