At EMC, we all went to Six Sigma class, and learned how to DMAIC key processes, and then DFSS. My personal favorite was the statapult exercise -- big fun if you've never done it.
Over time, an army of Six Sigma green belts were developed throughout EMC's ranks, augmented by the ultimate masters: the Six Sigma Black Belts.
Why did so many companies make such a large investment in Six Sigma?
The answer is painfully simple: it quickly became the new competitive ante.
You either invested in getting good at Six Sigma, or you had better be prepared to suffer at the hands of competitors who had wisely made that investment.
I think we're seeing the opening scenes of a similar movie: a need for investing in broad-based skills in big data analytics proficiency.
The case for this particular observation is strengthed by recent results from EMC's Data Science survey -- practitioners point to the lack of these broad based skills as one of the major things holding them back.
This time around, the motivation is also simple: invest in learning to use multiple data sources and the newer tools to better predict the future, or be prepared to suffer at the hands of those who have made that investment.
And today, EMC is announcing a key component of our investments to help our customers and partners get better at this new and important skill set: an associate-level one week course and certification in big data analytics techniques.
I think it's going to be popular :)
History Can Always Teach Us Lessons If you're in a competitive industry (and who isn't?), a lot of leadership time is spent thinking about new ways to create a competitive edge. Everything is fair game: better versions of existing products, new ways of engaging with customers, investments in entering new markets, a focus on creating an innovative culture, tools to make better decisions, and so on.
The story of Six Sigma is instructive in this regard. My impression is that Motorola figured out a way to improve quality processes and ended up kicking serious patootie on everyone else in their sector at the time. Although Six Sigma had its roots in semiconductor manufacturing, the framework proved broadly applicable to all manners of business processes.
EMC's motivations -- at an executive level -- were likely quite simple.
This Six Sigma stuff looks like it creates a meaningful competitive advantage for those that seriously adopt it.
We do business in an incredibly competitive industry.
Ergo, we have no choice but to enthusiastically embrace Six Sigma proficiency throughout EMC, so let's get started.
(Quick note: this was the same line of thinking that was behind our investment in social media proficiency five years ago, not to mention other similar corporate initiatives).
The executive team started broadcasting the priority loud and clearly. A Six Sigma program office was formed and staffed to drive engagement. As a member of the management team, I was "strongly encouraged" to take a few days of training. A few of my people were really interested in the whole topic, and ended up going down the green-belt-leads-to-black-belt path. Communications on progress and business results were consistently frequent.
And then, one day, we were all sort of done with the heavy lifting.
We understood the problems, the tools and the methodologies. We had successfully applied the methodologies to broad portions of our business, and the results were plainly obvious to all. Six sigma had simply become part of our culture and the way we did business.
The envisioned change had happened.
History Is Repeating Itself
Done well, big data analytics enables organizations to create models that can help predict future outcomes.
Traditional business intelligence was mostly about understanding what had happened in the past using limited data sets; the new wave is clearly focused on understanding underlying relationships between ostensibly disparate data sets and using them to make predictions about likely outcomes.
In essence, you're investing in creating the proverbial crystal ball. Being able to predict future outcomes using statistically validated models seems like a handy thing to have in the corporate tool belt, if you ask me :)
While the ideas behind big data analytics and associated data scientists aren't really all that new, their broader applicability is certainly new. New, rich data sources are popping up everywhere, and they're getting easier to acquire. The costs associated with the supporting tools and infrastructure are droppping like rocks (insert obligatory EMC product technology plug here). Core business processes that are enabled with real-time predictive analytical insights can clearly be shown to perform far better than those that are not.
And more and more business leaders are realizing that -- yes -- big data analytics is the next competitive ante. Whether they got there by themselves -- or are seeing their erstwhile competitors doing it -- really doesn't matter.
For the newer business models that were "born digital", they already get it, and are well along their way. Feed them cool technology (and lots of data sources), and they'll be just fine.
The real interesting action is in traditional business models that look very different when augmented by big data analytics.
I'm starting to see more executive teams "get it" (just like they did with Six Sigma), invest in corporate-level program offices (just like they did with Six Sigma), driving broad-based training to create a cadre of data science green belts and black belts, and creating newer self-service large-scale analytics environments where the new skills can be developed and practiced.
For me, history is starting to repeat itself. Again.
EMC Education Is Investing In Skills Creation
If you're with me so far, you probably have a good understanding how EMC Education's new EMC Proven Professional Data Science Associate coursework and certification fits in (EMCDSA for short).
We think these skills are going to be incredibly important: now, and in the future. We believe it, and our customers are telling us the exact same thing.
While this specific new educational offering won't make you a bona-fide, card-carrying data science rockstar in a week, what it does do very well is take someone with the natural skills and inclinations, and gives them the background and experience to work as part of a larger data science team.
People who are interested in data science and this whole area are generally fun people to work with.
We've constructed a sample persona, and -- based on my personal experience -- it's pretty accurate. It's a nice mix of left-brain and right-brain skills: from the quantitative to the collaborative to the creative.
If you're a regular reader of this blog, your personality probably lines up in many of these regards, as does mine :)
The model I'm seeing over and over again is a small team of hard-core data scientists, augmented by a much larger audience of people who understand what they do, and how they do it.
Whether these people are co-workers, managers, helpers, business partners, etc. etc. -- this course is targeted at people who (a) see themselves working more with data scientists -- and data science -- in the future, or (b) see themselves evolving into a rock-star data scientist over time.
Either way, I think there's a large audience for this sort of coursework.
The Coursework
As you can see from the attached graphic, it's a week well-spent, in my humble opinion.
The first day is about context: why this is important, why it's different, the intended role that's being fulfilled, and so on.
The next two days are deep dives in classical analytics using modern tools. The fourth day branches out to unstructured data (e.g. Hadoop) as well as the powerful capabilities of in-database analytics.
And the fifth day is mostly about the all-important communication and storytelling aspects.
Lots of labs with real-world data sets, modern tools and large-scale infrastructure, plus the opportunity to meet and work with like-minded people. Like other EMC Education offerings, I'm sorely tempted to clear a full week and go have some fun :)
Wait, I'm In IT -- Why Should I Care?
When I talk about this subject to career IT professionals, they're interested, but they're not exactly sure how these skills might apply to them in their chosen profession. I think there's a deeper connection than most may realize.
First (and most obviously) if you're going to have an organization with progressively more big data analytics types, it pays to understand a bit about who they are, what they're doing -- and what they need from IT.
More directly, it appears that many IT disciplines will incorporate big data analytics skills in the near future. Consider, just for a moment, what capacity planning or performance management might look like in a few short years, especially in a world of large-scale variable IT service consumption. You're going to want to get pretty good at predicting the future :)
Indeed, the next wave of security thinking is already leaning towards predictive analytical models using an incredibly wide variety of data sources.
The message is simple: big data analytics will not only be a new use case for IT, it will likely transform many of IT's key processes as well.
Why This Matters
Competing through big data analytics is quickly becoming the new ante in so many industries that I encounter. Sooner or later, most business leaders will realize they have to invest in these proficiencies, or suffer at the hands of those that have.
Once these leadership teams make their decisions -- and start to organize for success -- there will be a real and immediate need for increased proficiency across the broader organization.
I think all the bright people who think they might be involved with this have a decision to make.
Do they wait for the time when they're told to attend a specific course?
Or do they decide to get ahead of the curve -- ahead of the inevitable wave?
By: Chuck Hollis
