I was recently preparing for an data-centric interview to be published as a podcast [now available to listen to here, it is the second podcast]. A chat with the interviewer had prompted me to think about the question of how you can tell that there are issues with your Data Strategy. During the actual interview, we had so many things to talk about that we never got to this question. I thought that it was interesting enough to merit a mini-article, which is the genesis of this piece.
I have often had my services retained by organisations to develop a Data Strategy from scratch [1]. However, I have also gone into organisations who have an established Data Strategy, but are concerned about whether it is the right one and how it is being executed. In this latter case, my thought processes include the following.
The initial question to consider is, “are there any obvious alarm bells ringing?” Some alarm bells are ones that would apply to any strategy.
First of all, you need to be clear which problem you are addressing or which opportunity you want to seize (sometimes both). I have been brought into organisations where the Data Strategy consists of something like “build a Data Lake”. While I have nothing against data lakes myself, and indeed have helped to create them, the obvious question is “why does this organisation need a Data Lake?” If the answer is not something core to the operations of the organisation, it may well not need one.
Next implementing a technology is not a strategy. The data arena is unfortunately plagued by technology fan-boyism [2]. The latest and greatest visualisation tool is not going to sort out your data quality problems all by itself. Moving your back-end data platform from Oracle to Hadoop is not going to suddenly increase adoption of Analytics. All of these technologies have valuable parts to play, but the important thing to remember is that a Data Strategy must first and foremost be a business strategy. As such it must do at least one of: increase sales, optimise pricing, decrease costs, reduce risks or open new markets. A Data Lake will not in and of itself do any of these, what you chose to do with it may well contribute to many of these areas.
A further consideration is “what else is going on in the organisation?” This is important both in a business and a technological sense. If the organisation has just acquired another one, does the Data Strategy reflect this? If there is an ongoing Digital Transformation programme, then how does the Data Strategy align itself with this; is it an enabler, a Digital programme work-stream, or a stand-alone programme? In the same vein, it may well make sense to initially design the Data Strategy along purist lines (failing to do so at least initially may be a missed opportunity for radical change [3]), however there will then need to be an adjustment to take into account what else is going on in the organisation, its current situation and its culture.
Having introduced the word “culture”, the final observation is in this area. If the Data Strategy does not envisage impacting corporate culture (e.g. to shift it to focus more on the importance and potential value of data), then one must ask what are its chances of achieving anything tangible? All organisations are comprised of individuals and the best strategies both take this into account and were developed as a result of spending time thinking how best to influence people’s behaviour in a positive manner [4]. Absence of cultural and education / communication elements from a Data Strategy is more a 200 decibel claxon than a regular alarm bell.
Given I am generally brought in when organisations want to address a data problem or seize a data opportunity, I have to admit that I probably have a biassed set of experiences. Nevertheless one or more of the above issues has been present whenever I have started to examine an existing Data Strategy. In the (for me) hypothetical case where things are in better shape, then the next steps in evaluating a Data Strategy would be to get into the details of each of: the Data Strategy itself; the organisation and what makes it tick; and the people and personalities involved. However, if a Data Strategy does not suffer from any of the above flaws, it is already more sound than the majority of Data Strategies and the people who drew it up are to be congratulated.
If you would like help with your existing Data Strategy, then please consider our Data Strategy Review Service. If you want to kick-off the process of developing a Data Strategy from scratch, then we can help via our Data Consultancy Service. Or just feel free to get in touch.
Further reading on this subject:
- Forming an Information Strategy: Part I – General Strategy
- Forming an Information Strategy: Part II – Situational Analysis
- Forming an Information Strategy: Part III – Completing the Strategy
- Building Momentum – How to begin becoming a Data-driven Organisation
Notes
| [1] |
A matrix of the data-centric (and other) areas I have been accountable for at various organisations appears here. Just scroll down to Data Strategy, which the is the second row in the Data-centric Work section. |
| [2] |
And fan-girlism, though this seems to be less of a thing TBH. |
| [3] |
See: |
| [4] |
I cover the cultural aspects of Data-centric work in many places on this site, perhaps start with 20 Risks that Beset Data Programmes and Ever tried? Ever failed?, both of which also link back to my earlier (and still relevant) writing on this subject. |
From: peterjamesthomas.com, home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases
I appreciate your insight on the challenges for managing data strategies in large organizations.
Thank you Patrick
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