Take DataOps approach for quick digital transformation wins

Data is a pivotal part of any modernisation initiative, so it must be the first consideration when driving a digital transformation journey.
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Digital transformation remains a top priority for every organisation as they move into a new era of work and business, with the Gartner CIO Agenda 2021 noting that 69% of boards report accelerating digital business initiatives in response to COVID-19.

In South Africa, we see large enterprises and even small to midsized businesses looking to digital transformation to reduce costs, become more agile and support resilience in an uncertain future.

Organisations are striving to improve customer service, increase operational efficiencies and enhance innovation through digital transformation.

But many organisations hit a wall in their digital transformation journey when they discover their data is not mature enough to support their transformation ambitions.

Data is the key to unlocking any digital transformation journey. in fact, a recent joint study from Informatica and DataIQ explored the digital transformation journey of over 100 organisations and found the higher their level of data maturity, the more easily companies could respond to changing market conditions.

Since data is a pivotal part of any modernisation initiative, it should be the first consideration when driving a digital transformation journey.

Unfortunately for many organisations, their data maturity is nowhere near where it should be to support digital transformation. Every organisation has the data residing somewhere, as it is part of their business processes. However, they do need to understand where it is, apply proper governance and leverage it correctly to get full value from it.

Data is the key to unlocking any digital transformation journey.

Many don’t even know the state of their data; they make assumptions about it, and continue making assumptions until something goes really wrong. They do not have insight into whether their data is accurate, up to date and what their sources are.

One common problem is around roles and responsibilities for ownership of the data. Who owns what piece of data? Who has authority to sign off on changes to be made to the data?

Data ownership needs to be defined. Organisations have to address the basics of quality and data governance, encompassing ownership, roles and policies on who can access the data.

In large organisations with massive volumes of data, this can be challenging, which is why many organisations shy away from it until a digital transformation project forces them to address their data problems. The scope of work required to make the data and entire environment ready to support transformation can be truly daunting.

This is why we often see data projects running in parallel with digital transformation projects. However, this isn’t an ideal scenario and puts a great deal of pressure on the teams involved.

For data maturity, organisations need a strong governance foundation in place, with a strategy defining where data ownership sits, policies around use of the data, and who is responsible for data quality, metadata management and integration aspects.

However, addressing data challenges need not happen in a single mammoth project: organisations can achieve quick wins by tackling data challenges and digital transformation in small, manageable projects with the greatest impact for business.

With a compelling use case with clear boundaries and defined success metrics as a starting point to their digitisation, organisations can then move to finding underlying processes and data sources right down to the data fields and attributes that contribute to those use cases.

Then, they can focus on quality, metadata and integration of that data for a quicker win and build on that win later. This might be seen as an agile data management approach, or a DataOps view.

Having a broader roadmap is a good approach when embarking on a DataOps model for data and digital transformation: think big, but start small.

Every organisational goal − revenue growth, customer retention, operational efficiency or improved customer experience − can be broken into bite-size, achievable chunks to ensure continued success, and can then be scaled and added to in an iterative approach.

This model also mitigates the risk of catastrophic failures, which can happen when organisations attempt to eat the proverbial elephant whole.

By addressing one area of business that has a burning need for digital transformation, and successfully improving it, teams will also benefit from executive buy-in and funding for future projects. This is important, since many digital transformation programmes fail due to a lack of executive sponsorship.

Veemal Kalanjee

MD of Infoflow.

Veemal Kalanjee is MD of Infoflow, part of the Knowledge Integration Dynamics (KID) group. He has an extensive background in data management sciences, having graduated from Potchefstroom University with an MSc in computer science. He subsequently worked at KID for seven years in various roles within the data management space. Kalanjee later moved to Informatica SA as a senior pre-sales consultant, and recently moved back to the KID Group as MD of Infoflow, which focuses on data management technologies, in particular, Informatica.

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15 Aug
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