To be “enterprising” is to be eager to undertake or prompt to attempt. To show initiative and be resourceful. These are leadership traits, so to be enterprising is to lead. “Analytics” is how we use data to inform decision making, in the context of achieving business objectives. These are management practices, so analytics is about management.
“Enterprising Analytics” is about being creative, resourceful and adventurous with decision making to achieve business objectives. It is about the set of leadership and management practices that need to be in place for an organization to make the most of it’s analytics investment
Data management is about exploration
Columbus should have known better. Actually, he did know better. But he thought he knew best. The world would have been very different if he had stayed on the map. But, we ask, if he was on a voyage of discovery, there was no map? In cartographic terms this is true. But he had enough knowledge to tell him that the new continent could not have been India.
Data becomes information through communication. Peter Drucker wrote that ‘the most important thing in communication is hearing what isn’t said,’ The most important thing about information is discovering what’s been left out. The most important thing in data management is finding what isn’t at hand. In a changing world, the tried and true becomes tired and trite.
If we trust our people to define the future (which is why we hired them), then it’s on us to set them up for success. Which means that we need to get our data management sorted out. Data management is becoming big business. In his report “Data Management at the Time of Self-Service Analytics“, Ovum analyst Laurent-Olivier Lioté estimated the data management market to grow by 8.7% through 2020.
Beware the illusion of control
As with much of our technology, data management products are emphasizing self-service. This makes sense from an enterprise analytics perspective. We want as many smart people to have easy access to rich data. That’s how we solve business problems and keep our organizations in the game for another year. Success with self-service presupposes we’re helping people learn how to explore. Leaving the technology aside for a moment, this is the point of the whole data management market.
Playing around with data is where discovery comes from. Smart business leaders encourage this. Play “can support education, promote emotional wellbeing, and equip children with skills in problem-solving, team-work and self control” Anyone on a journey of working with data is going through a similar experience to kids with lego.
Much of the cult of management rests on the idea that we know where we are going (more or less). And that we know how we are going to get there (more or less). And we enshrine these elaborations into our strategic plans. Which, as many veteran leaders come to realize, can be worse than useless. They encourage an ‘illusion of control’.
We don’t need a plan to explore
In his written evidence to the UK House of Commons Public Administration Select Committee, John Kay described the conceit behind many of our best laid plans,
“We do a great deal of what I call bogus rationality, which is pretending we know things we don’t and erecting elaborate models and structures of argument roundabout them, quantifying things that are not sensibly quantified. These models, and I have spent part of my life building them, are basically rubbish.”
Enterprise analytics is a control for leaders to avoid indulging in elaborate rubbish. A primary reason is analytics involves learning and learning involves play and exploration. It’s because there is no plan that data exploration can be such a difference maker.
A well designed data exploration environment (I hesitate to use the term data management) enables our people to be “… always making discoveries, by accidents and sagacity, of things which they were not in quest of”. Working with data reduces even the mightiest of egos as we discover our clever ideas were not so clever.
And the people who look unremarkable make the most remarkable discoveries. Serendipity isn’t being in the right place at the right time. It’s discovering one place while in search of another. This is exactly what the strategic CXO wants to encourage.
The good news is that data management vendors have figured this out. They are providing automated tools through a code-free, intuitive user interface. This means our inquisitive people can transform their data dumps using pre-established rules. They move straight to separating the wheat from the chaff.
For every task there is a tool and this holds true for the data management market. Following the lead of self-service analytics, data management platforms are doubling down on natural language programming and instant visualization. As the market grows, so do the number of vendors and with it procurement problems. Which to pick?
The good news here is that a healthy dose of patience will help. As Thomas Friedman writes in Thank You for Being Late,, “… patience isn’t just the absence of speed. It is space for reflection and thought”. The point being that the market is consolidating data management platforms. To be a decent platform these days you need to provide a set of complementary functions.
The supply side will sort itself out
The trend of buyouts continues at strength and will extend into data management. As Sameer al-Sakran wrote for Techcrunch there’s a strong economic incentive for acquisitions,
“If you take a SaaS company with solid product and adoption, whose growth has stuttered, get rid of everything aside from a skeleton crew of engineering, customer support and accounting, cash flows can look very, very tempting. While it’s risky to do this too early in a company’s product life cycle, once major companies have wired you into their processes, they’re not inclined to do much fiddling. All the margin of “software” with none of the cash trough of SaaS growth. What’s not to love?”
In April, Bob Evelson of Forrester wrote for ZDNet about the similar trend in the business analytics market,
“In the next five to 10 years, most independent BI vendors will likely get absorbed by industry behemoths, retool into industry — vertical — or business-domain-specific solutions, or go out of business. Companies should invest in deeper evaluation of emerging technologies or allocate their time wisely to other initiatives rather than spin their wheels selecting a BI platform that they base on table-stakes features”
The smart move is here to let the supply side of the market sort itself out and put our time into working with our people. For the CXO this means continuing to put more time into data governance. This isn’t about stacking rule on suffocating rule. It’s about establishing the basic requirements for safe exploration and genuine discovery. Because a free-for-all doesn’t help anybody.
Everything needs a boundary
For Laurent-Olivier Lioté and Ovum this means ‘governed data exploration’,
“To manage a growing user base (increasingly made up of non-experts) manipulating different types of data, Ovum believes that vendors and enterprises should invest in ‘governed data exploration’. Governed data exploration consists of authentication, authorization, and data security.
- Authentication: only certified users can access certain data
- Authorization: outline what users can do with that data
- Data Security: encrypt data and monitor usage logs and patterns”
This is about CXO’s exercising responsible control over data exploration. Control in the sense that a pilot has control of a plane. It’s a fundamental duty of modern technology leadership. Building a responsible data culture means letting people follow their noses as they seek to meet the objectives we have hired them to do. It also means establishing rules of governance so people recognize good behavior. And, importantly, what is genuine discovery and what isn’t.
As an explorer, Columbus should have recognized that what he discovered was a new continent and not the back door to India. This is because his boundaries of exploration had been set 1,200 years earlier by Eratosthenes. We know he knew his Eratosthenes and so we know he knew better. It’s just that he chose to know best. This is why we want to establish good data governance. So that when ambitious young professionals claim great discoveries, we’re able to check their work for even greater discoveries.