Key Points
- Research from UKISUG and Syniti reveals significant data quality and accessibility gaps among UK SAP users, with only 4% very confident in data quality and 2% in data accessibility.
- Survey of 46 SAP user organisations across UK and Ireland shows 94% cite data silos preventing real-time decision-making.
- 89% of respondents state data challenges will slow AI adoption, while 87% say high-quality data is essential for AI ROI.
- Half of respondents now have comprehensive or near-comprehensive data strategies, up from 40%, and 79% prioritise data transformation.
- Data governance and compliance concerns top the list at 72%, but only 21% use dedicated data quality tools.
- 76% view data management as a major challenge in SAP S/4HANA migrations, with 27% citing lack of business resources for data cleansing and 22% data quality delays.
- SAP’s separate survey indicates 92% of UK high-growth businesses prioritise standard AI applications, but one-third report insufficient datasets and poor data quality hindering AI model training.
- 34% struggle with AI talent recruitment, prompting 40% to prioritise skills development.
- Legacy technology impedes 27% of organisations in scaling AI.
- SAP Engagement Index UK highlights data silos affecting CX, with 63% unable to access real-time customer data and 71% holding unusable “dark data”, limiting AI personalisation.
UK SAP users are grappling with profound data deficiencies that are stalling their artificial intelligence ambitions, according to a fresh survey by the UK and Ireland SAP User Group (UKISUG) and Syniti. The study, covering 46 organisations, exposes alarmingly low confidence in data foundations amid rising investments in data strategies. Nearly all respondents flag silos and governance woes as barriers to timely decisions and AI progress.
What Triggered This Latest Warning on SAP Data Issues?
As reported by Sofiah Nichole Salivio of IT Brief UK, research from UKISUG and Syniti demonstrates that UK organisations confront substantial gaps in data quality and accessibility. The survey encompassed 46 SAP user organisations across the UK and Ireland. Sofiah Nichole Salivio writes that the findings indicate a disconnect between escalating investment in data programmes and waning confidence in the underpinning information.
Half of respondents affirmed they now possess a comprehensive or near-comprehensive data strategy, an increase from 40%, while 79% declared data transformation a business priority. Nevertheless, confidence in data lingers at dismal levels. Just 4% of organisations professed to be very confident in data quality, and merely 2% echoed that sentiment regarding data accessibility. Almost all respondents—94%—asserted that data silos obstruct real-time decision-making.
Sofiah Nichole Salivio notes that the results imply organisations are unearthing deep-seated frailties as they advance with expansive transformation initiatives. Rather than fostering assurance, heightened data maturity seems to spotlight persistent issues with fragmented records, inconsistent standards, and inadequate access to information.
Why Are Data Silos Crippling SAP Users’ Decisions?
Data silos emerge as a pervasive scourge, with 94% of UKISUG-Syniti survey participants identifying them as blockers to real-time decisions. This fragmentation not only hampers operational agility but also undermines strategic initiatives like AI deployment.
In SAP’s Engagement Index UK report, fragmented data and siloed systems limit AI-driven personalisation and engagement. Specifically, 63% of UK organisations cannot access or utilise customer data in real time, while 71% harbour “dark data” that remains inaccessible or unusable. These constraints directly curtail AI effectiveness, despite 80% of businesses deeming AI vital for customer retention.
Sofiah Nichole Salivio reports that data governance and compliance rank as the paramount concern, invoked by 72% of respondents. Concurrently, only 21% deploy dedicated data quality tools, intimating that numerous entities persist in tackling foundational data quandaries sans specialised systems.
How Do These Gaps Affect SAP S/4HANA Migrations?
Data management looms large in SAP migrations, with 76% of respondents deeming it a principal challenge during transitions to SAP S/4HANA. Half anticipate overhauling more than half of their data and attendant processes in this endeavour.
The survey pinpoints pragmatic hurdles: the foremost cited migration snag is the dearth of business resources for data cleansing, nominated by 27% of respondents, trailed by data quality issues precipitating delays, at 22%. Sofiah Nichole Salivio observes that for SAP user organisations, this holds acute relevance since migrations and transformations hinge on consistent, accessible, and trustworthy data spanning business functions. Absent these, projects decelerate as teams allocate extra effort to cleansing, validation, and reconciliation.
