The explosion of raw business data within companies of all sizes has posed a significant challenge to corporate executives who must now turn these growing data volumes into viable, intelligent information that can be used to improve the business from a variety of aspects that span production line to sales and marketing initiatives.
Business intelligence (BI) is the technology designed to extract meaningful information about a company`s performance, its products, clients, employees and competitors from previously untapped raw data resource pools.
"BI is one of the most significant by-products of the e-commerce revolution, which focused on the integration of data and its widespread availability to all parties within a supply chain," says Altus Viljoen, a business technologist at Bytes Business Solutions (a member of the Altech Group).
"Today BI technology is used to help decision-makers in business to reduce costs while making the best possible use of their resources. BI technology is used to examine business drivers from a variety of sources and predict trends based on the intelligent interpretation of the data associated with its findings," he says.
"In the process, hundreds, if not thousands, of inputs are processed from sources within and outside of the business on a continual basis."
Viljoen says today`s sophisticated BI solutions are able to find "real" business value for their users in terms of the creation of new business opportunities.
"For instance, the information gathered by medical insurance companies from medical claims has intrinsic value to other suppliers in the value chain. And details of airline passenger buying patterns and behaviour gathered by airports companies has value to airlines and other related service organisations.
"Many examples exist where information is used to great effect. But there is one condition: BI data must be obtained correctly and packaged efficiently."
Viljoen says accurate decisions have to be supported by accurate data. "Data quality is a major stumbling block in the execution of BI solutions. Data quality has to be addressed in both transactional and operational environments in relation to time lines associated with its sourcing.
"With data volumes increasing exponentially any company attempting to support BI technologies using manual code will certainly fail.
"Because the nature of business is dynamic, almost as soon as the reports based on manually gathered data are published they will be out of date.
"Effective BI solutions require code free tools to gain access to complex data and to keep pace with changing analysis specifications. Only in this way can BI add value and participate fully in the business challenge of beating off tough competitors and helping organisations to succeed in difficult markets."
Viljoen notes that organisations should measure data quality in terms of its validity, completeness, accuracy, currency and consistency and then implement a methodology, with appropriate controls, to eliminate gaps in the data flow.
"This is best achieved by standardising and aligning data across geographical and organisational boundaries," he says.
"Data quality can almost be described as the `fitness scale" of an organisation; If it is clean - and healthy - it will sustain business in a productive way for long periods," he adds.
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