Large corporations depend on accurate data to employ efficiency in every department from customer services and logistics to payroll and accounting. Health and safety, recruitment, building maintenance and IT services all require efficient data management and regular data cleansing is a core ingredient in maintaining efficient and clear systems in every department. Regular data cleansing will enable a large corporation to run more cost efficiently and close black holes where costs seem to spiral.
Data entry error and inaccurate data
The very first point of capture is where most data errors take place. A simple input error such as one character of a postcode being input incorrectly can render the rest of the data in the file useless. Yet this is the area where the greatest inaccuracies take place- the original point of capture. Human error makes this unpreventable and the only way to overcome this is to ensure that data is regularly updated. When error is discovered it should be corrected immediately.
The use of address data
Large corporations have huge databases that involve address management, covering areas such as payroll, logistics and store or office location data. Data covering customer and delivery information is also stored and this is where even one simple error can spell disaster for a customer or corporation alike. Data should be matched regularly across departments that hold information about the same client or supplier. When an anomaly is visible, comprehensive data matching should then make it possible to establish where the inaccuracies lie and automatically correct them.
Data sharing
When changes take place, the same information should automatically update across all databases. A typical scenario where this doesn’t happen is where a client changes their details with the sales department but the logistics department still holds old information. The sales department will issue an invoice to the correct address, but the delivery note will still go to the old address. The consequence of this will result in unnecessary additional logistics costs to the company and even possibly costs in customer relationship management. Until that data is corrected, it will continue to incur the company costs. Regular data cleansing that uses data matching methods helps to overcome this problem.
Decision making
Other issues that arise from inaccurate data error and consequential costs is duplication of data. Inaccurate date and partial data also cause problems especially when it comes to decision making. It is possible that there are multiple records relating to the same source which when combined can create a single accurate data record as opposed to multiple partial records. Having single, accurate records also saves time when accessing records because all the information is available at once.
Good quality reliable data plays a key role in the decision making processes within a company. Good data means good decisions. Bad data means bad decisions will be made. The relationship between healthy data management and long term state of wellbeing of the company is evident. Many critical decisions are based on data analysis and where the data is poor, good analysis and decisions cannot be made.
Financial repercussions
When a corporation has a well maintained data infrastructure with regular cleansing to ensure maintenance is managed well, the financial knock is considerable. With something as seemingly simple as address management, logistics will experience fewer returns or outright loss, due to bad delivery. Even mailing campaigns become more effective because with a clean database the corporation is reaching confirmed and qualified customers rather than reaching out to where the customers used to be.
Data cleansing Services
Most large companies already use data cleansing services that will ensure that their data is accurate and kept up to date. Cleansing services will invariably use multiple sources of information to verify the data held by the company and to recover or replace bad data. This is usually achieved by matching information during the verification process and the data that matches best is then updated. This makes it possible to keep track of debtors and creditors, even when the client has not always the corporation of their changes in address.
Data cleansing also appends new data to the records that will update them and increase the value of the data held on that particular record. Often the Royal Mail postal address files are used to update address databases making this kind of cleansing much easier. The more up to date the record is, the more valuable it is, in terms of both increasing revenue and loss prevention.
