Sixteen percent of the large organizations in our 2020 survey confirms that a lack of skills impedes their overall data strategies. At the same time, however, respondents indicate a marked trend to move skills in-house. This doesn’t seem like the best tactic when the relevant skill sets are in short supply.

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We’re now well into an age of ‘big’ data. More data is collected across organizations than ever before — and we’re talking about many different kinds of data, both structured and unstructured, often moving at high velocities.

How organizations handle this data can have a massive impact on business performance: indeed, 74% of organizations in our survey acknowledge that data has become a critical business priority. Handling data poorly can lead to inefficient resource use, whereas high-quality data analysis informs better business decisions that will feed straight through to the bottom line.

Obviously, that is a technology issue as well, and 80% of respondents do see data accountability and management as technological problems, but the critical reason data handling practices, policies, and the process must be ushered to the front of the queue is the business value of data.

We find emphasis being placed on speed (32%), cost (28%), and compliance (30%) among respondents, but not enough on the fundamental value of data insights (43%) – or the abilities of decision-makers to move towards developing new products and services from those very insights (41%).

Concomitant with this is the need for better skills to deal with data and deliver this business value. Nearly 60% of respondents in our survey indicate that they will recruit in the next three years, offering a clear opportunity for organizations to ‘skill up’ and add staff in-house.

Currently, only a quarter of large organizations tell us that they elect to have a chief data officer (CDO), with even smaller shares placing accountability for data issues and solutions with others in the C-suite. Perhaps businesses need schooling on the role of the CDO as the primary overseer of all the different aspects of the data strategy, also?

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A strong understanding of data complexity, data governance, operating models, and regulatory compliance are the most significant barriers to implementing a data strategy, according to our latest report. C-suites should start here and scale fast, focusing on getting strategy, governance, and repeatable approaches right, leveraging skills and technology already in place rather than merely pursuing a tactical technology-driven approach[i].

Developing the right capabilities takes time. While firms are investing in foundational skills, moving skills in-house, introducing new roles, third parties, and external contractors or consultancies can fill the gap. Currently, externals play a crucial role in enabling many organizations’ digital transformations, and this can prove highly satisfactory.

Taken together, the results of our survey clearly suggest that businesses are far from ready to exploit the vast potential of natural language processing (NLP), robotic process automation (RPA), and machine learning – let alone artificial intelligence (AI) – in future years.

These data-dependent trends will only become more critical across multiple markets, from manufacturing to healthcare, financial services, retail, entertainment, and many more sectors, as time goes on. Only 10% of respondents in our survey say they are using advanced analytics such as machine learning or big data capabilities in production environments as these are more business-led[ii].

As McKinsey has noted in its report, ‘Achieving Business Impact with Data’: “Capturing value from data depends on the integrity of the entire insights value chain, and the chain is only as good as its weakest component. Organizations looking to be successful in data insight must ensure excellence in all components and steps of the insights value chain.”

If organizations are struggling to maximize the value of their data, they’re certainly setting themselves up to fail long term.

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