Organisations Setting up to Fail as They Struggle to Maximise Value from Their Data

Coeus Consulting, data strategy, data investments, analytics, machine learning (ML), Artificial Intelligence (AI), natural language processing (NLP), automation capabilities
Organisations Setting up to Fail as They Struggle to Maximise Value from Their Data

Coeus Consulting, an award-winning independent IT advisory, announced new research into the approaches organizations are using to drive value from their data. The report – Beyond Technology: How can Organisations drive Sustainable Value from Their Data Investments? – highlights that many organizations are potentially failing to realize the potential value of, or monetize, their data despite 74% acknowledging it as a key priority.

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The survey found that in 80% of the large organizations surveyed, believe accountability for data strategy rests with technology leaders such as IT Directors, CIOs or CTOs. Additionally, only a quarter of organizations currently electing to have a Chief Data Officer, and even less placing accountability with others in the C-suite. Emphasis is being placed on speed (32%), cost (28%), and competition (30%), but less so on more fundamental underlying value, the insights it offers, or decision-makers’ ability to develop new products and services from those learnings.

According to one source, DATAVLT, only one percent of the data companies collect is currently analyzed, and they expect as many as 96% of businesses that exist today to fail in 10 years.

“Many investments in data and analytics have been started from a technology perspective with little alignment to business value or desired outcomes that can be measured against a business strategy. Businesses need a change of mindset and approach right across the organization, and the challenge is more than simply collecting data and making it available”, commented Richard Graham, Associate Director, Coeus.

However, the survey did find that 66% of respondents are actively trialing the use of machine learning (ML), Artificial Intelligence (AI), natural language processing (NLP) and automation capabilities. Yet, only 39% admit to widely using data lakes and warehousing, suggesting that organizations have either not completed these activities or are not placing enough importance on them.

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“Being data-driven is imperative for most organizations and there is a growing trend to incubate and deploy advanced analytics, but organizations need to ensure they have certain fundamental capabilities in place before trying to achieve digital transformation.

There seems to be a motivation to be ‘AI first’, perhaps driven by the perception that most organizations are already ahead in using these capabilities, rather than getting to grips with untapped value in existing data, and how best to make use of it” noted Graham.

The survey results highlight that there are many obstacles to overcome before companies can begin to see meaningful benefits from the data available through technology-led investments such as AI. Of the top five enterprise data bugbears, the majority are business-related: the scale and complexity of data sets (27%); governance and ownership (24%); the lack of a data operating model (19%); regulatory compliance issues (19%); and difficulty in integrating new technologies.

Organizations are facing tougher regulatory environments and when asked to express their concerns about data regulations, compliance with ethical and moral requirements was the biggest, cited by 49% of respondents. This has obvious implications for data management, analysis, and technology buying decisions, and the potential reputational and financial repercussions.

16% of respondents also stated that a lack of expertise and skills is a major obstacle. Whilst companies are investing in foundational skills, moving skills in-house and introducing new roles, third parties and external contractors play a key role in enabling and supplementing these organizations and will continue to do so.

When asked where organizations are seeing the biggest benefits from their data initiatives, 43% of respondents said they are in ‘improved customer insights’, while 41% identified an improved ability to take proactive, predictive decisions. Improved reporting’ also scored highly, along with better management of risk and regulatory compliance (37%). However, only 24% cited an increased ability to meet customer needs, with 20% citing a greater ability to spot future business opportunities.

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In contrast, just 12% identified attracting new customers as a core motivation – the least favored option on the list, this is despite the realization that improved customer insight could be the biggest benefit.