What’s in your data and analytics strategy?
Every day our team receives requests from organizations to review their strategies. Most often this starts with the submission of a "strategy" document for us to read and inwardly digest. Then we either send back a written response with detailed comments and ideas, often followed up with a real live telephone call; or we just go right into a telephone inquiry.
We have written a number of research pieces on strategy; and we have analyzed many client produced documents. I simply cannot (and am not permitted) to share all the findings here – there are just too many of them. But there are a couple of items I wanted to share with you – one small and one useful going forward.
In Data and Analytics Strategies Need More-Concrete Metrics of Success, Frank Buytendijk and colleagues analyzed a large number of so called strategy documents: “The data and analytics research community did a total of 106 strategy document reviews between January 2014 and December 2015. These documents were called “strategy,” “plan” or “road-map,” and they focused on “data,” “information” or “big data.”
The workload for such things is increasing – there are more and more strategy documents to review. They tend to be long – over 200 pages. They tend to be read once, if you are lucky. They are rarely updated since it takes to long to do so. They are not referenced often or used. They tend to cost a small fortune to produce, and consulting companies love the work. But in truth, there are many findings that are alarming. Here are two:
- The vast majority of such documents do not make any reference to a real measurable business outcome (what Frank’s note calls out)
- The vast majority of such documents misconstrue the term strategy for plan, action, decision, risk, principle, goal, direction, effort, tactic, and so on.
The other item I wanted to share – the real purpose of this blog – concerns the scope of these strategies. We are all using terms such as:
- Data Analytics (a lazy term; a bug-bear of mine, explained here)
- Data and Analytics
- Knowledge Management (remember this one?)
- Business Intelligence (still going strong in some places)
And at the same time many of the strategies span a continuum of use cases split across:
- Operational work, or parts of an organization – as in – what a firm actually does
- Analytical work, or parts of what organization does in anticipation of work to analyze and prepare
And these two dimensions – the ‘data-analytics’ and ‘operational-analytical-use-cases’ - are a real big landscape.
Not one of the named strategies above really make it clear which of the dimensions or their intersections are in focus. As such, when we receive a strategy document to review, we first have to determine he scope of the strategy since that will determine who in our team can help the client most. Here are some examples to demonstrate why these are different.
Example 1: Data Strategy, Operational Focus: This will lead to a focus on how data is used in operational situations such as storage, persistence, governance, in business applications or an IoT network.
Example 2: Data Analytics Strategy (meaning big data): This, despite the innocuous terms “data analytics” ends up being about analytics and the management of only the data needed for the analytics. This ends up being more analytical and does not end up talking about business process design, business applications and any execution whatsoever.
Example 3: Data and Analytics Strategy: This rare bird is actually the broadest of all since it implies all data, including the analytical uses of data. So this could include example 1 and add in example 2. But there is a huge difference between 2 and 3: With this focus the actually changes in business processes and applications are part of the analytics development; so this is not a focus on dashboards or AI etc; it focuses on decision making and business process design.
The worst part of all of this is that the names chosen by clients when they submit their strategy document for review cannot be classified neatly as the above dialog might suggest. We have to dialog and understand in order to make a call on what the real need is.
Anecdotally, I would argue that over 80 percent of “data and analytics” strategies are really just up to date or modern “analytics strategies” and not really that rounded at all – they are enamored with the newest analytic tool and do not concern themselves with operational use of data, business process change or how decision making, consuming new data, may actually change the business process.
To help convey the point of this blog, I came up with a graphic. I am just starting to use it to explore with clients: Just example what do you mean when you say, “data and analytics”?
Figure 1: What’s in Your D&A Strategy?
(Source: Gartner, Inc., 2018)
This graphic is basically an adaptation of a concept that Valerie Logan pioneered a short while ago. In Information as a Second Language: Enabling Data Literacy for Digital Society back in February 2017 Valerie argued that, and I paraphrase, the factors of production have evolved beyond people, process and technology and today are now expressed as people, process, technology AND data. The addition of ‘data” is what is explored in figure 1 above – what exactly do we mean by “data”?
(This post originally appeared on Andrew White's Gartner blog, which can be viewed here).