Data strategy: The bedrock of successful transformation
Organisations have embarked on their digital transformation to varying degrees and in a multiplicity of ways, from a more gradual, organic evolution via a series of projects to a holistic, fully-planned ‘root and branch’ core programme and everything in between.
However, one of the areas we see often overlooked is data strategy. A somewhat abstract concept, considering the vast amounts of data produced automatically via systems, but data strategy is , as MIT CSR Data Board describes, “a central, integrated concept that articulates how data will enable and inspire business strategy”. It is the plans for the capture, management, enrichment and commercialisation of organisational data to result in information which is actionable, timely and relevant for decision making.
This post covers why having a data strategy ensures the potency of your efforts and why you should have one, what are the considerations, learnings
Core anatomy of a data strategy
There are already excellent materials available which can elaborate on the critical components of a data strategy, however some critical components the strategy needs to address are outlined here:
The absence of a coherent strategy around organisational data gives rise to the risk that vast amounts of data is generated, stored, processed but never effectively used. The opportunity cost of not having a data strategy are even more considerable in the days of automation and RPA.
- What data is needed by the organisation now?
- What formats does the date?
- What data is must have, nice to have, not needed?
- How do we acquire the data? Is it 1st, 2nd or 3rd party data? How do we capture the data and validate its accuracy?
- How and where do we store the data?
- Do we understand the compliance issues around this?
- What integration requirements do we have to ensure data accuracy?
- How do we manage the data?
- What functions, systems or skills are required to
- How do we secure organisational data?
- What resources are required to do so?
- What data security and permissions settings need to be applied to mitigate operating risks
- How will we use the data (to enable what?)?
- How accessible is the data? How can we make available across the data
- How can we commercialise our data? What data is most valuable and to whom?
Towards a more collaborative, commercial data strategy
It is an important step to indeed have and fully implement a data strategy. However where it can and often does fall down is in the area of organisational alignment.
Taking a democratic view of the value of data from internal teams can result in a bloated view of what is critical to the organisation. The results, as one can easily imagine, are significant, unnecessary and avoidable costs incurred managing, collating, storing, cleansing, processing, analysing, protecting and sharing data of little organisational value.
It is necessary therefore to ensure we take a ‘lean’ approach to data, starting with the question: as an organisation, how will this data be put to work to generate economic value?
In this way, organisations should strive towards what one might call minimum viable data – the minimum data the organisation needs at a specific point in time to operate at maximum efficiency. Of course, data strategy also has a significant longer-term horizon, as data required tomorrow needs to be captured today in order to be actionable at the time it is required.
Transformation and data strategy – a symbiotic relationship
And so to transformation. The enormous costs associated with giving effect to multi-year business transformation programs obviously necessitates an accurate, data-informed picture of the organisation ‘as is’ and ‘to be’. A key initial question is ‘how can we gain the most accurate understanding of our current position and what data points will we need’?
You will have heard the phrase ‘data is the new oil’. In a metaphorical sense there is enormous truth to this, but oil itself can do nothing unless it can be located, extracted, refined. Data strategy is what enriches the value of data and avoids often latent costs.
It is very common for organisations embarking on their transformation journey not to even have the initial data points required (or at least not in formats which make it easy to deduce from) to assess their starting position. To gain this understanding a data strategy is vital.
Once an initial picture has been formed, data strategy will need to be reviewed and refreshed to ensure that the organisations position can be mapped against its starting position. As a result, data strategy should become part of the annual planning flow to ensure that requirements are understood and met.
Once the organisation has a foundation layer of accurate, timely, and accessible data, it should then feed back into and inform the ongoing transformation and enable its evolution in line with the opportunities and threats facing the business.
Without a data strategy, opportunity costs are significant
We are all aware to varying degrees of what is occurring at the digital forefront, with respect to automation, intelligent workflows, robotic process automation and machine learning for example. The value extracted from these extremely valuable advancements is predicated on data. Nothing can take place without foundations of data
Having a clearly articulated strategy around data or otherwise, could be a decisive factor in whether your organisation undergoes successful transformation, or gets left behind in the digital dust of its current (or future) competitors.
- Data is a highly valuable organisational commodity and necessitates a strategy for capture, management, enrichment and commercialisation.
- Alignment around what is important as a collective is critical. Taking point surveys of individual teams on the importance of specific data will inevitably result in data bloat i.e. ‘everything is important’. Ensuring you take a ‘lean approach’ to data, collecting and managing only what is critical for your organisation at a point of time will ensure your strategy is efficient and aligned, and that data can be deployed to deliver maximum incremental value.
- Invest in and renew your data strategy as part of your annual and 3 year planning processes
- Prioritise you data using MoScoW methods. Every piece of data across the organisation must have an ‘owner’. If it doesn’t do not collect it.
- A coherent data strategy ensures you can operationalise your data for analysis, insight, marketing and sales
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