Of course, data management is important to ensure the quality and integrity of the database that resides in a CRM system. A task-based activity, it includes regularly updating contacts, eliminating duplicates, periodically deleting or appropriately archiving old data, reviewing data change management (DCM) rules, and so on. This constitutes good housekeeping, often on a daily weekly monthly and annual basis.
However, this data comes to life to deliver value when a ‘strategy’ is applied to its ‘management’ and when this approach is aligned with business goals of the firm. For instance, if the business goal is to, say, enter a new region, then the data management attention needs to be on databases pertaining to referral networks in the respective region, existing clients with offices in that geography, and prospects in that area. Similarly, if key account management is a strategic focus for the business, then priority needs to be given to data linked to those specific firms And their key decision makers. Or if tapping into the firm’s alumni community is a firm objective, then emphasis needs to be laid on building and maintaining that list for campaigns and communications.
Here are some tips to get started on such an approach:
- Review data – Holistically review all the data in your CRM system with the goal of ascertaining what data is processed, which records are up to date and incomplete, which records are important and tie into the business, marketing and business development goals and campaigns, which contacts are best archived, and so on. This will help you focus on and prioritise your data management efforts.
- De-duplicate records – Closely review and monitor the strategic data sets. Interrogation of records, especially duplicates will likely throw light on where the duplicate records are being generated – i.e. is it another system (e.g. a practice management solution) that is generating the duplicates or is it an individual; why the data entry guidelines aren’t being followed and so on. With this information, you will be able to take remedial measures to prevent such records.
- Review DCM rules – Some data is more important than others. Review your data change management (DCM) rules, adjusting the policies for the various data sets. For example if we are focusing on key clients in the strategy then we will have higher DCM rules for Key clients, so we really need to check un classified data in the same way?
- Strategise data management – Create a strategy that links directly to the goals of the business. How can Data Minder help here?. Focus on at risk and stale contact but review in order of priority, key clients firms perhaps this also enables you to focus on less data with the highest impact.
- De-risk relationships – Interrogate the prioritised data sets to identify risks. For instance, if for a key client organisation, your firm only has a strong relationship with one decision maker, that business could potentially be a high risk. Similarly, from a succession planning standpoint, if a review of a soon-to-be-retiring Partner’s client portfolio reveals that for certain organisations, the individual in question is the only key contact, the firm can put measures in place to de-risk the portfolio.
- Automate – This will help ensure that the business-critical data sets are always accurate and up to date. For example, InterAction IQ is a great tool for this. It automatically mines email signatures and either updates the existing contacts in the solution or creates new ones. Any other tool/example? Equally our Hybrid cloud enables you to update data from signatures from an email in your inbox. Another example is data normalisation, if you are finding yourself normalising the data for reliable search results then why do this manually normalise the data automatically based on your set rules, USA to United States of America, CFO to Chief Executive Officer., Str to Strasse and so on.
Data management strategy and tactical application of that strategy are inextricably linked. Approaching data management strategically will help ensure the required levels of resource allocation and data management prioritisation and greatly facilitate data driven decision making. Most importantly, the availability of this kind of superior data will greatly abet user adoption of the CRM system as the information will be trustworthy and genuinely valuable to users, especially the fee earners who are under pressure to meet their new business and organic growth targets.
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