Data is now a critical asset for many organizations today, and its precision and accuracy has vastly improved due to the use of deep intelligence technologies.
Although there is a big Achilles heel: dirty data. For organizations to maintain efficiency and accuracy of the data they use, organizations are required to adopt a best practice strategy to cleanse data that supports high levels of data integrity and data quality.
Organizations experience many flaws with data; duplication, inaccurate, inconsistencies, and incomplete data sets. This results in lost productivity, revenue, operational efficiency, and, importantly, low data quality. The decisions that organizations make based on dirty data will eventually become ineffective and harmful.
V3B reports that Experian estimates organizations lose up to 12% of revenue due to dirty data. Data needs to remain accurate, up to date, and consistent, and all that is required is a data cleansing ( or data hygiene) strategy.
What Is Data Cleansing?
In simple terms, data cleansing (or data hygiene) is the process of identifying any issues, errors, or inaccuracies with a data set and making the required corrections to it for a single line of accurate data. Performing this task boosts the consistency, reliability, and value of company data while meeting basic data quality standards. Data is a critical driver of how companies operate and the decisions they make. With vast amounts of data streaming in from various sources, a data cleansing strategy is now more important than ever to ensure that outdated, inaccurate data sets are cleaned appropriately to aid better quality decisions.
A data cleansing strategy upholds the following data fundamentals:
Alongside supporting in making high-quality decisions, data cleansing also introduces the following four benefits:
Boost In Productivity
Clean data ensures employees can make the best use of their time during working hours. Low-quality data means employees spend a large amount of time cleaning data manually. The ability to make timely decisions stems from high data quality. Therefore, a data cleansing strategy will not only boost the confidence and productivity of employees, but it guarantees high-quality decision making.
Companies are striving to build their reputation and gain customer trust. Accurate and reliable data is a great way of achieving this. This also introduces benefits to those customers who receive your data and maintains brand integrity as well as reputation.
The report from Experian indicates just how essential accurate data is to companies. Clean data introduces an upturn in results, a more significant return-on-investment (ROI), and considerably improves customer communications. If companies want to convey information directly, this can be completed quickly and conveniently with accurate data. Also, customers benefit by receiving significant value. For example, if a company sends communications relating to products and services, one email containing accurate and direct content is more likely to gain the interest of customers compared to outdated, redundant data that requires customers filtering through to find what they need. Customers are more likely to lose interest in this way.
Streamlined operations mean simplifying and eliminating unnecessary operational tasks to improve efficiency, modernize processes, and save money. A data cleansing strategy goes a long way in achieving this. Not only this, but companies will also allow for better time management catered to other essential tasks, minimize risk, and promote greater transparency.
Accurate data acts as a trigger for correct decisions, and for companies to benefit, a data cleansing strategy is now more critical than ever. Opportunities for error are continuous, but companies who implement a data cleansing strategy will reap the benefits of reliability, quality, revenue, and productivity. Not only this, but data cleansing will prove vital for companies to storm ahead of the competition and maintain a valued reputation.