Top 3 Data Strategy Mistakes (and How To Avoid Them) – Part 3 of 5

In Part 2 of this series I explained that the first common mistake in writing a Data Strategy is being unclear on business outcomes.  In this post, I move onto common mistake number two…

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Mistake #2: Unbalanced or Biased Capability Mix

The second mistake is another common one and manifests itself in several ways, often caused by the background of the Strategy’s author.

Does your Data Strategy put a massive emphasis on governing and controlling your data, about legal and regulatory compliance and making sure Data Quality is at the centre of everything you do, without really explaining how doing this will drive any real value for your organisation?

Is your Data Strategy super-focussed on Data Analytics and Insight, about creating new Data Lakes and Data Warehouses and Data Marts and doing whizzy new things with the Data, without referencing anything to do with how Data is captured and processed across your business before it reaches the analytical layer?

Or is your Data Strategy really just an excuse to push a technology overhaul, with new Master Data platforms, APIs, advanced Data Services, Big Data and AI/ML capabilities?

Whilst any one of these Strategies may well the right for your business, if your Data Strategy doesn't at the very least reference the broad range of options and capabilities available, ideally with an explanation of the proposed decisions and what trade-offs these decisions entail, then the likelihood is that you could be missing a great opportunity.

The most common reason that you will find a Data Strategy that is very biased towards a particular set of capabilities is that the person that wrote it probably has that background.  Whilst it is increasingly common that Data Professionals have a breadth of experience across multiple different disciplines and styles of approach, it is also very common for people to "grow up" through particular career paths that lend themselves to one style of Strategy or another.

Three key ways to mitigate these biases are:

1. Ensure clear alignment to business outcomes (per mistake #1): some capabilities are better at supporting some outcomes than others; and if a set of capabilities are being proposed that do not clearly support a business outcome then they are probably being proposed because the author just thinks they're a good idea, rather than because there's a real need;
2. Explicitly get input from a representative sample of different stakeholder groups, including business, technical, legal/compliance and any other key audiences that will both benefit from the Strategy and will play a role in delivering it.  Sometimes seeking external validation of your Data Strategy from an independent Data Expert can help to make sure you've not missed anything too;
3. Make sure there's a specific section either in the Strategy or in its appendix that looks at alternative approaches and explains the approach that has been taken, based on an analysis of pro's and con's.

Some Data Strategies will by necessity have a focus on a particular set of actions that are biased towards the outcomes that an organisation is aiming to achieve; however, the best strategies consider a range of options before consciously committing to a particular path, with clear rationale behind any proposed trade-offs or areas of emphasis.

Coming up in Part 4…

In part 4, I will explain the third of three common mistakes, related to how the strategy will achieve its vision…