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A pet peeve for anyone in marketing or sales who has to work with CRM data while setting up their direct mail or other outbound campaigns is bad data. While there are great features when it comes to hosted CRMs like Salesforce.com, Sugar CRM, you will only realize the full potential of these CRM if you maintain clean data. A poorly maintained database results in lots & lots of inefficiency whether you need to run reports for analytics or even a simple task like printing out addresses for a direct mail campaign. With multiple access and data pouring in from several sources, CRM database maintenance is not as simple as it may seem. It needs a disciplined ‘every day’ process and consistent effort from all of those who use it. Here are a few simple Do’s to maintain a clean database:

• Do make sure you enter only the data you need into your CRM and filter out what looks like junk right at the start. Many have a habit of dumping entire excel or csv sheets of leads, accounts and contact data which hasn’t been screened before upload for junk data which may be completely unnecessary
• Do run your data through a normalization process and format all the data well in the csv file or sheet before you upload it to your CRM. It’s a lot quicker and it can save you from having to access several accounts and check the data online once it has already been assigned to various user accounts and campaigns
• Do label all campaigns and sets of uploaded data items with a standard company wide naming convention which is clearly understood by users and ensures that every record is assigned correctly. As simple as it may seem, a lot of accounts have data that is simply uploaded and in time nobody knows to which source it came from, which campaign it belongs to or whose lead it is
• Do follow a standard convention across all users for entering data such as account names, postal addresses, job titles etc. It standardizes formats and makes things easier while printing reports or direct mail campaigns
• Do look for missing data elements like a missing phone number or missing zip code in the address preferably before uploading the data or at least while entering an individual record. Appending missing data at an earlier stage when there are fewer records to handle is a lot easier than having to go through the entire database and then look for whats missing
• Do ensure that users update the status of a company, lead or opportunity every time some activity is performed. If these are not updated the reports run on this data will yield inaccurate results
• Do run regular audits for data quality and carry out a data cleansing effort periodically (at least once every quarter). If you can do it more frequently, there will be less data which needs to be updated or cleansed and the data will stay in better shape

Good data in. Good data out. Its that simple!