Everyone knows what it is and is hopefully working analytics (“big” or not) into their 2017 business strategy (and beyond). I worry some planners are using data in a “me too” manner rather than one that builds data into their true core planning.
Do they get it? Do they embrace it? Or is the mindset that data is a fluffy unicorn and will go away?
Let’s work out a new version of Maslow’s hierarchy of needs to define different layers of acceptance of data in our companies.
Are you really a data-driven, Big Data, and analytics-engaged company or not? Has your company completed its journey to build data into the culture — into the core decision-making of your business? Or, is your company doing a that-is-really-great-information-but-I’m-sticking-to-Plan-A maneuver? Where are you on the chart?
I have taken liberty with Maslow’s hierarchy of needs to put some broad categories of data adoption together.
As I have traveled about and watched the movement of analytics and Big Data go from dreams to struggling implementation to full adoption, I have noticed a pattern develop with companies as they work their way through finding a comfort zone with using data, analytics, and true “Big Data” in their operations.
Some companies go so far as to build a line-item plank in their strategic plans’ key analytical projects from which to build their futures.
Others made the technical and talent investments and see some gains, but when contract renewal time cycled through or at the annual planning retreat, they retreated to a safe zone. Gut and intuition.
These two examples are the opposite ends of the chart spectrum.
Where does your company fall in the spectrum?
Stage 1: Physiological
The executives understand there is value in the data. They probably have a couple Excel wizards producing excellent KPI and dashboard reports. They are probably using the same reports designed in 1998 because they tell the execs “everything they need to know.”
The executive suite is wondering why readership, circulation, and revenues are falling. The executive suite has the Excel folks generate some trend reports to solve the mystery. The Excelers may have a huge Excel spreadsheet that, in jest, has told the suite is 75mb with 435,000 rows and goes out to column EB. But, they have snuck an Access database in behind the scenes in a vain attempt to move to a better analysis tool/database.
The execs are happy and use the trend lines to make decisions.
But, year-over-year, they still wonder why things are going south. With Excel as the digital version of basic food and shelter, the executives are not willing to listen to anyone. Their gut and intuition will ride the storm out — or at least serve as rationalisation for the failure of the company.
Excel is used as a descriptive analytical tool. Data isn’t used to drive the business, but only to explain the past. Hopefully your company isn’t at this stage.
Stage 2: Safety
Business is changing and revenues are shifting. So there is little support from the financial people to support a large investment to replace the US$800,000 (at the time) state-of-the-art circulation (distribution and billing system) brought in 18 years ago from Big Software House.
The 18-year-old software is safe. It is still updated regularly so the executive level thought is that, what Big Software House provides with all its tools and bells and whistles, is everything that is needed. Data? Sure, we have it — all managed by Big Software House’s package.
Lost is the fact that Big Software House’s package is really just a billing-and-distribution system. It is good at getting the right number of products out today to the right places. It is good at showing what happened yesterday.
From a data and analytics perspective, it is only good at descriptive analytics. It has no way to predict, and no way of connecting the 150,000 daily print copies sold with the 200,000 unique visitors a day to the company’s digital product(s).
At the business planner level, all is good because the system knows everything that happened. But, from a data usage perspective, it is a disaster because it only knows that, and not the “why” or “what if.”
Companies at this stage are playing it safe and willing to accept falling circulation as a change in the world we live in. In the end, playing it safe is a long, slow, painful way to ride out the career.
Stage 3: Belonging
Here, the executive suite has realised that Big Software House’s distributions system is a solution to a point problem — who gets the product — but has no tools to help figure out the what, why, and how questions.
The executive suite has heard about data and is now willing to play, but only so much. “Let’s invest in a data warehouse to capture data,” they say. “We have some Excel folks in finance. Let them do some analysis.”
The digging is slow and quite labor intensive, and, in the end, is still a rearward-looking process.
The executive has let loose on the data the wrong type of analyst. Financial analytics are descriptive (for example, in 2008, we increased prices by 15%, and from then on saw an acceleration in our downward sales results).
Some financial analysts may be a bit more progressive in their approach. A diagnostic analysis is produced (that is, not only in 2008 did we increase prices by 15%, but at the same time, the housing market collapsed, unemployment spiked, gasoline prices took off, and it became increasingly hard for our customers to justify continuing their discretionary spend — their subscription — with us as the average age of our largest customer group is approaching 60).
At this third level, analysts see the need for more powerful tools — SPSS or SAS — or maybe even to build out a consolidated view of the customer (a single customer view, as some call it), but our executives find out the deeper diving analytical tools are expensive.
Executives start asking around within their peer group and find others are in the same boat while ignoring the peers that are much more enlightened. Other conservative peers have a data warehouse and some tools, but share little about their own search for something better.
Stage 3 executives find comfort in the fact others are feeling the same pain. This category of company struggles to take “the risk” to move to a higher level in the pyramid.
While ignoring the need to move to the next level, our executive at this stage also takes pride in allowing (err, taking credit for) analysts doing deeper dives into the data than they are supposed to take. After all, they are financial analysts, not data analysts.
Stage 4: Esteem
The executives here realise the old-school technology and analysis tools are not good enough to show what is needed. They have moved from reporting to predicting. Old-school tools are only for point solutions and not part of how they do all business.
Companies at this level are pretty good at diagnostic analytics. Companies at this level are making the data investment, but the embracement of data isn’t built into the core of the company.
There is a lot of “me too” at this level. Executives, with pride, know they have a Big Data system — Hadoop rolls off the tongue in peer-to-peer conversations.
But, in the end, the power of Hadoop (or NoSQL, for those so inclined) is not embraced in the company’s core decision-making process. The company will dive deep into the data, but can’t quite make the leap to build data-driven decisions right into the core of how the company works.
A lot of gut and intuition still lives in these companies, typically manifested when the “data” confirms their decisions. When the data doesn’t confirm — or even suggests completely different strategies — the data is ignored as “we’re still learning how to make that Hadoop thing work.”
Companies at this level can be a frustrating place to work. They have the tools, but not the desire.
Stage 5: Self-actualisation
What does the data tell us (descriptive)? Why did it happen (diagnostic)? What will happen (predictive)? How can we make it happen (prescriptive)?
These are the questions the executives in Stage 5 companies expect to see answered.
The company has fully adopted Big Data into an organisation that works seamlessly in the environments of data insight and data-driven foresight. Executives are acutely aware that analytics are no longer nice to have but a key strategic tool in the operation. They use data and appreciate the power of data with the same enthusiasm as they do an investment in a new production process improvement purchase.
Getting to this level is very difficult, but it is not an out-of-reach dream. Companies at this level have made years of investment, built a culture around using information, and made the investment in people (not just the technical people, but employees in the company as a whole to embrace the use of data), the needed infrastructure, and the resolve to continue to evolve and improve all processes to leverage information.
Every purchase (or cart abandon) delivers data that can be turned into information, and with the right analysis, can be used to influence future events (sales). From clicks to mouse movement. From page load time to scroll depth. From story version one to version two. From feature/price point one to feature/price point two. Copies sold and whether it rained that day. Sales changes based on houses in foreclosure.
Does the price of a gallon of gas make a difference in sales? Do specific groups of items make a difference? And so on. There are hundreds of things to look at. The companies at Stage 5 understand this and build their plans around the information.
With the right information, you can adjust your acquisition practices, know how (and when) to leverage social networks, and refine your promotion strategy to make pricing and distribution decisions. Data should be used to make product decisions.
Message delivery (content) has never been so competitive. Data can give you an advantage over your competition, and in the era of “free,” having an advantage in your strategic plan is a no-brainer.