Top 3 Data Strategy Mistakes (and How To Avoid Them) – Part 4 of 5
In Part 3 of this series I explained how a Data Strategy needs to be balanced and consider a variety of deliverables and capabilities in order to be most effective. In this post, I explain the last of the three common mistakes in this series...
Please read my Disclaimer by clicking here.
So this is the next obvious thing to look out for.
Some Data Strategies are just pure theory. They're really an elongated Vision statement, without any detail on a roadmap to getting to the described outcomes.
Conversely, some Strategies are super-detailed plans; so, beware the opposite end of the spectrum, where you're presented with reams of Microsoft Project printouts, or overly-busy glossy Gannt charts with spaghetti dependency maps.
Another common mistake relating to this in some Data Strategies, is where a roadmap is proposed that depends on lots of other parts of an organisation doing things, where those other parts of the organisation haven't actually signed up to doing those things. For example, roadmaps that depend on a department investing time and money building out a new platform, which they have not committed to do. If there's a plan that calls out lots of other business areas, make sure sufficient business engagement and commitment from those business areas has been established before you accept whatever the Data Strategy is suggesting.
Does your Data Strategy outline specific actions and who will do them? Does it give an idea of the timescales for achieving them? Does it propose metrics that will be used to track progress?
A good Data Strategy provides a sense of what needs to be done to achieve the outcomes that it sets out and how those things will be achieved. It is presented at an appropriate level of detail for the audience of the document to digest and understand it, and with sufficient information to give confidence that the proposals are achievable. Ideally it will also articulate some idea of how progress will be tracked and timescales.
Please read my Disclaimer by clicking here.
Mistake #3: Missing the "How"
A Strategy is a document that articulates how to get somewhere. It is not just a Vision statement with no concept of how your Vision will be achieved. Vision is important; and as described in Part 2, must be linked to Business Outcomes. However, a Strategy without some concept of the steps to achieving an end goal, is not really a Strategy at all.So this is the next obvious thing to look out for.
Some Data Strategies are just pure theory. They're really an elongated Vision statement, without any detail on a roadmap to getting to the described outcomes.
Conversely, some Strategies are super-detailed plans; so, beware the opposite end of the spectrum, where you're presented with reams of Microsoft Project printouts, or overly-busy glossy Gannt charts with spaghetti dependency maps.
Another common mistake relating to this in some Data Strategies, is where a roadmap is proposed that depends on lots of other parts of an organisation doing things, where those other parts of the organisation haven't actually signed up to doing those things. For example, roadmaps that depend on a department investing time and money building out a new platform, which they have not committed to do. If there's a plan that calls out lots of other business areas, make sure sufficient business engagement and commitment from those business areas has been established before you accept whatever the Data Strategy is suggesting.
Does your Data Strategy outline specific actions and who will do them? Does it give an idea of the timescales for achieving them? Does it propose metrics that will be used to track progress?
A good Data Strategy provides a sense of what needs to be done to achieve the outcomes that it sets out and how those things will be achieved. It is presented at an appropriate level of detail for the audience of the document to digest and understand it, and with sufficient information to give confidence that the proposals are achievable. Ideally it will also articulate some idea of how progress will be tracked and timescales.
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