The Importance of Data Quality10 September 2011
With the exponential increase in the collection, manipulation and analysis of data it is imperative to maintain data quality but also go beyond data quality and support the needs of a growing business. There are often vast challenges with harmonising, correcting and consolidating the enormous amounts of data generated by most businesses.
The trend internationally is for companies to combine areas such as business intelligence, data quality, master data, content management, data governance, and data security under an all-inclusive data management strategy. These organisations have realised that sub-optimal data will lead to sub-optimal decisions. Due to increased competitiveness, rapid agility, demands for vastly improved customer experiences and rigid compliance requirements, South African companies can no longer afford to view business intelligence alone as the solution to the problem of poor data quality.
Implementing Data Quality10 September 2011
Quantivation employs a proven, phased approach to the improvement of data quality. This structured approach is cyclical in nature, which promotes continuous improvement of data quality in your organisation.
During the business analysis phase an objective assessment of current data standards is provided, while also considering business priorities. The second phase, data analysis requires that a quantifiable measure against which the improvements can be evaluated is obtained. In order to do this, a clearly defined, reliable benchmark for the organisation's current data quality levels is obtained.
The reliability of the data is improved and supplemented during the data cleansing and enrichment phase. During the final phase all implemented procedures are checked and results are measured against the established benchmark. Specialist feedback is critical to enable gains from lessons learnt during the process.