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Provide Data Support to the Select Few, Not the Many

Not everyone is in need of the all the 'right data, right now'

Many IT teams across different industries and parts of the world have focused a lot of their time and resources recently on answering calls from the rest of the business to provide more accessible, higher quality data.

This, argue line managers and other colleagues, will in turn help them make better decisions. But all this effort is often misguided, and based on assumptions that are increasingly untrue in the digital era.

Three Reasons to Disagree

While every company has different information priorities and digital objectives, there are three strong reasons why IT teams should stop focusing on improving data attainability and quality to support decision making – once they reach a certain degree of competence – and instead disproportionately prioritize the needs of data science, analytics, and digital product teams. Especially so, given these teams themselves now have a disproportionate influence on digital-based growth.

  1. Algorithms increasingly perform analysis, not employees: Across industries and employee roles, more and more companies use algorithm-based analyses to guide employee decisions.

    For instance, in capital goods companies, predictive maintenance sensors track and predict when new parts need to be installed. In the healthcare industry, medicine dosage tracking devices inform doctors when patients may need treatment intervention. Indeed, 50% of critical business decisions are estimated as likely be automated in 2020 (see chart 1).

    As such, IT teams should focus more of their resources on supporting the teams who create algorithms to automate analysis, not the employees who rely on information to support human judgment-based analysis.

    Chart 1: Trends in the business use of algorithms to support analysis and decision making

  2. Investments to improve information attainability and accuracy quickly hit diminishing returns: Once IT teams have got their company’s data to a basic level of attainability and usefulness, further investments to this end hit a large plateau with little additional benefit to the effectiveness of any analysis being conducted (see chart 2).

    Yet interviews with dozens of senior enterprise architecture managers in CEB’s networks showed that better data attainability and quality is a fairly common goal – and consumer of funds – for IT teams, all driven by the often misguided assumption this will deliver significant business value.

    Chart 2: Relationship of data attainability and usefulness to effectiveness at analytics  n=207 Quality staff  Source: CEB 2017 Quality Data Analytics Survey

    a = Index of effectiveness of Quality at using data analytics to: validate hypotheses or root causes through analytic techniques; improve the communication of quality related decisions with greater confidence; uncover information that was previously unknown; predict future quality problems; and automate processes and procedures that were previously manually intensive.

    b = Index of Quality staff’s agreement with the following statements: all the data that we could use to improve Quality performance is available to us; the data we need for analysis is easy to access; and the data we have available for access is accurate (e.g., consistently tagged, free of redundancies).

  3. Data science, analytics, and digital product teams will increasingly drive corporate growth: CEOs increasingly depend on initiatives executed by data science, analytics, or digital product teams to bring more revenue into the company, as reflected in CEO rankings of their investment priorities (see chart 3).

    Another interesting marker is the number of times that companies have mentioned artificial intelligence (AI) in their quarterly earnings calls. Prior to 2014, almost no companies were talking about AI when discussing their business with analysts. Since then, the number of mentions has risen exponentially.

    IT organizations may be wise to shift their own priorities to reflect these CEO-level changes. One IT team in CEB’s networks said that they now devote 90% of their information strategy to supporting these teams specifically and allot only 10% of their focus to everyone else.

    Chart 3: CEO rankings of investment priorities  Source: KPMG US CEO Outlook


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