Water, water, every where,
And all the boards did shrink;
Water, water, every where,
Nor any drop to drink.

The Rime of the Ancient Mariner by S. T. Coleridge

Data, data, everywhere …

Data upkeep is quite an investment. Information and the collecting and analyzing of it is a cornerstone of successful enterprises. Sometimes it may seem that the information that gets collected can be a barrier to success just because it can become extraneous, unreliable, or contain conflicts. Data is similar to the Mariner’s water in that it’s vital in many ways, but it can be toxic and harmful in other circumstances.

In order to gain the most out of collecting information, it’s good to maintain and curate the data as best as possible and clean it as often as possible. Certain data collection points are more valuable than others, so reviewing the collection types and the data’s value is important. Knowing a window of time for an event might be more important than knowing the market segment, for example. Identifying the key data points can be crucial, and these data points will differ from organization to organization. There are a few important guidelines to keep in mind when reviewing your data.

Bad data is going to manifest one way or another. 

It can happen for various reasons, and it’s good to start with a process of properly entering information into your database. Then, the information should be regularly maintained. Check for duplication because it can cause reports to misrepresent what has been collected. Secondly, the data needs to be accurate. Evaluate processes that can put rails on the data entry process, and have reports available to check for conflicting data points.

How does your data become harmful? Here are four ways:
  1. Data entry error
    These simple errors can arise anywhere. Perhaps state abbreviations are incorrect, or typoes are entered in the system. Incorrect numerical entries can cause nightmares for accounting departments. An opportunity to win a lead might be missed because of a simple overlooked Yes/No field.

  2. Staff changes
    This industry sees a lot of position changes and people moving from one business to another. The newer staff may not have the tribal knowledge the previous team had, or at least not know the best practices and standard processes involved.

  3. Best practices slip over time
    This happens due to the turnover previously mentioned, but it can also be caused by a change of direction from the reporting authority. If a board decides to focus on other elements over time, then the data might not contain the most accurate information for the more recent demands.

  4. Importing bulk records
    Collecting data in a spreadsheet or other means and then importing it into the CRM can be a valuable time saver. However, there can be data cross-over, and information attached to one record, like a contact, can be applied to an entire account, causing confusion. When importing data, it’s imperative to review the consistency of the information. This can be done with filters in Excel, where columns can be evaluated visually so that discrepancies can be identified.

The Pain, Part 1: Time

Time is perhaps the most valuable resource lost when it comes to cleaning up data. It will always be a burden, but the most strenuous parts can be nipped early on. A few hours of maintenance once a week or a month will offset the many hours of cleaning up when done annually or longer and will lead to inaccurate reporting in the meantime.

A sales team might often spend more time chasing down phone numbers and emails than actually connecting with the contact. Similarly, one might chase after a lead and spend time making calls or sending emails for a lead that isn’t even a potential prospect. The frustration from unproductive work and the annoyance of bad data can be unhealthy for the workforce.

Over time, if the data is consistently causing trouble, staff may choose not even to second-guess the data but to ignore it altogether. One or two bad email addresses or phone numbers will cause some frustration. Then, as more time is spent deciding on if the data is useful or not, second-guessing occurs. After that, it may take multiple reviews, which may cause multiple tiers of personnel to take time to make decisions on what is good or bad data and how to proceed. In the end, bad data can cause, at the minimum, annoyance from staff, not taking into consideration the potential business or relationships that could have been created.


The Pain, Part 2: Reputation

Inaccurate data can lead to bad or misguided decisions. When enough bad decisions are made, resources can be lost due to frustration internally and externally. As well, forecasts made with bad data can put an organization on the wrong path. Not having accurate data can lead to a loss of trust from employees, an overseeing committee, and the general public. A thousand accurate reports may not offset one or two inaccurate ones, and it takes time to build back trust.

Your Solution is Simple. Not Easy, But Simple

Well begun is half done.
 — Mary Poppins (Disney Pictures)

Have a plan and stick to it. It’s tedious work, but ultimately it saves time for the staff involved to figure out practices that work and the most meaningful structure. Setting up processes and guidelines for staff can make a tremendous difference in the data that is collected and used. 

  • Define what is most important
  • Remove duplicates
  • Normalize data
    • Is a Yes/No field accurate, or should you use a date field? A date field can imply an actual date that data collection date, or it could be blank, which is a “no,” but it still allows you to create searches on time periods.
  • Run reports to ensure data is standardized and accurate
    • Identify old records that can be inactivated or deleted. Archiving and saving data can be helpful, but a balance exists between storing useless data that clutters the database and keeping potential long-term opportunities.
    • Identify records to follow up on using traces. Correct or add to the records.
    • Find incomplete records. This can mean finding accounts without a region or market segment or finding accounts with contacts. Evaluate the need for a record to exist. Does it have contact information? Is there any background? What are we going to do with it? If it’s incomplete, either fix it or remove it. 
Common Practices to Have
  • Data input
    • Check for existing records before adding a potential duplicate.
    • Maintain data health by regularly running reports to find irrelevant or duplicated data. Merge or recycle records as needed.
    • Review Custom Fields (UDFs/User Defined Fields). Are these fields used anymore? Can there be a better way to store them?
    • Check for data Types. Is a Yes/No needed, or something more open like a Yes/No/Unknown option? Is an open text field truly better than corralling data entry into a dropdown or multi-select field?
    • Is it relevant? Storing, or more importantly, having staff store unnecessary data can clutter things up and even cause staff frustration.
    • Are fields truly defined as they should be? A zip code is actually a text field. Although it looks like a number, it really is not. A numerical field can be calculated, and zip codes are not added or multiplied against other factors.
  • Automated processes and reports
    • Are there any outdated reports being sent out? More importantly, outdated triggers that create unneeded traces? Review reports and searches for clutter and remove those that get in the way. It’s easier to scroll through fifty relevant searches than a few hundred that haven’t been used in over a year.
  • Check for conflicts
    • Are there inherently conflicting records, like a zip code not matching a state or region? Or there might be a difference between a lead’s market segment and the account’s market segment.

Ultimately, it’s important to evaluate existing data, the collection process, and how data is used. Staff and goals can change, which impacts processes and the importance of certain data collecting. Taking a step back every few months to review each record type and reviewing the dropdown options, custom fields, and other data points can help correct the ship’s course. 

As always, Simpleview can help with guidance on best practices, mass data updates, and restructuring. After evaluating records, if it’s best to change a multi-select field into multiple corresponding Yes/No fields, we can assist. Your CRM analyst is available to help with any questions and offer guidance.

Whether you're eager to upgrade your Simpleview CRM system or just ready to retire your existing solution, we're here to help.

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