Digiterre’s Rajesh Jethwa is named in the top 30 of the 2023 CIO 100
Coronavirus is causing unprecedented change in business and technology at a time when most organisations were already stretched by digital transformation. What does coronavirus mean for digital transformation and what implications does it have for data engineering which lies at its heart?
Digital transformation – the use of digital technology to solve problems – is accelerating due to the dislocations caused by coronavirus. Whilst it was always part of the effort to increase growth and reduce cost, digital transformation is now more relevant than ever. However, it is data transformation, in particular, that underpins most digital change – a fact that many organisations fail to appreciate. Given the seismic changes impacting all of us, it’s time to re-imagine the data maturity model, which helps us navigate digital transformation, and re-assess the optimal path through it.
The data maturity model charts the stages of data utilisation within an organisation. We can think of this as a journey with five stages.
First, in the “no data” stage, there may be very little known or useful data so the organisation is unable to run metrics and therefore has no information-based insights. Business decisions are unattributable – they literally cannot be attributed to a data source – and are as good as guesses.
Moving beyond this stage is a crucial milestone, and a peculiar experience. As soon as an organisation starts exploring its data in the digital age it will experience a deluge of data – it’s not like opening a tap, it’s more like opening the flood gates. This is the “big data” stage when businesses will be inundated with data and need to make the most of the data sources, data silos and scripted transfers. Contrary to what many believe, organisational decisions at this stage are still based on little or no useful information, so organisations need to progress to the next stage.
The “quality data” stage is when an organisation is able to create standardised sets of data, interfaces and reporting, and corporate taxonomy and metadata. There’s a cultural shift of organisational behaviour where decisions are based on explainable data.
The penultimate step in the data maturity model is the “predictive” stage when data is used to make all critical business decisions and organisations can perform predictive analysis as well as retroactive analysis – they can predict market behaviour.
Finally, the “strategic” stage is when data is fully embedded in all business processes and the entire business model is predicated on analytical models. No decisions proceed without forward looking analytic data.
The end point of digital maturity is optimal decision-making that maximises business value. Target outcomes include reduced cycle time, reduced defects, increased predictability of delivery, greater compliance with regulations, improved risk management and reduced cost. These outcomes are simultaneously harder to achieve, and more important, in a time of increased uncertainty – better decisions must be made with greater urgency that may have greater business impacts. This, in turn, requires the highest value, highest volume, most timely and highest fidelity data – which means organisations need to reach the top of the data maturity model.
Whilst the model may not change, how we address it, and the way we move through it, is already changing. Agile approaches to software development, which are characterised by the division of tasks into short phases of work and the continuous assessment and adaptation of work, will become increasingly powerful in the new normal. Previously, managing digital transformation using agile approaches was seen as incidental – just another tool in the tool box. Now, it is the only effective way to respond to, and succeed in, a dynamic, complex and unpredictable business environment. For example, an Agile approach can help identify and achieve quick wins whilst also planning and implementing longer term data transformation, maintaining productivity and staying focused on customer needs.
Technologists sometimes map digital progress, including the data maturity model, against Maslow’s hierarchy of needs. Coronavirus has made it painfully obvious that some organisations are languishing at the bottom of the hierarchy whereas others are operating very successfully at the apex. For example, there are businesses that could work remotely, or undertake key tasks outside the office environment, with the correct planning, but have not done the preparation and therefore cannot operate effectively in the current environment. Whereas other organisations are operating very successfully in the new climate either because they had robust business continuity plans in place, or because they worked hard, albeit reactively, to set up remote working and distributed teams, or a combination of both. The crisis has made plain a fact that some businesses only paid lip service to before – to be truly digital you need to master your data, there are different stages of data maturity, and agile approaches are more relevant than ever to navigate the journey.
By: Rajesh Jethwa
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