Companies are modernizing their data infrastructure as part of AI initiatives to gain competitive advantages. How can technology providers help businesses solve data management challenges?
Businesses are pursuing a range of AI initiatives, and modernizing data infrastructure tops the list. But current data practices are an issue, as several companies haven’t attained a high level of sophistication with crucial data-related aspects. A Deloitte study of AI adopters finds businesses face challenges in critical aspects of data management: preparing and cleaning data, integrating data from diverse sources, training AI models, and ensuring data governance.
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In Deloitte’s latest State of AI in the Enterprise survey, at least 40% of adopter organizations reported “low” or “medium” level of sophistication across a range of data practices.1 Moreover, nearly a third of executives identified data-related challenges among the top three concerns hampering their company’s AI initiatives.
Is cleaning data really that difficult? Research suggests that, indeed, cleaning crude and inaccurate data before feeding it into an AI model is a cumbersome process. For instance, companies now routinely spend six to 12 months cleaning the data.2 Cleaning data is vital, as the cost of adjusting poor data rises dramatically later in the process.3
Data preparation demands persistence and disciplined execution. Data specialists spend a large part of their workweek preparing data for analytics and AI/machine learning (AI/ML) initiatives.4 As more organizations shift their AI workloads to a cloud environment, data integration challenges are intensifying. Some of the most common barriers to access third-party data sources include dealing with disparate data that exists on different systems and merging data from diverse sources.5 For all these efforts, the right talent and expertise can be critical. Often, AI/ML initiatives fail primarily due to lack of expertise, besides other major factors that include unavailability of production-ready data and integrated development environment.6
To add to these issues, data governance is fast gaining prominence as a problem spot. A 2019 study found that more than half of organizations lacked a formal data governance framework and a dedicated budget to address the issue.7 And even as regulatory scrutiny has intensified worldwide, Deloitte’s State of AI survey finds leaders “highly concerned” about a lack of data policy for personal data use. A shortage of specialists and difficulty in building a comprehensive data strategy are among the top challenges impeding data governance efforts.8
If these various data management and governance issues are not addressed early on, deeper issues could emerge later to fracture AI initiatives. AI technology providers can play a role in supporting businesses to navigate shortcomings related to data practices by:
These steps can enable the adopter organizations to develop a holistic data-based AI strategy that scales with and adapts to their changing needs and demands. As Deloitte LLP US technology sector leader Paul Silvergate notes, “Gleaning information from data—and then a competitive advantage from that information—requires a clear view of where the business is going, coupled with a tight link between the business and IT.”
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