The resolution of problems connected with data quality is coming to the fore of companies' interests, as the quality of data substantially influences the quality of business decisions based on data. Inefficiency caused by data resulting in erroneous decisions has the consequence that achieved results often do not reflect costly investments in corporate BI applications. In such a case, the low quality of data is a barrier to the company's development and negatively affects its competitiveness.

At the same time, low-quality data can unfavourably affect ordinary activities associated with operations, for example by causing errors in invoicing or by generally limiting the ability to comply with binding agreements and norms requiring certain statements (accounting, financial, statistical, etc.) that contain undistorted information from the company and are intended for business partners as well as for government and regulatory bodies. Failure to comply with such agreements and norms, or provision of false information, can result in sanctions.

Comprehensive approach to data quality

It is necessary to take a comprehensive approach to the quality of data in a company. The ascertained extent of impurity in customers' databases, for example, is high, often affecting over 50% of records. More important, however, is its impact. It does not make sense to correct a million minor inconsistencies in addresses if the post office delivers consignments without any problems. The discovery that ten percent of records are duplicates, where we require their individuality, can represent a bigger problem, such as in the case of perfectly formatted addresses whose original holders have relocated.

Automated cleaning is a very effective means of preventing such problems wherever we can define rules and we have information for corrections or for inserting missing data. For ongoing maintenance of quality, we need to ascertain and eliminate the causes of impurities and to set up processes that will maintain a quality database. Data quality also depends on how metadata, master data and the issue of enterprise content management are handled within the company.

How to get started with Profinit

We recommend starting with an audit of data quality in your company, on the basis of which the subsequent process will be determined and solution designed. Profinit specialises in the area of both data and information quality. We offer standard software products and custom development of solutions.