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human resources and legal may need to be consulted. Marketing personnel may want to drive, or
be heavily involved in, pilot analytics of customer engagement to gauge customer response. IT
may be unable to support planned efforts due to competing priorities. Risk, security, and privacy
functions may want to review and assess the controls in the big data environment.

Without buy-in from key business stakeholders and consumers on their responsibilities in the
overall effort, data insights may go unused for long periods of time following the deployment of
the big data program. Therefore, prior to making a significant investment in big data efforts,
organizations should engage all relevant stakeholders to ensure support, determine value, inquire
about requirements, address preferences, gauge bandwidth to advance plans in spite of other
priorities, and drive action during and post implementation. A formal stakeholder analysis may be
necessary to identify all relevant stakeholders before implementing the program. Figure 4 shows
examples of key stakeholders who may help drive and support big data efforts.
Figure 4: Examples of Big Data Key Stakeholders

Project sponsor

Executive level resource who drives support and funding for the program.
Business/data owners

Data owners who support data consolidation and integration into one
solution that supports organizational goals.
Chief information officer

The resources who maintain knowledge of business needs and technology
capabilities in order to transform business requirements into big data
solutions.
Consumers (e.g., marketing)
Any function within the organization that consumes data and/or uses the
analytic results.

Chief privacy officer/chief information security
officer
Executive level resource responsible for delivering the technology solution, as
well as partnering with external vendors when big data is outsourced.
Business analysts

Executive level resources who should be consulted on controls related to the
security, protection, and use of the data an resulting analytics.
Chief data officer
Executive level resource who directs enterprise-level data governance.

Technical data analytics resources/data analysts
These resources can include database administrators, software developers,
technical tools administrators, and script writers.

Data scientist
An advanced analytics professional who understands the technology and
business processes, and can develop and support innovative analytics to drive
business value (e.g., predictive analytics).
Source: The IIA

10 — theiia.org