Are you prepared for the next evolution of analytics? If not, now's the time to prepare for what's next and seize competitive advantage by being early in what's next.
Let's look at where analytics has been:
In the beginning, having any kind of data at all was an accomplishment. Veteran marketers remember the early days of server-based logs with tools such as AWStats and other CGI analytics tools. These were the days of descriptive analytics, the raw stuff itself. We focused heavily on data quality - was our data clean, chosen well, and compatible with our primitive analysis tools at the time? From that data, we drew some basic analysis - how much traffic did our website get? What was our email open rate?
Descriptive analytics answers the question: what happened?
Over time, tools have evolved from simply doing data dumps to helping us visualize data and begin to do real analysis. Today, most modern analytics tools such as Google Analytics and Tableau can help us understand not just what happened, but start to explain why. Why did traffic to the website go up 50% yesterday? Why did lead conversion drop like a rock last month? Analytics became diagnostic.
Diagnostic analytics answers the question: why did it happen?
The next generation of tools which are only beginning to be adopted now are predictive in nature, helping us to not only understand what happened in the past, but what could happen in the future. Tools such as IBM's Watson Analytics, Google Predictive API, Amazon Sagemaker, Facebook Prophet, and other machine learning technologies are just now enabling organizations and individuals to do valid, useful predictions from our data.
Predictive analytics answers the question: what's going to happen next?
What of the generations after prediction? Gartner, Inc. posits that the final generation of analytics is prescriptive, analytics that tell us what to do. With enough machine learning and artificial intelligence, analytics tools may recommend courses of action based on targeted patterns of the past and predicted outcomes. Wouldn't it be nice to load up our marketing analytics tool with data and see what our next month's marketing plan should be? Given the rate of change and progress in software development, the horizon for true prescriptive analytics is much closer than we think.
Prescriptive analytics answers the question: what should we do?
I believe there's a generation after prescriptive. The last generation of analytics is the proactive generation in which the machines don't need us for the tactical execution of data-driven programs. They will simply do the work, leaving strategy and vision for us.
Proactive Analytics answers the question: what can you do for me?
As our machines evolve to understand what to do, it is a relatively small stretch of imagination for them to execute, to do some of the work on our behalf. We already have some of the technology necessary to do so. Consider a few examples:
- In the advertising technology world, programmatic advertising - the bidding and buying of inventory and automatic triggering of ads - is quite successful.
- High-frequency trading on Wall Street makes millions of dollars per day for investment companies who can afford the technology.
- We are, years ahead of futurists' predictions, beginning to see autonomous vehicles on our streets.
If a machine can successfully drive a car - a literal life or death scenario - then executing a marketing campaign should be trivial by comparison, once the technology evolves and spreads into the marketing automation domain.
In the same way that automation removed a large portion of the manufacturing process that did not leverage humanity's strengths, I expect automation to eliminate the drudgery in analytics.
What does this mean for us, ours career, our companies? The evolution of analytics is already a battleground. Companies which are most agile, fastest to adopt, and most flexible will create and sustain nearly insurmountable competitive advantage over laggards.
How to Take Advantage of the Evolution of Analytics
On a personal level, try out every analytics platform that you can reasonably test out.
Become familiar with the machine learning and artificial intelligence technologies from companies like Google, IBM, Facebook, Amazon, and Microsoft.
Learn the tools and language of analytics, from serious academic packages like R and SPSS to marketing-specific products like Google Analytics.
Once you've developed analytics skills, you'll be able to confer strategic competitive advantage to any company or organization you work for that will be difficult to replicate, especially if you give your organization an early head start.
Disclosure: this post has been revised and updated several times. The most recent edition changed the artwork and made much more explicit the role and function of each layer in the hierarchy of analytics.
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