Is Big Data Giving You Grief? Part Two: Anger
”We missed our numbers last quarter because were not leveraging Big Data! How did we miss this?!”
Continuing this five part series focused on how organizations frequently go through the five stages of grief when confronting big data challenges, this post will focus on the second stage: anger.
It’s important to note that while an organization may begin confronting big data with something very like denial, anger usually isn’t far behind. As mentioned previously, very often the denial is rooted in the fact that the company doesn’t see the benefit in big data, or the benefits appear too expensive. And sometimes the denial can be rooted in a company’s own organizational inertia.
Moving past denial often entails learning—that big data is worth pursuing. Ideally, this learning comes from self-discovery and research—looking at the various opportunities it represents, casting a broad net as to technologies for addressing it, etc. Unfortunately, sometimes the learning can be much less pleasant as the competition learns big data first … and suddenly is performing much better. This can show up in a variety of ways—your competitors suddenly have products that seem much more aligned with what people want to buy; their customer service improves dramatically while their overhead actually goes down, and so on.
For better or worse, this learning often results in something that looks an awful lot like organizational ”anger”. As I look back at my own career, I can recall more than a few all-hands meetings hosted by somber executives highlighting deteriorating financials, as well as meetings featuring a fist pounding leader or two talking about the need to change, dammit! Its a natural part of the process wherein eyes are suddenly opened to the fact that change needs to occur. This anger often is focused at the parties involved in the situation. So, who’re the targets, and why?
The Leadership Team
At any company worth its salt, the buck stops with the leadership team. A shortcoming of the company is a shortcoming of the leadership. So self-reflection would be a natural focus of anger. How did a team of experienced business leaders miss this? Companies’ task leaders with both the strategic and operational guidance of the business—so if they missed a big opportunity in big data, or shot it down because it looked to costly or risky, this is often seen as a problem.
Not to let anybody off the hook, but company leadership is also tasked with a responsibility to the investors. And this varies with the type of company, stage in the market, etc. In an organization tasked with steady growth, taking chances on something which appears risky—like a big data project where the benefits are less understood than the costs—is often discouraged. Also, leaders often develop their own ”playbook”—their way of viewing and running a business that works. And not that many retool their skills and thinking over time. So their playbook might’ve worked great when brand value was determined by commercial airtime, and social media was word of mouth from a tradeshow. But the types and volume of information available are changing rapidly in the big data world, so that playbook may be obsolete.
Also, innovation is as much art as science. This is something near and dear to me both in my educational background as well as career interests. If innovation was a competence that could just be taught or bought, we wouldn’t see a constant flow of companies appearing (and disappearing) across markets. We also wouldn’t see new ideas (the web! social networking!) appear overnight to upend entire segments of the economy. For most firms, recognizing the possibilities inherent in big data and acting on those possibilities represents innovation, so it’s not surprising to see that some leadership teams struggle.
There are times when the upset over a missed big data opportunity is aimed at the staff. It’s not unusual to see a situation where the CEO of a firm asked IT to research big data opportunities, only to have the team come back and state that they weren’t worthwhile. And six months later, after discovering that the competition is eating their lunch, the CEO is a bit upset at the IT team.
While this is sometimes due to teams being ”in the bunker” (see my previous post here), in my experience it occurs far more often due to the IT comfort zone. Early in my career, I worked in IT for a human resources department. The leader of the department asked a group of us to research new opportunities for the delivery of information to the HR team across a large geographic area (yeah, I’m dating myself a bit here… this was in the very early days of the web). We were all very excited about it, so we ran back to our desks and proceeded to install a bunch of software to see what it could do. In retrospect I have to laugh at myself about this—it never occurred to me to have a conversation with the stakeholders first! My first thought was to install the technology and experiment with it, then build something.
This is probably the most common issue I see in IT today. The technologies are different but the practice is the same. Ask a room full of techies to research big data with no business context and… theyll go set up a bunch of technology and see what it can do! Will the solution meet the needs of the business? Hmm. Given the historical failure rate of large IT projects, probably not.
