STRUCTURING THE DATA & ANALYTICS STRATEGY

Updated: Feb 11


My recent post on Modernizing the Data Strategy was relatively general. We need to dive deeper. But before we do so, I wanted to share with you the framework I use to structure the Data & Analytics Strategy. Every organization is different, and the focus will be on specific components; however, you must understand where I am going to tie it all together.


Modern Data Analytics' Data Analytics Strategy Framework

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This is more than people, processes and technology. How I see it is that there are people, processes and tooling in each of the six components. I have put examples of the elements that we should define beside each box. These are by no means exhaustive. In essence, we must establish a current and target state as well as a transition plan for each component, and the plans must work together as one program, aligned with the business strategy, all rooted in common sense and increment delivery of real business value.


All of it


I would also say that skipping any one of these at the Data & Analytics Strategy level can get us into trouble. Because all of it must work together, any change in one can have impacts elsewhere, so be careful. My advice is if you are going to carry out a Data & Analytics Strategy, do it well. What is possible is to do it incrementally, at increasing levels of depth, but always consider all six components at each turn.


Business case


Another point I would like to make is that we are at a significant moment in Data & Analytics. If your organization has not gone through its Data Transformation, there is a good chance that it could be self-funding.


The savings come in many shapes:


  • faster, more accurate analytical models can create sizeable revenue uplift

  • reduced storage and infrastructure costs (the cloud!!)

  • improved productivity among data resources


The business case will come from answering a few questions, upfront:


  1. What features, based on data, have the potential to transform your business model or your market position?

  2. What area of the business is most at risk of being disrupted by data?

  3. How can data improve profitability? 

  4. How can we use data to reduce our business risk? 

  5. What is the opportunity cost of waiting?

  6. Can you do it with in-house teams?


If we think of how much data is duplicated or redundant and often inconsistent, how much data analysis functions are sometimes overlapping and doubled, and how projects with limited budgets have put in place solutions that are either not scalable or sustainable, it's easy to see how a Data Transformation based on a solid Data & Analytics Strategy can indeed pay for itself.


Offense or defense


The questions above highlight another aspect of the Data & Analytics Strategy: Are we trying to change the business, or are we mitigating risks and protecting something?


Leandro DalleMule, CDO at AIG at the time, and Thomas H. Davenport wrote an article for HBR.org almost three years ago about trade-offs between "defensive" and "offensive" uses of data and between control and flexibility in its use. The article goes on to say that in highly-regulated industries, like Healthcare, we typically see a more defensive approach., whereas, in Retail, we take a more offensive approach. And 50/50 is not a good idea, based on the fact that IT will need to execute somewhat differently depending on the orientation. Read the article for more details.

Conclusion


Finally, it is necessary to measure and make sure we are on track with the objectives. Data Governance has its own measures to track the quality of the data effectively (see more details from Irene Mikhailouskaya), and many tools can automate the process. More importantly, we want to follow how efficient we are at delivering our business-outcomes-based program, and our rate of delivering business value. We want to identify the bottlenecks and be able to act on them, know if we need to ramp up training or if one of the technologies is underperforming.

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