Getting the data foundations right for digital transformation

Driving successful data transformation within an organisation requires a combination of senior leadership support, a clear link to the overall business strategy, and a willingness to take an incremental, evidence-based approach. That’s according to Lee Almond OBE, who has led data transformation efforts across a range of public and private sector organisations.

Almond, who is now VP of consulting at IT services firm CGI, has held data-focused roles at HM Revenue & Customs (HMRC), the Home Office, and the National Crime Agency over the course of her 20-year career in central government.

“Data is the second most important asset to any organisation – people are first,” Almond told attendees at the recent Think Data for Government event (pictured). “But your data must be second, and you must invest in it in the same way. If we don’t, it causes so many problems, both in terms of customer outcomes, overhead costs, inefficiency, all sorts.”

Almond’s passion for data transformation was sparked during her time at HMRC, where she led the IT work around the implementation of the Real Time Information (RTI) programme, a major change to the way Pay As You Earn (PAYE) operated.

“I absolutely loved it. I loved getting into the thick of it to understand where the problem was. Was it to do with how it was all structured at the front end? Then, the various fields weren’t matching the back end. And what was the outcome for the customer where people were not getting their student loan payments right, inheritance tax wasn’t working, the tax code wasn’t taking the right tax – the different impacts were endless,” she said.

That experience taught Almond the importance of getting to grips with data issues and configuration challenges across multiple systems in order to deliver successful transformation.

While Almond has worked across both the public and private sectors, she believes the core themes around data transformation are largely the same, with the main differences lying in the specific drivers and governance structures.

“I thought when I joined the private sector – I’ve pretty much been a public servant most of my life – I thought it would be so different. I don’t think it is. There are different drivers. And even the different public organisations I’ve worked for over my career had different drivers, because they have different missions, different objectives,” she said.

Regardless of the sector, Almond emphasises the importance of securing senior sponsorship for any data transformation programme.

“If you haven’t got the senior sponsorship of your organisation, just makes it so hard to continue to keep the investment,” she said.

Building a robust evidence base to demonstrate the case for change is also crucial, Almond added. This includes quantifying the organisation’s ‘data debt’ – the costs associated with storing unused data and manual data processing.

“Everyone talks about technology debt. Shouldn’t we be looking at our data metrics as well? Like, what’s the data debt of your organisation? Do we know how much data hasn’t been accessed in several years, but you’re paying the storage costs? How much does that cost in your organisation? How many of your applications have automation around the management of your data and how much is still manual?”

Rather than tackling data transformation as a single, monolithic programme, Almond advocates a more phased, incremental approach, breaking the work down into smaller, more manageable chunks that are clearly aligned to the organisation’s overall strategy and objectives.

She added: “Don’t worry if you hit a bump in the road – these things happen. Use your network, networks across other organisations that have already gone through the journey, or going through a journey that you’re about to embark on. Draw on that network, because, no doubt, somebody’s been through whatever scenario you’re going through already.”