Risks of Poor Data Quality and Ways to Improve

Due to rapid development in information technology during the past few years organizations have large amounts of data collected and stored. Since data is larger in volume, it is difficult to manage and process it. With increasing data complexity, it is important to main data accuracy otherwise it leads to poor data quality. The business costs can increase due to error while compiling and managing data, which can lead to long term issues in the future as companies depend on such historical data for their sales, marketing, and services. Time as they say is money, so if data that companies have is redundant it will waste time and resources on poor quality data. It will lead to making poor decisions for the companies, which can lead to a loss in sales or client business deals/opportunities, also flawed planning, inventory, and orders. Additional costs will be allocated to resolve this issue to provide correct data.

Ways to Improve Data Quality

What is data mining?

Data mining is the process where large data sets are examined to generate new information, where raw data is turned into useful information. It helps to create good quality data and reduce data redundancy, as well as strategies for marketing and sales to help businesses reduce costs. It helps to predict outcomes when considering large datasets and this information can be used at various levels in business. It can practically be beneficial to any organization, retail companies, financial institutions, pharmaceutical companies, security firms, government departments, online service providers, libraries, and researchers too.

What is text mining?

It is the process of analyzing unstructured text data available in huge amounts using software that helps to identify patterns, relations, and attributes in the data and is also called text analytics. It helps to sort data and reduce the quantity of available unstructured text to structured and useful data. This is used in the following areas –

1. Resume selection based on words used in the resume.

2. Sentiment analysis widely used in social media to identify threats that are positive and negative based on user feedback, tweets, comments, etc.

Text mining has been widely used to improve customer user experience, identifying issues that customers come across with the products, fraud detection, advertising, etc. which in turn has benefits businesses in increasing profits and revenues.

Poor Data quality poses a great risk for an organization in terms of analysis capability of the data, increases costs, and resources, also it reduces business growth. The role of data in an organization is important as it helps in enabling futuristic ideas and technologies, but we are risking this aspect if we don’t work on making the quality of the data a priority. There are various challenges in improving data quality and it can be difficult, but we can’t ignore the benefits of good quality data and the costs related to it is nothing compared to the losses and risks incurred due to poor data. The loss is not only financial but other also such as organization losing its reputation, missing business opportunities from clients, and high risk in decision-making due to lack of analysis based on poor quality data. The focus should be on improving the quality as a lot is on stake here, there are various benefits of it besides financial and revenue which needs to be evaluated for understanding. The quality of data enables better analysis, decision-making, and increased productivity. We shouldn’t also ignore that this data helps to improve IT in the future and improve customer interface and sales/marketing strategies.

Conclusion

We shouldn’t ignore the poor decisions based on information processed from the data at hand as it’s not only costly, but it slows down employees and their performance. The reason data quality is critical because information extracted from this is used by leaders, administrators, learning specialists, information researchers, and others in their day to day work which can result in expensive losses. What’s more, doing work like this is both tedious and costly. The information that is required may have a lot of blunders, and notwithstanding a basic due date, numerous people essentially make remedies themselves to finish the job needing to be done. Text mining has become an integral part of information researchers and different clients because of the improvement of huge information stages and profound learning calculations that can investigate huge amounts of unstructured information. Mining and breaking down content enable associations to discover information that can be profitable for business experiences in corporate reports, client messages, call focus logs, verbatim overview remarks, informal community posts, restorative records, and different wellsprings of content-based information.