Adoption of predictive and prescriptive analytics: Usage within PM-Dashboards
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Abstract
Purpose This research describes how predictive and prescriptive analytics can be adopted by companies
within their Performance Management Dashboard (PM-Dashboard) environments.
Methodology The Design Science methodology was used to build a PM-Dashboard adoption framework.
First, a literature review has been written which provided the basis for the framework development. Hereafter,
two rounds of interviews have been conducted with Deloitte consultants and clients to build, fill, and
validate the framework.
Findings During the development of the framework it became clear that defining leading and lagging indicators,
describing an extensive business case, having the right leadership, implementing a change journey
& culture, building strong governance, and ensuring high data quality & management are the most important
capabilities to develop when a PM-Dashboard environment needs to be built. The literature review and the
interviews conducted during this research provided an extensive list of inhibitors and enablers which can be
used to build these capabilities.
Next to the most important capability areas, this research showed that it is not necessary to have a base
level in data quality before an effective PM-Dashboard can be build but that a PM-Dashboard implementation
can even help in making bad data quality more visible in the organization. Furthermore, it became clear that
Performance Management practices are quite similar across industries since organization often pursue similar
goals, this has a positive effect on the usability of this research since the framework build can be generically
used in a wide range of organizations. Lastly, this research showed that real-time refresh of data in
PM-Dashboards is not always beneficial for the desired outcome since faster refresh rates could lead to
micromanagement from the executives using PM-Dashboards.
Value This research adds insight to the scientific world on what capabilities are the most important for
organizations in order for them to optimally use predictive and prescriptive analytics within their PM-Dashboard
environment. This is done by providing a framework consisting of a process, a Capability Maturity
Model (CMM), and an adoption questionnaire linked with each other via an extensive list of inhibitors and
enablers for adoption of predictive and prescriptive analytics within PM-Dashboards which can be used to
further develop and mature PM-Dashboard environments within organizations. The framework will help in
identifying on which areas the adoption of a PM-Dashboard is lacking which helps in better focus improvement
efforts.