I'm pleased to welcome a new blogger to the Journal today - Paul Hansford with Simplesoft Solutions. Paul is here to share some tips and strategies for data management in 2010. Paul, welcome and take it away!
Dirty Data Cleaned Dirt Cheap Nine Practical Strategies for Data Management in 2010
Data quality is one of the core pillars for Customer Relationship Management (CRM) success. It does not take long to realize when using a CRM tool, like Sage SalesLogix, that the need for data quality management is paramount. The grueling pains of poor data management can range from the costs of inaccurate printing and returned mailings to the subtle troubles of users not trusting the data like they should. The effectiveness of customer facing operations (sales, marketing, and support) depends on having clean data. Think about what would happen if you marketed to customers using inaccurate data and sent them a prospecting letter (maybe you have lived it.) The costly mistakes in credibility speak for themselves.
Research studies from the Data Warehousing Institute suggest it only takes about four and a half years for a database to become ninety-eight percent bad if data is not managed properly. T.L.C. will keep the data constant, but changes that occur with records when customers move, marry, divorce, pass away, go out of business, etc., render them obsolete quickly. Users can become skeptical of the data and resort to deploying their own data repositories. Of course, no one plans to use a tool where the system data is outdated and not trusted. So, what can a Sage SalesLogix Administrator do?
Strategies for Data Success
1. First, establish the processes and tools to prevent inaccuracies.
Tip: We have found it helpful to develop a document to build user policies and procedures to establish data standards. One place to start is with USPS Publication 28. Define the policies and procedures for managing change early before the system is deployed and communicate those policies to the end users of the system. Record the standards via common usage guidelines and train users on the policies accordingly.
2. Clean up existing data inaccuracies (if they exist). Our customers find it helpful to use a data survey checklist that helps you think about areas of review.
3. Review critical data suppliers and entry points. Insist they provide accurate and current data. We have a nifty document for establishing a data clean up plan. Especially important before importing data with G.I.G.O. (Garbage In, Garbage Out) in mind.
4. Build in data accuracy by enforcing required fields and pick lists. Also, use scripting to enforce business rules whenever possible.
Tip: Focus on the most important data especially for reporting. I am sure you have observed a state field with OH, Oh., OH., Ohio, OH-IO and other variations. This can be easily prevented.
5. Don’t be seduced by the promise of CRM data cleanup tools without a plan for prevention, too. There is no substitute for preventing errors at the source.
6. Select specific software tools to solve specific problems rather than general tools for general problems. An easy-to-use built-in tool is Sage SalesLogix Groups.
Tip: There are many built in tools and methods to manage data.
7. Assign a data steward (or trustee) to be responsible for each data element (contacts, accounts, pricing information, etc.). Typically this role is the business process administrator, whereas the power user role manages the data and both should have strong executive support.
8. Establish a process through which the accuracy and quality of data in all systems can be reviewed and assured on a reoccuring basis. Schedule regular tasks to review and cleanup data, such as weekly or monthly. The data steward/administrator should be held accountable within reason.
9. In the case of multiple systems using the same data, identify a system of "official record". That is, a system in which data will originate and "feed" other systems that require the data (accounting and sales integration come to mind).
Identify Data Quality Issues in Sage SalesLogix Sage SalesLogix Administrative users can create administrative A/C/O groups for various data segmentation such as missing required fields, duplication of data, and data that needs to be reviewed because the records have not been updated or reviewed, within a specified period of time, such as a six months or a year.
Sage SalesLogix includes tools to mass 'swap' the content of fields, change values, and merge records. If the outcome of the steps are questionable, then first complete the actions in a test environment to verify the steps. After verifying the data in a test environment, then perform the steps in your production data system. (Tip: Always backup the database before making mass changes. You also may want to use the built in “user fields” to mark which records are changing, this will simplify an otherwise unreversable change and provide a temporary audit.)
Cleaning Data Using Sage SalesLogix
There are also some basic built-in tools for managing and de-duplicating data when necessary, which can be supplemented with and enhanced by third-party tools available such as QGate’s Paribus and several other data management solutions.
Nevertheless, a data management strategy, as outlined above, is required. Some of the data de-duplication tools available in Sage SalesLogix include:
Administrative Groups – predefined Groups to identify data issues
Sage SalesLogix Check for Duplicates Wizard – an automated method of finding and eliminating data duplicates
Merging Records Manually – a simple method of merging two or more records from the Group view
You can find out more about the built in tools by using the help features of SalesLogix.
Increasing the reliability of CRM data within a sales organization is priceless. Avoiding costly mistakes such as targeting a current customer as a new prospect is critical. As we are at the turn of the new decade, 2010 is a great time to review and clean your Sage SalesLogix data. Remember to clean the data you have, clean the data coming in, and keep the current data from becoming “stale”. Managing data quality is an ever moving target and must be a part of your overall system management strategy.