Data Dumpster

Has your CRM system turned into a data dumpster?

With the rampant proliferation of marketing and sales technologies in the last five years, many companies’ CRM systems (this applies equally to marketing automation platforms, but we’ll say CRM for short) have turned into “data dumpsters”. Here is a quick self-assessment to see if you already have a data mess/dumpster on your hands:

  • There are > 500 data fields in your Lead, Contact, or Account data object
  • There are > 10 sets of data fields that start with a vendor’s name
  • There are > 100 data fields you have no idea what they are, and you’re the CRM administrator
  • There are > 20 data fields with almost identical names (e.g., “Products” and “Product”)
  • There are more than 5 fields each for phone number, email, job title, and address
  • There is ongoing concern about running over your CRM’s API quota

If your assessment shows you already have a data dumpster on your hands, or are well on your way to having data dumpster, you probably already know you’re having trouble with segmentation, attribution, lead routing, and lead/account scoring. This blog post will give you practical tips on how to clean up your data mess, or better yet, how to not have a data dumpster in the first place.

How Data Dumpsters Come About

CRM systems don’t turn into a data dumpsters overnight. These are the typical contributing factors:

  • Many applications that directly integrate into the CRM and inject data into the CRM.
  • Several data enrichment services either via direct integration or manual injection.
  • Many changes in management and operating procedures.
  • Lots of changes in the operating team who constantly say, “I don’t know what that field is, so let’s just create another one to be safe.”
  • Lack of data governance policy and procedure.
  • Lack of ownership, a “data steward.”
  • Lack of a single data management platform.
  • “Data hoarder” mentality, usually the result of a highly political company culture.

How to Clean Up Your Data Dumpster

If you already have a data mess/dumpster on your hands, we feel your pain. Here are some tips on how to clean up the mess. This is not a comprehensive list of guidance to get your CRM data into the best possible quality. These are the top three “urgent care” tips to get your data mess under control and have a clean CRM.

1. Unify Duplicate Data Fields

If you have more than one set of contact information (this does not include data for different purposes such as bill-to and ship-to)  such as phone, email, or address, you need to unify them. Anything more than one set creates confusion and paralysis not only for your users but for all the applications that rely on this data for workflow and analysis. All the valuable data you store in the custom fields are useless unless they are unified into the primary fields, so reconcile all the data you have into the CRM’s primary fields. Use these criteria to develop a consistent unification logic:

  • The age of the data set. Fresh data is usually better data.
  • Evidence of engagement using that set of contact data.
  • The source of the data and the trustworthiness of the source.
  • If the source is a third-party, the reputation and first-hand experience of the data quality.
  • Can the data be validated internally or using a service?

Once the logic is defined, here are some execution tips:

  • Leverage automation technology to unify the data in a timely manner.
  • Remove all the data and custom fields after the data has been reconciled into the primary fields.
  • Put in automation to reconcile all new data immediately while the data is fresh.

2. Remove Duplicate Records

Duplicate records not only create problems with business processes but also screw up metrics and analytics. If you have a significant number of duplicate records, you are likely overpaying your marketing automation vendors in license fees. Here are some practical tips on how to remove duplicates from your data dumpster efficiently:

  • Identifying duplicate records is simple, but if your data is dirty, some quick and basic cleansing can help surface duplicate records that would otherwise appear distinct. For example:
    • “” and “joe@acme.comcom”
    • “Acme Corp.” and “Acme Corporation”
    • “Acme Corp.” + “United States” and “Acme Corp.” + “USA”
  • While identification is easy, picking the surviving record, merging duplicate records, and executing the merge in your CRM are the tricky parts of the process. Focus your time and energy there.
  • Put in processes and technology to either prevent duplicates from being added to your CRM in the future or remove them as soon as they’re identified.

3. Simplify Your Technology Stack

The tremendous growth of sales and marketing technologies in the last five years has also produced the “shiny object” syndrome. Teams have purchased many technologies in an attempt to turbo-charge their demand generation and sales efforts. An over-complicated technology stack not only contributes to polluting your CRM data dumpster, but creates process coordination, API quota, and management challenges. Here are some tips on how to assess and simplify your technology stack for a clean CRM:

  • Many of these technologies likely have not panned out due to vendors’ over-promising, bad data quality, lack of management support, or simply just a bad fit for your organization. If you can’t quantify a solid ROI over the trailing 12 months, then you likely won’t miss the technology if it’s removed.
  • If your core sales and marketing processes don’t stop working when a piece of technology is removed, then maybe it’s a “nice-to-have” and not a “must-have”.
  • Technologies have overlapping features. Use your “must-have” technologies to the fullest, and then other technologies will become redundant and easy to remove from your technology stack.
  • Can you replace many point-solutions with a single platform technology?

Taking these three steps will have you well on your way to cleaning up your data dumpster.  Stay tuned for the next blog post were we cover steps to prevent your CRM system from becoming a data dumpster in the first place.


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