Blog

Data cleansing is the process of detecting, correcting, or removing corrupted or inaccurate records from record sets, tables, databases, in order to identify incomplete, incorrect, inaccurate, irrelevant data parts.

Research conducted by the Harvard Business Review found that the cost of inaccurate data is $3.1 trillion. About 80% of Forbes data scientists working time is spent collecting, cleaning and preparing data. And only 20% of their time is spent directly on data analysis. Companies generate massive amounts of data every day. And this in turn adds to the cost of bad data.

Not all companies use data warehouses and master data management systems. This approach eliminates the ability to ensure data accuracy. This increases the risk of making the wrong decision based on incorrect data. However, more and more owners understand the value of quality data and the high cost of correcting errors with it. So, this increases their interest in implementing solutions for continuous data cleansing.

Quite often, data scientists and analysts have tight deadlines for completing their tasks. Time chasing keeps them from focusing on data quality. The entry of low-quality data into the system has a strong impact on all operations (for example, market research and its opportunities, analytics, planning and forecasting, efficiency calculation, customer support, etc.). Also, poor quality data can cause the system to overflow. This, in turn, will entail the inability to search for the necessary information in the database, assess the market, demand and other important operations.

A common cause of underachieving sales and revenue targets is the use of incorrect and outdated data. Data is a key component of successful work of analysts and business in general. Redundant tasks and manual data validation are time consuming and reduce productivity.

Customers are the main source of any business. The only thing that every client wants is to receive a product or service that fully meets their needs and expectations. Business analytics is aimed at processing customer data and identifying their needs, behavior analysis, etc. to increase customers’ loyalty. However, it is very difficult to achieve this by using false customer information. As a result, this leads to the opposite effect – a decrease in customer loyalty and satisfaction.

To prevent this situation, it is rational to implement one of data cleaning solutions. This is a necessary step for running a successful business activity given the generation of huge data amounts.

So, 3 components of successful work with data:

• high quality data
• effective integration
• continuous data cleaning