IT Worked In Dev’ – A new series from SCC’s Digital Automation Practice
Part 1: The automation journey starts with data
Boardrooms are full of ambition for automation and AI, lower cost-to-serve, faster resolution times, better employee experience, stronger compliance posture. In our opening conversation for this new series, SCC experts kept coming back to a quieter truth. Most automation programmes are constrained less by tooling and more by the condition of the data beneath it.
The stakes are high.
McKinsey notes that 70% of transformations fail, a useful reference point for any leader treating automation as an operating-model shift rather than a software rollout. And Gartner estimates that poor data quality costs organisations at least $12.9 million per year on average, which frames data readiness as a material value lever, not an IT hygiene task.
This is first of a series from SCC’s Digital Automation Practice, reported from conversations from experts across SCC. Each week we will bring in additional voices, specialists across service management, automation engineering, data foundations, governance and managed services, then go deeper into sector pressures and real-world constraints.
For the opening conversation, we spoke with Chris Lowery a Solutions specialist and Duncan Castle from Digital Automation who work across service management platforms, including ServiceNow and others and the data disciplines that determine whether automation scales.
“If you try to pick up all the clutter you’ve got… you’ll end up in the same place”
Chris set the tone with a straightforward recommendation. Start by rationalising the data you already have before you automate anything on top of it.
He warned against migrating clutter from one place to the next. Lift-and-shift the mess and you risk ending up where you started, if not worse.
He described an all-too-common pattern. Data scattered across unstructured databases, emails, spreadsheets and people’s machines, alongside twenty-two systems that all do roughly the same thing.
In that environment, extracting insight takes an extreme amount of work and what you produce becomes obsolete quickly.
The operational point for executives is not that data is messy. Automation amplifies what you feed it. Without a clear view of which data is required for target outcomes, organisations risk importing noise and institutionalising confusion.
Service management is where AI becomes real. And where bad data shows up fastest
The conversation focused heavily on service management as a practical launching point for workplace automation. Chris positioned the practice’s scope as helping how an organisation operates day to day, particularly where work crosses people, systems, teams, suppliers and external parties.
He offered a simple maturity ladder. In less mature environments, the priority is often making work visible because it is otherwise trapped in email and spreadsheets. As maturity improves, organisations can move toward orchestration and automation that goes out and fixes something, touches a device and is supported by analytics and AI.
Chris illustrated what good looks like from a service desk perspective. When data is consistent, leaders can request instant trend reporting. When it is not, tickets are miscategorised, routed incorrectly and can sit there festering, creating poor experience and wasted effort.
His message was clear. Getting your data right in the first place will enable you to go way faster in the long run.
The human cost: retention and experience are data problems too
Duncan connected data quality directly to retention and customer experience. Poor data equals poor experience and frustrated frontline teams dealing with systems that are crap are at real risk of churn.
This is not only intuition. Adaptavist’s 2025 research reports that 23% of knowledge workers have looked for a new job due to workplace technology and 5% actually quit for that reason. This gives executive audiences a credible, people-centred proof point. The quality of your operational data and systems is increasingly a workforce strategy issue, not only an IT concern.
Why “buy the platform” thinking breaks automation programmes
Duncan used an analogy that will resonate with leaders who have seen major platform investments under-deliver. If you’ve got a Ferrari and you put diesel in it, it is not going to work very well.
He described a recurring misconception. Buy ServiceNow, press the green button and kapow.
The platform is an enabler, he said, but outcomes depend on processes, data management and planning.
Chris made the governance implication explicit. Organisations should plan for data governance, including Master Data Management and in the ServiceNow context align to best-practice models like the Common Service Data Model so everything will work properly.
The integration trap: automation slows down when data can’t move safely
A major part of data management, Chris argued, is integration strategy done in a way that remains scalable and governable. Without that, future integration requests are pushed back because of the cost involved in building, testing, rolling out and sustaining one-off connections.
He also highlighted that surfacing data securely matters as much as moving it. Approaches should respect role-based access so only the right people can see the right things.
How SCC’s Digital Automation Practice fits: why an expert partner changes the odds
If 70% of transformations fail, the executive question becomes how to beat the odds. The conversation with Chris and Duncan points to a consistent answer. Success is less
about buying technology and more about building a repeatable capability across data, process, governance and ongoing control.
Chris described data and service management as inherently cross-disciplinary inside SCC. The work draws on platform specialists alongside broader data management stakeholders because the automation outcome depends on both the data structured and the service platform built on top of it.
The conversation stressed the operational reality of platform stewardship. ServiceNow is powerful but pretty complex and unmanaged customisation can make upgrades a disaster, especially with two releases a year.
That is where an expert partner reduces risk. Not only in implementation, but in keeping the platform upgrade-safe, governed and value-generating.
SCC’s role is not more hands. It is pattern recognition and preventative control. Rationalising and right-sizing data before migration, aligning to governance and data models, implementing automation in a way that survives platform change and designing integrations that scale so the programme does not stall after the first wave of wins.
Coming Soon
Part 2 will focus on a problem leadership teams raise repeatedly. How to prioritise automation use cases without creating a graveyard of pilots. We will add further SCC voices from discovery, engineering and governance to outline a sequencing approach that ties data readiness to measurable outcomes.
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Editor : Julian Gustea, Software & Security, Marketing UK, SCC