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Three tips to find and remediate bad data within your organization

data
(Image credit: Shutterstock / whiteMocca)

Businesses are overflowing with data, whether they realize it or not. How businesses choose to handle and care for their data is reflective of their ethics, and ultimately a factor in their operational efficiency. To put it simply, bad data affects your bottom line – impacting your revenue, marketing efforts, customer retention, employee satisfaction, team productivity and more. 

If that’s not enough to convince you, Data Warehouse Institute estimates that data quality problems cost U.S. businesses more than $600 billion annually. Finding and remediating bad data so your organization houses clean, ethically sourced customer and prospect data is imperative, but it’s no simple task. Here are my top three tips to get your house in order.

Understand where your data is coming from

Within businesses, there’s often a misalignment between departments regarding who is responsible for the quality of data as it goes into a CRM system. 

While sales, marketing and customer teams typically enter the greatest proportion of customer and prospect data, it falls onto the CRM administrator to then manage the CRM system and ensure ongoing data quality. However, the administrator faces an uphill battle in ensuring that everything these teams are entering is accurate, clean and ethically sourced. Most often it also falls upon the CRM administrator to ensure the data is kept up to date, which is no easy feat considering the fact that the moment this data is entered into the system it starts to decay. In fact, Gartner reports that every month around 3 percent of data decays globally, while B2B data decays at a rate of 70.3 percent per year.

To mitigate this issue, it’s important to clearly identify where your data is coming from. Ask: Which department is entering this data into the system? ? Is it being pulled from another database and how often is that database being updated and is there a way to automatically receive updates to this data? How is the data being entered formatted and how does that potentially deviate from how other sources of this data are formatted? Do we have the appropriate rights and permissions to store this data in our system, and if so for how long?

Data privacy and security is becoming a bigger compliance risk for organizations every day,  what systems and people are entering data will allow businesses to identify where duplicate, outdated and incomplete data is coming from in order to remediate issues at the source. Automating as much of your data standardization and hygiene processes are key to ensuring high-quality data at scale across your entire organization.

Standardize how and where data is stored and maintained before importing it to your CRM system

Once businesses understand where their data comes from, they can begin the process of cleaning it up.

It’s far more expensive to remediate data after it has been entered into the system, so businesses should focus on cleaning data before it reaches the point of entry. Data experts often reference the 1-10-100 rule which states that though it may only cost $1 to enter data correctly and prevent bad data challenges, it can cost ten times that to fix bad data that’s already in the system. Failing to remediate that bad data can cost 100 times more than the initial $1, as it could cost you a customer, your reputation, and more. 

That’s why it’s crucial to fix bad data problems at the source. This entails establishing a guideline for what data an organization needs and what data it does not. In order to ensure that this guideline is followed, administrators should speak to a key stakeholder of  each department in their business about their data needs, how they store data, and how they plan to use that data in their work. 

First of all, define your data points and fields within your CRM system and document potential sources for each of these fields.

From there, the administrator should create clear, universal standards for all departments regarding the correct format of data the business needs and how that data will be updated on an ongoing basis. 

Try to keep the data input process for your team as simple and as quick as possible. Team members are more likely to cut corners if the standard data entry processes take too much of their precious time.

Make changes to your data systems cautiously and as necessary

It can be easy for businesses to get so focused on efficiency that they forget the obvious – prioritizing top-level needs when customizing their CRM system. Today’s needs can be quickly forgotten a week, a month or a year from now. Ensure the customizations you make today are properly documented for their intent and regularly audit whether or not your data needs have evolved over time. 

When users can easily view and edit accurate data, their jobs are made easier and they’re more likely to trust and value the CRM system. vs. breaking it down with disparate tools, services, and add-ons that only serve to slow down the technology.  

It should also be noted that the more system changes and customizations you make, the more time-consuming storing and maintaining data can become. So try to start with the minimum viable product (MVP) needed and then Once you can ensure the customizations are valuable and being used by the organization to derive business value you can then build out the complexity of your data model from there. As previously mentioned, ensuring that you clearly document all system changes, and communicate those to each department for ongoing ease of use and buy-in is critical, along with regular audits of your data across your entire organization so you can keep your CRM operating as efficiently as possible. The more complex the system you will deliver the slower and more frustrating experience for your teams, which means that teams will stop trusting and valuing the data the system is meant to provide in the first place. 

While we’d all like to be able to put a band-aid on data quality issues and only focus on innovating new products and services, the reality is that your data is your business’s most valuable asset. Data quality improvement has to be an ongoing effort across your entire organization. Business leaders that hone in on ongoing data quality initiatives will reap the benefits tenfold. 

If your business fails to safeguard and efficiently organize data, there are a myriad of challenges to come, perhaps most notably deterioration of the customer relationship. If you have accurate data about your customers and prospects, your teams are going to serve them better and improve your brand reputation and value. This means more accurate business reporting, better ROI on marketing campaigns, improved sales productivity and better customer retention.

Kate Adams, SVP of Marketing, Validity

Kate Adams is the SVP of Marketing at Validity, which helps companies target, contact, engage and keep customers using trustworthy data as a key advantage. She has more than 15 years of tech experience building strong businesses within different stages and industries. Kate previously served as the VP of Marketing at Drift.