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Why You Don't Need To Dice With Dirty Data Or Dodgy Databases
Wed 24th August 2011
Even after cleansing, a data set might still have inconsistencies caused when generating from different sources. This is the point when the process of data cleansing involves removing typographical errors or validating and correcting values against a known list of entities. The validation could be to reject any address that does not have a valid post code, for example - which is referred to as strict cleaning; or it might be fuzzy cleaning - such as correcting records that partially match existing records. When you have gone to all this trouble sourcing and cleansing your data, you need to be sure that your databases will be secure.
When we hear about dirty data we often think about data quality; that is how trustworthy data is, including whether the data is valid, consistent, accurate and relevant. Usually, when data is described as dirty, the information is inaccurate, incomplete or duplicated. However, a startling three-quarters of large enterprises will make hardly any improvements to clean up their data. To gain the competitive advantage from this type of information, it's crucial that organisations begin to look at their data as a valuable corporate asset.
Poor quality or dirty customer data can lead to huge, unnecessary business costs when mail-outs are misdirected and emails sent to out-of-date contacts, and it's common for more than 10% of the marketing budget to be wasted on this kind of mis-targetting. Today, businesses from government, banking and finance, telecommunications, utilities to charity organisations recognise that data quality has a huge impact on most of their marketing and strategic business plans as they address the need into trying to clean up their act.
Data doyens trust Microsoft SQL Server with their data. Microsoft SQL is an application used to create computer databases for the Microsoft Windows family of server operating systems. Microsoft SQL Server provides an environment used to generate databases that can be accessed from workstations, the Internet or other media such as a personal digital assistant (PDA).
SQL Server offers an auditing tool that involves tracking and logging events that occur on the system. You can use several methods of auditing for SQL Server and with SQL Server 2008 Enterprise, you can also set up automatic auditing by using SQL Server Audit.
There are several levels of auditing for SQL Server, depending on government or standards requirements for your installation. SQL Server Audit provides the tools and processes you must have to enable, store, and view audits on various server and database objects.
You can record server audit action groups per-instance, and either database audit action groups or database audit actions per database. The audit event will occur every time that the auditable action is encountered. The components of SQL Server Audit combine to produce an output that is called an audit, just as a report definition combined with graphics and data elements produces a report.
When you define an audit, you specify the location for the output of the results. This is the audit destination. The audit is created in a disabled state, and does not automatically audit any actions. After the audit is enabled, the audit destination receives data from the audit.
The results of your audit can be sent to a target, which can be a file, the Windows Security event log, or the Windows Application event log, although writing to the Security log is not available on Windows XP. Logs must be reviewed and archived periodically to make sure that the target has sufficient space to write additional records.
If you're worried about your data, don't gamble with dodgy databases or dirty data - instead put your trust in Microsoft and check out the SQL Server capabilities.
Author is a freelance copywriter. For more information on london/microsoft/training, please visit https://www.stl-training.co.uk
Original article appears here:
https://www.stl-training.co.uk/article-1930-why-you-dont-need-dice-with-dirty-data-or-dodgy-databases.html
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