Tremendous headline over on CIO right now: C-Level Execs Value Data, Have No Idea What To Do With It. This could be an earlier leader on the nerd/wonk front for best headline of the year, because it is unflinchingly and utterly true. You can read the whole article at the link, and it’s a good one, but let me pull out probably the most stunning contextual paragraph:
But while these executives recognize the value, 96 percent say that untapped benefits remain on the table. In fact, 85 percent say they face challenges implementing the correct solutions to accurately analyze and interpret their existing data and 75 percent say they find it difficult to make decisions around data and analytics. Many (40 percent) say they are wrestling with integrating data technology into their existing systems and business models.
Wrap your head around that; 3/4 of those polled are saying they find it difficult to make decisions around data and analytics. Out of a room of 100 execs, only 25 are comfortable using existing data trends and patterns to make decisions. Are the other 75 using their gut, which is probably 57-65 years old and has never heard of Snapchat or Uber? That doesn’t seem like the best model either.
There are about a million thoughts one can offer around this topic, so let me minnow it down to two or three.
1. This summer, I had a job that had very little to do with data in the traditional sense. However, the company was doing a project around employee engagement, which is something that interests me, so I offered to help. In the early meetings around it, I heard about four different people say stuff like “the plan here is to really drill down on the data.” We never once discussed what data we were (a) collecting or (b) looking for. My job was contract, but I offered to help with it after my run was up. I actually called the project manager (not based where I live) 3-4 times and he never called me back. I heard from someone that the project was “kinda sorta shelved.” Hm. I think this probably speaks to the biggest problem: first off, data can be overwhelming. No one likes to look at 280 Excel rows. That sensation sucks. So people want to approach it in a more manageable way, but how can you do that? It would require good, effective communication between senior staff and execution-level staff (which is probably more rare than we all want to admit), and it would require people that know how to take data, organize it, figure out what it means, and then also know how to present that in one slide/five minutes to a COO. So, we’re back to the issue of good hiring again.
2. Off that last little point, the absolute best person in terms of data isn’t the Excel wizard or the “OMG I found this Asian kid and he can do everything!” person. Those might be good fits, or even the best fits — but the real person you want is that last example. You want someone that can understand what is in front of them and then explain it briefly. Here’s another example from the summer. I worked with a girl whose project was entirely data-based. At the end of the summer, we got the chance to present our work to some C-Level people. My project was 10 slides, done in about 8-12 minutes. Her project was, before first edit, over 100 slides. Listen, I’m all for being thorough in my work, but there’s some key context here — C-Level people are inherently busy, and they don’t want to sit through 100 slides of anything, unless Slide 100 is talking about a 40 percent boost in revenue and a $200K bonus for them, both guaranteed. The challenge of big data is finding the “nerd” who can analyze it but also present it in a way that’s (a) quick and (b) ties to business goals.
3. You see the same problem in this Big Data discussion as you see in a lot of other areas of the workforce — namely, there’s a generational shift right now and it’s hurting productivity. You have 65+ guys leading divisions and sectors but not wanting to jam up on retirement because of fiscal uncertainty, so they’re clinging to what they understand. Here’s a good example on the marketing side: most companies that are successful with reaching new customers and retaining old customers are now using a mix of inbound (create content to draw people to you) and outbound (traditional stuff like direct mail and cold-calling). I’ve been to so many interviews where people are like “We need to get in touch with inbound…” and then six months later, after I’ve been rejected from the job, I look at their website and the last content update was eight months ago. I know those guys are working a hardcore sales funnel, and the reason is simple: that’s what they understand, and people gravitate towards what they understand, especially in times of uncertainty. Big Data can be confusing and overwhelming. Why would I base a decision off what my customers are actually doing on my website when I could base it off my gut? I’m the boss, after all. That mentality costs people happiness and revenue. Look for the right people and you can solve this quandary.
There are other takes on this from Forbes and The Futurist that are much smarter than what I can muster, so check them out too.
It’s not just generational. I’ve seen plenty of 30-something managers who just feel more important trusting their gut than the data. They *say* they want the data, and all those other “trends” like “user engagement.” But it’s unappealing to people who focus on ladder-climbing. Sad but true.
This is very interesting…great points about Big Data. There can be several layers of disconnect between raw data sets and the C-levels. First, the data sets have to be as accurate and complete as possible. Proper database management is key.
Second, data analysts/scientists/name du jour(s) are used as middle men entrusted with stewardship of the data. This can present problems when hiring managers for these positions assume that only candidates with PhDs or statisticians can handle analyses. Maybe they can handle the analyses, but they can’t communicate and/or present relevant findings to non-analytical audiences. Or, maybe the reverse is true: an MBA understands the meanings and insights after the analyses are done, but he/she can’t handle the rigor/tedium of sifting through large data sets to get the end results. Much more recruiting effort needs to be applied to understanding the type of personality, skills and interests of niche candidates that are attracted to roles like these.
Third, stakeholders receiving reports/results from analyses must have some understanding of the analytical process, their own data sets, and truly be receptive to suggestions (like they say they are). Also, some results/recommendations can’t be explained in 30 seconds or 5 minutes or in 5 deck slides.