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Don't Be Caught With Dirty Data!

Oct 31, 2014

We’re very excited to have a guest blog by our very own Kirbie Pillette, Implementation Consultant at Apto! Kirbie helps onboard new users and works with data on a daily basis.

You may have heard the phrase “garbage in, garbage out.”  This means that if data is invalid, the end Data-Analytics-and-Dirty-Dataresults will be invalid. The end result is what’s important to your business. It could be a list of contacts, a report or a dashboard. Nobody wants “garbage out” so we’ve compiled some examples of “dirty data” that you don’t want in your CRM system!

Data that is Incorrect or Mostly Correct

A phone number without enough digits is an obvious example of incorrect data, but, many times, data entry errors lead to information that’s “mostly” correct.  Address data that has a street, city, and state but has the wrong zip code is an example of  data that is mostly correct.  Mostly correct data can often lead to more issues than data that is blatantly incorrect, because it isn’t as obvious to catch those errors.  

Data that is Not Current

Commercial Real Estate moves at a fast pace, so data that is not current will only slow your business down.  Out-dated contact information for a client or old ownership information on a property won’t do much good when you’re trying to close deals!

Data that Does Not Adhere To or Suit Your Business Rules

A comp record where Lease Expiration Date is before Lease Start Date would violate a business rule.  Or, if sending targeted marketing emails is a part of your business, it would be counterintuitive to allow business users to enter contact records without ensuring there is always a valid email address.

Data that is Too General in Nature

A broker acknowledges that a deal was lost and lists the reason lost as simply "relationship." This is an example of data that is misleading, because it is too general to know whether this deal was lost due to a poor relationship with the client or due to the client's better relationship with the broker that eventually won the deal.    

Data that Is Duplicated

Duplicated data can be especially confusing, because, in many cases, the records may both have vital information contained in them.  There are several tools available on the Salesforce AppExchange to help you with data deduplication. DupeBlocker and Merge Any Object are a couple of our favorites!

Data Without Proper Formatting

Where possible, try to create standards around your data.  For example, you may want to enforce that system dates are entered in a similar manner.  For example, if a specific start date is necessary for record-keeping purposes or contracting needs, your system may need to enforce that users always enter the day when entering date information.  

Data that Is Inconsistent

Many times, especially in reference to company and contact names, there is a lack of consistency.  For example, a company might be entered as American Broadcasting Company, ABC, or A.B.C. depending on the user entering the information.  Failing to have consistency in your database could lead to duplication and misinterpretations.  

Data that is Incomplete

Data can be incomplete or missing for a number of reasons.  The best way to head off incomplete data, is by making certain fields required when filling out forms in your systems.  If you are looking for a good place to start, make sure that your most important reference fields like name, email address, phone numbers, and address are filled in.

It’s important to note that you should only make fields required that are necessary to your business processes. Too many required fields can act as a deterrent to your users. Finding the right balance between encouraging user adoption and getting the information your business needs is key.

Data that Does Not Use the Same Business Terms

Make sure that everyone involved with the system is using the same terminology.  If they are not, data inputted can be misinterpreted.  Ex: A prospect in some business areas might refer to a contact person, while in other business areas, the prospect refers to the deal opportunity.  Make sure your users are on the same page.

Partnering with an industry specific CRM provider can help with this as they’ll be familiar with your business terms and can help customize the application during the initial setup.

Believe it or not, the biggest hurdles in data cleanups are not doing the cleaning up, its:clean_data
  1. Figuring out who is responsible for the dirty data (identifying an owner of the data in your business processes)
  2. Putting in controls to prevent the dirty data from continuing (formatting, rules, standards, etc.)
  3. Getting users to take charge and be accountable for their dirty data.  

Only after those 3 things are in place, can you hope to have quality data.  You can continue cleaning up data periodically, but that's an ineffective use of your time and it doesn't keep your users accountable for the data they input.

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Topics: Best Practices

Kirbie Pillette

Written by Kirbie Pillette

Kirbie is an Implementation Consultant at Apto. She helps onboard new users and works with data on a daily basis. She knows all the tricks of the trade to get your org customized and ready to go; we call her "The Apto Bedazzler."

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