Before you overreact to the headline, let’s make one thing perfectly clear: You need analytics. On that, there’s no debate.
Regardless of size, or industry vertical within which it does business, your company's ability to survive and thrive in the modern economy is inextricably tied to its ability to connect to and analyze data. Without the predictive insights that advanced data analysis provides, even the smartest, fastest and most agile companies will find it difficult to keep up with and outmode competitors. As far as must-have capabilities go, advanced analytics is right atop the list.
But there’s a difference between going all-in on analytics -- which companies are wise to do -- and going overboard. And unfortunately, an increasing number of companies seem to be gravitating towards the latter.
By over-rotating on analysis they in the process negate many of its primary benefits. What does over-rotating on analysis look like? It’s when data analysts outnumber creative strategists 3-to-1 at marketing planning meetings. Or when a company’s creative innovators and internal entrepreneurs can’t act on ideas that don’t meet the approval of its data analysts. Or when financial analysts, rather than creative company leaders, dictate when and where money is spent on innovation, awareness and thought leadership.
The Drawbacks of Overanalyzing
In order to better understand the potential drawbacks of excessive analysis, you first need to remember why data analytics are so valuable in the first place. Data analytics, especially those that are predictive in nature, enable companies to obtain forward-looking insights they wouldn’t otherwise have been privy to -- such as forthcoming changes in customer behavior and buying patterns, or the formation of new market segments as-of-yet unserved by the industry. In other words, one of the major reasons companies invest in advanced analytics in the first place is to drive innovation and enjoy first-mover advantage.
The problem is that while you typically can’t achieve the competitive edge you’re looking for without analytics, you can’t achieve it when you over-analyze, either. This is where the analytics game becomes a balancing act, one that companies are increasingly struggling to manage.
Optimization vs. Innovation
On one hand, optimizing business processes is a vital part of modern day company success, and achieving the desired level of optimization requires ongoing data analysis. On the other hand, while optimization is certainly a critical part of what companies should seek to derive from analytics, so too is innovation. And more often than not, when companies over-analyze, they become obsessed with optimization at the expense of innovation.
When that happens, analytics becomes exclusively about finding ways to cut costs, when it should also be about finding ways to be cutting edge. It becomes exclusively about protecting today’s existing revenue stream, when it should also be about finding tomorrow’s new one.
In other words, focusing too much on optimization can have the unintended effect of making organizations afraid to take on any risk. When done properly, with equal focus on optimization and innovation, analytics should have the opposite effect. Good data analysis should be what gives company leaders the justification and confidence they need to be bold and take risks with an eye on the future.
The purpose of analytics isn’t -- and never will be -- for company bean counters to pull the plug on forward-looking innovations or company R&D because the latest data analysis shows it won’t immediately make money. Analytics is not about inundating the business with numbers, metrics and measurements for the purpose of killing every project or employee action that won’t immediately drive revenue.
Finding the Balance
So, how do you balance the ledger? How do you achieve desired levels of optimization without becoming so risk-averse that you lose your innovative edge? The answer is to create company processes and a company culture in which analysis is about innovation as much as it is optimization.
And while you’re creating those new processes and instilling that new culture, leave no room for interpretation. Create a clear separation between church and state, so to speak. Deliberately separate the teams that analyze data for the purpose of business optimization (the process guys) from the teams that analysis data for the purpose of innovating and uncovering new opportunities (the culture guys) not available to the business today.
A great rule of thumb is to organize your analytics efforts into three distinct categories. First, create a team focused on near-term business efficiencies. This team’s job is very simply to analyze data to find ways to optimize operational processes in the here and now. But -- and this is crucially important -- teams focused on optimization cannot and should not be given authority to pull the plug on projects implemented by teams focused on long-term innovation.
Next, create teams focused on intermediate-term innovation. These folks are ideally tasked with analyzing data to identify emerging trends that don’t impact the business and its bottom line today, but are very likely to do so in the next 3-5 years. Think of these people as your thought-leaders who can focus on where the market is going in both the short and long term, helping the company mitigate risk while still pushing the envelope on behalf of customers.
Lastly, build teams that are empowered to use data in order to go forth and innovate, with no specific time horizon to focus on, and no concerns over the ROI their work will deliver that quarter, that year or perhaps even that decade. This is a team that should feel empowered to look 10 or 20 years out and predict the type of changes that lay ahead for the business and its industry at large.
Stop the Snowball Before it Starts
Here’s the good news: The use of analytics is still in its infant stages, and over-analysis is not yet an epidemic by any means. But as any parent of young children will tell you, bad habits are hard to break if you don’t address them early. And make no mistake, overanalysis at the expense of innovation is a snowball that rolls downhill quickly and can be hard stop once it gets going.
There’s nothing wrong with using data analysis as a means to protect short-term revenue, but keep two things in mind: 1. that it was almost always some form of innovation that made it possible for you to get that revenue in the first place and 2. that at its heart, analytics is about always empowering innovation.