How many discussions about data did you have in 2013? I suspect it was more than a handful. I think it's pretty safe to say that no matter what role you play in your organization, data is having an effect on you. No doubt it is an important, albeit painful, topic for discussion - but it goes further beyond just that. When it comes to your bottom line, data can be a "make or break" for your organization, which is why database cleanliness should be at the top of the list of things to do in 2014.
Let's face it, no matter how big your database is, there are going to be mistakes. Duplicates, non-standardized values and inconsistent formatting will not only impact your ability to use your data, but also diminish its value. Cleaning up your data will help streamline tasks, create more efficiency and absolutely result in more revenue. However, in order to achieve maximum efficiency in your databases, you need a plan. Here are some simple guidelines that can give your organization the greatest chance at maximizing the benefit from the data it owns.
Start any data cleanliness project by determining the amount of work you will need to do in order to make sure your data is accurate, normalized, well formatted and complete. If you are merely updating a few dozen lead sources or country values, it might be something account managers can update during their normal course of business. If the project will involve a complete reformatting and/or overhaul of an old database before migrating to a new CRM or marketing automation tool, your company might consider additional resources.
Most databases have a wide variety of entry points. Manual entry by sales associates, imports from tradeshows, webinars or list providers, and data from integrated systems are just a few. But most common today are those leads entering via Web form; where prospects provide their information in exchange for content assets or access to other information they seek. One of the most important, and often overlooked aspects of database cleanliness is identifying all data entry points and making sure the data coming from them is clean. Without this step, the work you do on cleaning up your database can quickly become undone.
Each entry point is going to present a potential source of bad data. For Web forms, the use of drop-down menus on fields that contain a finite set of values is highly recommended. For example, by using a drop-down field versus a text field for Country, you're already taking the right step to normalizing the data. Values like "USA," "U.S.A.," "US," or "United States of America," all become "United States," and your ability to filter lists based on that single known value is both streamlined and dramatically more accurate.
Another potential entry point for dirty data is third-party lists. Combat this by first identifying which data sources should be considered trusted versus untrusted; then develop an import template for both. For untrusted sourced data, you might choose not to overwrite existing data or you might choose to skip importing certain fields altogether. Most importantly, before automatically importing any field from an acquired list, make sure the field values match the normalized values in your database.
Utilize the tools that are available in the marketplace to clean up and maintain your data. Some are free; some require a budget to purchase services or technology. Start planning that budget right now - I promise you, it will be one of the best investments you can make. Here are a couple of questions you can ask yourself to determine the tools you may need:
How many duplicates do I have? If it's over 5000, you might consider an automated solution - a competent intern might also be an option.
Are their duplicates across different objects? Do you have duplicate leads and contacts, or just issues with duplicate leads? If the duplication is cross-object, automation tools will save you time and improve accuracy.
Try to figure out what is causing the duplicate entries. A de-dupe process that focuses on only a single data point is not likely to get the job done. For example, a simple de-dupe on email address alone isn't going to catch multiple entries entry for the same person that uses a company email, Gmail, Yahoo!, and several other alternate addresses.
A plan for clean data is only as good your ongoing plan to enforce it. Show your sales team the impact bad data can have on their paychecks (e.g. bad or incomplete data can lead to missed opportunities). Then use that exercise as the catalyst for training, where you show them simple steps to accurate data entry. Trust me, any sales rep that fears losing out on a bigger paycheck is going to listen to what you have to say.
Demonstrate to your marketing team how a poorly sourced list and careless uploads into your database can overwrite good information and cause important data to be lost. If you are running processes off data, it can also cause all kinds of issues with lead routing, false messaging going out to sales reps, or worse yet, faulty information being sent to your prospects and customers.
Once you have everything cleaned up, practice continual monitoring. Set aside resources to review your duplicates and evaluate your overall data cleanliness on a monthly basis.
Good data housekeeping is not a hard task. It does, however, take discipline. Follow the steps I've outlined above and you will be off to a good start. And remember, clean data isn't just a matter of a nice-looking lists of names, emails and other data, it could be the difference between a qualified lead turning into a sale versus a qualified lead getting lost in a sea of bad data.
John Muehling is a marketing services manager for LeadMD. An accomplished marketing executive with over 15 years of experience, John leverages his operational experience with SalesForce.com, expertise as a Certified Marketo trainer and SCRUM Master techniques to deliver marketing automation solutions that help companies propel their organizations to stronger revenues and higher profitability.