Hyper-Decisive® Organizations Align Data and Analytics Strategies With Business Objectives and Outcomes

During a roundtable discussion on the subject of “Toward a Modern Analytical Architecture and Technology Stack” during our most recent Real BI Conference, the chief data officer (CDO) of a technology company shared a recurring observation of general bias toward focusing technology deployment on functional roles, rather than aligning it first to business objectives and requirements.

Our data also support the observed bias in favor of technology. More than 50 percent of organizations consider it somewhat difficult, difficult, or impossible to find analytic content. Survey participants reporting these levels of difficulty overwhelmingly (86 percent) come from functional roles aligned to data and analytic content (IT, finance, BICC, R&D, and executive management).

The CDO shared an analysis conducted on data professionals within four Global 500 companies. Using data sourced from LinkedIn, the CDO determined that data professionals—those with roles related to data science, data engineering, data warehousing, BI, and line-of-business analysis—on average comprise about 5.5 percent of overall organizational headcount. If these individuals report difficulty in finding analytic content more than half the time, that suggests a likely much higher overall level of difficulty across the entirety of organizations.

Organizations that achieve higher levels of frequency in data-driven decision making report differentiated performance relative to their peers. Key to achieving higher levels of data-driven decision making is increased awareness of and broad access to data and analytic content. This attribute is expressed within higher levels of maturity of the Dresner Advisory Services’ Hyper-Decisive® Maturity Model (HDMM) by the need for and importance of trusted data and analytic content becoming readily available and broadly accessible throughout the organization. This is also reflected in our data on data leadership (CDOs and/or chief analytics officers, or CAOs) and data literacy.

Overly broad initiatives to “improve data and analytics” tend to have low rates of success. We recommend organizations take a more focused approach. Make use of the key external forces to characterize business value chains critical to the organization. This identification in turn will lead to critical business processes, and the data and analytics required to complete these. Only then should organizations start to determine what technology choices best facilitate delivering the data and analytics for associated functional roles. Adoption of a data-centric approach to address critical business objectives in such a focused manner provides clear articulation of the attributed value to associated BI investments and serves as the basis for developing deliberate education and training of targeted functional roles and personnel.

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