There are many reasons to clean your data regardless of what your role is or what industry you operate in. However, if you are responsible for managing partnerships and alliances it is even more important as limited resources, time and high value of those relationships make it even more critical. Here are just some common data issues and what to do about it:

  • Duplicate Records – Partner’s information can be found in other places including marketing’s prospect list and on a customer list. A partner can also be duplicate in your own list. The solution to this problem is not necessarily to de-dupe but to at least be aware of the fact that prospect marketing materials are being sent to your partners. Of course, you should also de-dupe inside your own partner lists, as you do not want to embarrass yourself by contacting the same person twice without realizing it!
  • Out Dated Records – it is not uncommon for people to change jobs and businesses to close down. As you spend a lot of time building out your relationship it can be very heartbroken to all of the sudden receive a bounced email or a notification that the person is no longer there. The solution is simple, using tools such as LinkedIn to connect to the person in their new position. Perhaps, you can pick up the relationship where you left off. You may also want to figure out who took over the role of the person that is no longer there to continue your partnership.
  • Incomplete Records – Having records which are missing critical information makes it impossible to run any analytics, segment your list or even communicate properly. The first step to fix this is to identify fields that are critical for your business. It could be partner type, geographic location, phone number and so on. Once identified you can now figure out the records that are missing this critical information and either append it yourself or using a third-party data provider to fill it in.
  • Inconsistent Fields – an opposite problem of having incomplete data is having data that is not consistent and therefore rendered useless when doing anything on a mass scale. For example, you may wish to distinguish the different partners based on their industry. The data is available in multiple places, you may have NAICS code, SIC Code, and industry field written down by your team. In order to pull a list of all partners in a specific industry you would have to utilize and bucket all those different fields. A simpler approach is to normalize/standardize the data into a new field that will just have a pre-set industry values that you wish to utilize by bucketing all the available information.

These are just normal data problems which channel marketers deal with on a daily basis. If you do have data integrity issues as described above or have a specific data issue do not despair! A professional data cleaning company can clean your data without putting a strain on your budgets or resources.

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