  The popularity of business intelligence (BI) systems to support business
analytics has tremendously increased in the last decade. The determination of
data items that should be stored in the BI system is vital to ensure the
success of an organisation's business analytic strategy. Expanding conventional
BI systems often leads to high costs of internally generating, cleansing and
maintaining new data items whilst the additional data storage costs are in many
cases of minor concern -- what is a conceptual difference to big data systems.
Thus, potential additional insights resulting from a new data item in the BI
system need to be balanced with the often high costs of data creation. While
the literature acknowledges this decision problem, no model-based approach to
inform this decision has hitherto been proposed. The present research describes
a prescriptive framework to prioritise data items for business analytics and
applies it to human resources. To achieve this goal, the proposed framework
captures core business activities in a comprehensive process map and assesses
their relative importance and possible data support with multi-criteria
decision analysis.

General Economics;Economics