Your company’s results Data is one of the company’s main assets. Therefore, data quality or data quality management is a priority , meaning the achievement and maintenance of accurate, consistent and complete data.
Optimal data is data that is functional in meeting the company’s needs at an operational and strategic level. To achieve this, the data must be reliable and timely, which means – in addition to its accuracy, consistency and completeness – being correctly interrelated and providing a unified vision.
Implementing a data quality initiative logically involves carrying out a data quality strategy that includes applying quality at different points in the process. Regardless of the services, methodologies and tools used, the ultimate goal will be none other than data quality.
Data quality: The importance of data quality
The competitive needs of organizations, in fact, clearly explain the importance of data quality. Indeed, data quality can uae whatsapp number data the success or failure of an organization.
It is an increasingly big problem, as the lack of data quality deteriorates data and leads to serious management problems and non-compliance with regulations. With data quality initiatives to correct it, in addition to solving them, we obtain a competitive advantage.
A substantial improvement in business results
Companies suffering from data quality problems do not have reliable information, with all the implications that this has at all levels. Because, as is easy to deduce, the problems do not only occur at an internal level.
The difficulties extend from within the webinword & eastern friuli trade district outwards, projecting into key aspects such as productivity , relationships with clients or suppliers , and decision- making .
The impact of poor data quality is a threat to the smooth running of a business. In addition to the consequences mentioned above, the impact on the business can be seen in increased management costs , difficulties in detecting fraudulent practices or, for example, faulty reporting and analysis.
That’s right, strategic decision-making based on data analysis is particularly affected. In fact, data quality is vital for those involved in predictive analytics, since the reliability of predictions depends greatly on data quality.
Predictive analysis tools specific to Business Intelligence your company’s results to big data. Such as data mining and statistical analysis techniques, among others. Require data quality techniques to ensure their success. If the data is not of quality. Strategic decisions will inevitably not find reliable support.
Key points to identify data quality issues
Every data quality issue can present contact lists both in identifying where. Those issues exist and in quantifying the magnitude of the problems. Quantification is important in determining where our data quality efforts should be focused in the first place. For example, a large number of missing email addresses might be alarming. But would have little impact if there is no email communication plan in place. Understanding the requirements of each business is imperative.
