There's no question that when you are dealing with data cleansing problems, you'll have a number of thorny issues to work through. We've put together some helpful points and tips to help you deal with them.
Here's the thing, you rely on the data your business accumulates to help you make tactical and strategic decisions. Your data hinders you from doing that job if your data is full of errors. The way your data warehouse functions and the data quality your company collects directly influences your organization's productivity, your customers' satisfaction (both internally and externally), and the perception that is your IT department's reliability.
Complete customer records are only possible when names and addresses match. The problem is that customer names and addresses are a poor data source. To rectify this issue, organizations must provide external references to validate data, correct inaccuracies, and supplement data points. The second piece of the puzzle is that matching and survivor software must match and merge customer data between the different systems.
This means that data cleansing must arrange the organization's data so that it is accessible to everyone who needs it. The warehouse should contain unified data, not scattered pieces. The data warehouse should contain a well-documented system so that employees can easily retrieve data. Leading data cleansing tools can extract data from a variety of sources, cleanse the data, transform it and download it in a database. Data cleansing also improves the data's quality by detecting and removing inaccurate, incomplete, incorrectly formatted, corrupt and duplicate data.
The amount of data our devices accumulate will grow as our beloved "smart" devices become capable of communicating information back to us and become ubiquitous in the business world. In these instances, humans comparing data to perform cleansing is not possible. Big Data requires cleansing to maintain its effectiveness. It requires computer analysis of unstructured or semi-structured and voluminous data so complex and varied. Data cleansing software is the only way to delve through such large volumes of data and come up with data that computer analysis can turn into business intelligence that you can actually use.
Data cleansing is an expensive and time-consuming prospect. Once staff cleans a data set, you will want to keep it pristine. Staff should keep a log of what and when each data set went through the cleaning process. Then, when new data enters the system, staff can conduct data cleansing only on the new data, saving additional time and unnecessary expense. If a data cleansing framework is not developed in advance data cleansing becomes a repetitive process. This means that data cleansing may take the form of several steps. The first step may include finding errors in the data and removing the erroneous data points. The next step may take the form of data auditing. The next step may include integrating various pieces of cleansed data together into one system. The final system, of course, will include maintenance of the cleansed data.
Conclusion
A recent poll by 451 Research of 200 IT leaders found that most face challenges with the quality of the data that is collected. The poll indicated that data quality severly affects their organizations' revenue and costs. As a result, the majority of them are also looking for new data quality management tools. As a result, these IT organizations are now making data quality a priority because they want to advance projects that have the potential to finally turn all the data they collect into a strategic business intelligence asset.
Using industry leading data cleansing tools, 5x Technology can extract data from multiple sources, cleanse and transform the data and then download it to any ODBC or JDBC database. Without the highest levels of data quality, line executives cannot rely on the data your business generates for tactical and strategic decisions. Data cleansing directly influences an organization's productivity, your internal and external customer satisfaction, and the perception of reliability of the IT department.
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