With data pouring in from multiple resources and users clamouring for information to make meaningful business decisions, data accuracy is crucial for business success. But achieving an integrated, holistic view of enterprise information can seem impossible.
So says Annemarie Cronje, solution architect, SAS Institute in South Africa, who points out that because data entry standards vary, information from multiple sources results in inconsistently represented data. "Company and individual names may be represented in a variety of ways. There are variations in business titles and addresses.
"Duplicate data with slight variations renders marketing campaigns costly and ineffective. Erroneous information produces inaccurate business forecasts and business decisions based on the results could be disastrous with enormous impact on the bottom line.
"In fact, poor data quality is detrimental to any organisation," says Cronje.
To overcome this problem, SAS has introduced value added data quality technologies into the data transformation process.
The SAS ETLQ Data Quality solution, for example, is a marriage between its ETL Data Warehousing Solution and desktop data cleansing tools.
Essentially the solution provides sophisticated matching and standardisation routines that enable users to analyse, cleanse and standardise data across platforms. It's not necessary to move data from its original source location in order to run the cleansing processes.
"You can analyse and parse input data and return standardised values for specific data, such as company names. You can standardise the capitalisation of data values and determine whether a particular character value represents an individual or an organisation," Cronje concludes.
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