Legacy technology compounds the issue, obstructing 27% of organisations from scaling AI implementations, as per SAP’s survey on high-growth UK businesses.
What Is Slowing AI Adoption Among UK SAP Users?
The UKISUG-Syniti research explicitly ties feeble data bedrock to languid AI uptake. A substantial majority—89%—contend that data challenges will impede AI adoption, whereas 87% insist high-quality data proves indispensable for realising returns on AI investments.
Sofiah Nichole Salivio highlights that these apprehensions arise as myriad organisations grapple with surging data volumes, occasionally propelled by their proprietary AI endeavours. The data intimates enterprises contend not solely with historical legacies but also the exertions of overseeing vaster, more intricate datasets.
In SAP’s “Supercharging growth with AI in the UK” survey, data quality poses another impediment, with one-third of companies lamenting insufficient datasets for AI model training—and the identical fraction pinpointing subpar data quality and planning tools as progress impediments. Wesley Doyle, Head of New Business, Corporate at SAP UKI, states: “UK high-growth businesses have a real opportunity. Those that can harness AI to streamline operations, adapt to change and drive innovation will set themselves apart in a fast-evolving market. Addressing barriers to growth, like digital immaturity, must be a priority for the UK’s high-growth organisations if they wish to reach their potential.”
Simon Duckett, Chief Digital Officer at Sonnedix, remarks: “AI is revolutionising the renewable energy sector, driving smarter decisions and operational efficiency to support growth and resilience. In a rapidly evolving market, AI’s predictive models and advanced analytics help us strengthen our supply chain, adapt to economic uncertainty, and seize new opportunities. By combining cutting-edge technology with human innovation, AI ensures we remain agile and prepared to scale and adapt to change sustainably.”
Which Broader Challenges Plague UK SAP AI Efforts?
Resource scarcities persist as a cardinal operational plight. Even where organisations grasp the imperative to ameliorate data, procuring personnel adept at cleansing, governing, and reconfiguring it proves restricted, intensifying strain on labyrinthine transformation programmes.
SAP’s research underscores a skills chasm, with 34% beleaguered in recruiting AI-proficient staff, spurring 40% to elevate talent development in growth blueprints. Meanwhile, 92% prioritise standard AI applications for the coming year, and 91% probe generative AI, yet digital immaturity in talent, data, and infrastructure stymies success.
In SAP Concur’s insights, 56% of IT leaders cite data quality as a pivotal AI adoption pain point, impeding endeavours like fraud detection and cash flow forecasting. The firm posits UK data tribulations stem from culture rather than technology, with patchy governance and muddled ownership prevalent.
What Do Surveys Reveal About Confidence Levels?
The bleakest metrics from UKISUG-Syniti: a mere 4% evince very high confidence in data quality, and 2% in accessibility. Sofiah Nichole Salivio underscores that although the survey spans 46 organisations, it furnishes a vignette of data aspirations materialising amid SAP users in the UK and Ireland. It posits many enterprises have transcended viewing data peripherally, only to discover foundational repairs demand unforeseen labour.
SAP’s high-growth survey echoes data as a growth obstacle, with one-in-three flagging inadequate datasets and 34% poor data/planning tools.
How Can UK SAP Users Overcome These Data Hurdles?
Though the UKISUG-Syniti survey proffers no explicit remedies, it spotlights imperatives like bolstering governance, deploying quality tools, and allocating cleansing resources. SAP advocates tackling digital immaturity via infrastructure upgrades and skills investment.
Wesley Doyle emphasises prioritising these barriers for high-growth firms. For SAP users eyeing S/4HANA and AI, putting data first—via strategies transcending IT silos toward C-suite stewardship—looms critical.
Professionals in SAP environments navigating data complexities for AI might benefit from targeted SAP Training to master governance, migration, and integration best practices. Such expertise equips teams to bridge gaps efficiently.