It’s a given that the vendors might get the initial blame for missing a big data opportunity. After all, they’re supposed to sell us stuff that solves our problems, aren’t they? As it turns out, that’s not exactly right. What they’re really selling us is stuff that solves problems for which their technology was built. Why? Well, that’s a longer discussion that Clayton Christensen has addressed far better than I ever could in “The Innovators Dilemma”. Suffice it to say that the world of computing technology continues to change rapidly today, and products built twenty years ago to handle data often are hobbled by their legacy— both in the technology and the organization that sells it.
But if a company is writing a large check every year to a vendor—it’s not at all unusual to see firms spend $1 million or more per year with technology vendors—they often expect a measure of thought leadership from that vendor. So if a company is blindsided by bad results because they’re behind on big data, it’s natural to expect that the vendor should have offered some guidance, even if it was just to steer the IT folks away from an unproductive big data science project (for more on that, see my blog post coming soon titled “That Giant Sucking Sound is Your Big Data Lab Experiment”).
Moving past anger
Organizational anger can be a real time-waster. Sometimes, assigning blame can gain enough momentum that it distracts from the original issue. Here are some thoughts on moving past this.
You can’t change the past, only the future. Learning from mistakes is a positive thing, but theres a difference between looking at the causes and looking for folks to blame. And it’s critical to identify the real reasons the opportunity was missed instead of playing the “blame game”, as it would suck up precious time and in fact may prevent the identification of the real issue. I’ve seen more than one organization with what I call a ”Teflon team”—a team which is never held responsible for any of the impacts their work has on the business, regardless of their track record. Once or twice, I’ve seen these teams do very poor work, but the responsibility has been placed elsewhere. So the team never improves and the poor work continues. So watch out for the Teflon team!
Big data is bigger than you think. It’s big in every sense of the word because it represents not just the things we usually talk about—volume of data, variety of data, and velocity of data—but it also represents the ability to bring computing to bear on problems where this was previously impossible. This is not an incremental or evolutionary opportunity, but a revolutionary one. Can a business improve its bottom line by ten percent with big data? Very likely. Can it drive more revenue? Almost certainly. But it can also develop entirely new products and capabilities, and even create new markets.
So it’s not surprising that businesses may have a hard time recognizing this and coping with it. Business leaders accustomed to thinking of incremental boosts to revenue, productivity, margins, etc. may not be ready to see the possibilities. And the IT team is likely to be even less prepared. So while it may take some convincing to get the VP of Marketing to accept that Twitter is a powerful tool for evaluating their brand, asking IT to evaluate it in a vacuum is a recipe for confusion.
So understanding the true scope of big data and what it means for an organization is critical to moving forward.
A vendor is a vendor. Most organizations have one or more data warehouses today, along with a variety of tools for the manipulation, transformation, delivery, analysis, and consumption of data. So they will almost always have some existing vendor relationships around technologies which manage data. And most of them will want to leverage the excitement around big data, so will have some message along those lines. But it’s important to separate the technology from the message. And to distinguish between aging technology which has simply been rebranded and technology which can actually do the job.
Also, particularly in big data, there are ”vendorless” or ”vendor-lite” technologies which have become quite popular. By this I mean technologies such as Apache Hadoop, Mongodb, Cassandra, etc. These are often driven less by a vendor with a product goal and more by a community of developers who cut their teeth on the concept of open-source software which comes with very different business economics. Generally without a single marketing department to control the message, these technologies can be associated with all manner of claims regarding capabilities—some of which are accurate, and some which aren’t. This is a tough issue to confront because the messages can be conflicting, diffused, etc. The best advice I’ve got here is—if an open source technology sounds too good to be true, it very likely is.
Fortunately, this phase is a transitional one. Having come to terms with anger over the missed big data opportunity or risk, businesses then start to move forward… only to find their way blocked. This is when the bargaining starts. So stay tuned!
Next up: Bargaining “Can’t we work with our current technologies (and vendors)? …but they cost too much!”