Maintaining the accuracy and quality of data is a common problem faced by many businesses. The poor data quality is a risk to the revenue of the organizations. Maintaining the quality of the data is not a onetime exercise. It is an on-going process, which requires continuous efforts. High quality data is crucial for making reliable business decisions. There are a number of reasons why the quality of the data is deteriorated. In order to ensure that the data is effective to serve its purpose, it is important to periodically review it. Here are some of the technical hitches you face to maintain data accuracy and quality.
Earlier the organizations collected, organized and managed the data that was created within their own systems, for instance, sales data. Today, the data is collected from diverse sources. The sources of Big Data are vast and the volume of data is huge. Consolidating this data is a daunting task for most organizations. Not only the data is coming from diverse sources, they are also inconsistent.
Usually, the data is converted from some previously existing data base. During the conversion process many data never finds a place in the new data source and some of them are altered so much that they are not able to serve any practical purpose. The problem magnifies when the data in the older data source is already incorrect.
Manual Data Entry
Most of the data in the organizations are manually entered in the database. As such, there are huge chances of human errors. There can be errors in typing or simply putting wrong value in the data box. Although many organizations have automated systems for data entry, most of the data is typed by human.
With the growing technology, more and more organizations are focusing on automating the data management tasks. While this reduces the chances of human error, the risk of deterioration of data increases due to the lack of judgment capability of a computer. Also, computer programs are prone to bugs which can negatively impact huge data.
Removal of Old Data
While it is important to remove the old data to make space for newer data, there is an inherent risk in the process. There is a possibility that the data which was deemed unnecessary and was removed from the system is actually relevant. Also, the data purging may also remove the incorrect data. For instance if the purging criteria is set to remove all records for the year 2010, any data where the year is erroneously entered as 2010 will also be removed.