Poor data quality leads to important and varied problems that impact the company on very multiple levels, from the deterioration of the corporate image or inefficient decision making to the decrease in sales, mismanagement of clients, the repetition of cost overruns or, among other common consequences, non-compliance with regulations.
If a company does not control that the quality of the information is adequate to guarantee the proper functioning of the business, one way or another will be impacted by data errors. In reality, data quality issues they carry a series of risks, and they do it differently in each organization, so when looking for solutions it is necessary to examine the real impact of this low data quality.
When determining losses, it is about identifying a concrete shape how affects that low quality of data to aspects of all kinds, as regulatory compliance, cost overruns, delays, Workload, decision efficiency, losses, Customer Support, loss of profit, information integration, calculation accuracy, privacy or, as an example, competitiveness of the company.
The causes of poor data quality
There are many causes that generate and explain poor data quality: migrations, data entry, the increase in volume and diversity of sources, process automation, loading errors in transactional systems, external data or, among others, creating new applications.
Knowing where this poor quality is generated is essential, the roots of the problem, in reality, to be able to apply the most effective solutions possible. Let's keep in mind that, although a data quality project needs follow-up to keep the results at an optimal level, the quality of quality is technical and economically unfeasible.
Target: implement a data quality project
The answer to these problems is to implement quality processes adapted to the needs of the company., based on the life cycle control data quality through structure and profile and content cleanliness. In general, the steps are divided into different actions, carried out in a logical order: to find out, analyze / establish, develop, check / analyze and monitor. Ideally, at the same time, the answer must be global, non-departmental, if not from the beginning, at least, long-term.
At the same time as implementing a data quality project, transforming losses into profits needs to have adequate human resources and state-of-the-art technologies. Once a successful application is achieved, as a reward for effort and investment, the advantages will come.
The benefits will accrue to a comprehensive improvement of the company. Productivity will be gained, competitiveness and customer loyalty, which will translate into key aspects such as customer retention, image enhancement, greater control of data quality -identification of erroneous and duplicate information-, cost savings, more agile and efficient administration, decrease in manuals. work on report preparation, data security, better decision making and data exploitation for knowledge management.
Image source: ddpavumba / FreeDigitalPhotos.net
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