I was presenting to a client today on the topic of, “How to Build and Sustain an Effective Data and Analytics Strategy”. This is a topic I present on regularly and the topic of developing a business-relevant strategy for D&A is very hot. It is hot for a number of reasons:
Too often “IT strategies” are mostly about technology (not “I”, but too much “T”).
Too often such strategies are over 200 pages long; cost a small fortune to develop; and require a third party consultant to “do the work” Too often they are too detailed and designed for the days when the perfect strategy was thought the best practice Nowadays with digital business coming to the fore, more and more chief digital officers are becoming chief data officers, and digital strategies are becoming data and analytics strategies. But let’s ignore the hyperbole…
There are several core messages that always become hot topics during such presentations. Today was no different. The key message concerned the technique that can be acquired and developed to link the impact of data quality to specific business outcomes.
Make no mistake – this is a skill, not a tool or technology. And once you master it, you can more easily demonstrate to business users the importance of them being involved and/or leading a data and analytics program, including MDM, information governance, or analytics/BI. You can even leverage this skill to improve the chances of getting business folks involved in governing data in that tired old data warehouse.
Anyway, I had completed the task of demonstrating how to make this link clear. One of the attendees made a crucial point at the wrap-up. She said, and I paraphrase only slightly.
Her role, by the way, is focused on supporting the development of the analytics capability for this client. She said, “I never realized how connected the business process, application, analytic and common data is! This work is an eye-opener. I now see how we need to stop talking of process, application and data/analytics as discrete topics but more as a single, integrated framework.”
What this attendees was saying to me was this:
A focus on data might lead to the wrong conclusion – that data is most important A focus on analytics might lead to the wrong conclusion – that analytics is the most important A focus on the process or application might lead to the wrong conclusion – that processes or application are most important What is most important is the outcome and the decision that drives it – supported and enabled by process, application, analytic and data that underpins all This was awesome!
Put another way, though I think of myself as an apps/process guy (my background is Supply Chain), and though I work mostly on the “data” side of the house, the reality is that I am a closet “analytic” analyst! This is not to say that “analytics”, as defined as a means to provide insight to guide decision making, it is that the word, ‘analytics’ tends to be interpreted by many, many users as being the equivalent to dashboards and analytics and metrics and KPI’s!
Conclusion: Consider stop talking about:
Data and being data-centric. Analytics and being analytic-centric. And consider messaging and marketing the following:
Desired business outcomes (the goal that requires change) Decision making that drives those outcomes (with feedback on actual outcome to tune/learn from) Then the supporting data, analytics, process and applications It is not helpful to suggest (as I did) that users are confusing “analytics” with “metrics”.
The reality is that they are thought to be the same or interchangeable by most folks. We might need to deemphasize (what we thought of as) the halo-effect of “analytics” and focus more on a business-relevant message of decision making.
This was an eye-opener for me today. And one message that won’t sit well with the data-crew, or the analytic side of the house. How to upset everyone at the same time!
(About the author: Andrew White is research vice president and distinguished analyst at Gartner. This post originally appeared on his Gartner blog, which can be viewed here)
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