Information Technology Review - Auditing Information Systems: Enhancing Performance of the Enterprise - page 48

duce various performance reports. Databases can handle large amounts of business
data which enterprises produce during the course of business.
Information managers use data sitting on company silos to identify various activities,
such as sales performance, profitability, competitiveness, and even identify cases of
fraud and revenue leakages.
Data analytics is one of the major uses of data sitting on databases. IS auditors make use
of data analysis software such as ACL, IDEA, SAS, even Excel to conduct invest-
igations, clean up data, and discover data input errors. Data sitting on databases can
also be used for business intelligence such as transforming raw data into meaningful
information, which can be used for business decision-making.
There are various database systems such as Microsoft SQL 2014, IBM DB2, Oracle 12c,
and SAP HANA developed for commercial use. There are also open-source data-
bases, which are in use in many enterprises such as MySQL, Apache Derby, and
SQLite.
IS auditors have a very important role to play in ensuring that data integrity is maintained
on databases. This can be done through regular audits of database systems. IS audits
include but are not limited to testing data integrity, data security, data privacy, data-
base system controls, and data recovery procedures.
k) Big Data
Big data is a new and growing phenomenon with a great impact on the way we do busi-
ness and on the type of information which is made available for decision-making by
business executives. Big data involves very large volumes of data which are gen-
erated by enterprises as single, vertically or horizontally integrated enterprises. The
size of data has become so large that our current database systems and applications
have challenges in processing the data. Using big data, business managers are able to
investigate data relationships, make comparisons, and identify correlations. Big data
holds a lot of secrets for the enterprise which can be discovered through data analyt-
ics. Good examples include investigating increasing operational costs, market trends,
performance levels, and revenue leakages.
Enterprises generate millions of megabytes of data per day, which are stored on enter-
prise data silos. The challenge is that this data is growing every day, and storing
it has become a big issue. The bigger challenge is how to process this data as it
requires superfast computers and large storage. Currently, in order to process such