The Hidden Costs of One-Off Data Cleanups

No one wants to spend more money than they need to. That’s one reason many organizations treat data maintenance as a one-off cleanup project rather than a continuous process. They may believe the one-off expense will save them money over continuous data maintenance while being an easier sell to stakeholders.

Those assumptions are faulty.

A one-off data cleanup project is never just a single project—regular cleanup projects become necessary as data issues stack up in your database. It actually costs more, even in the short term, to treat data maintenance as one-off projects instead of continuous cleanup and automation.

Data cleanup projects have many hidden costs and implications:

• Planning and orchestration costs: Data cleanup projects are time and resource-intensive. You must collect information, understand your issues, gain access to systems, define goals, research cross-departmental data issues, and coordinate with stakeholders.
• Between-cleanup data degradation costs: Between cleanups, new data with issues are flowing into your CRM, degrading data quality. This hurts your ability to segment your database, personalize messages, implement automation, and use accurate context when engaging with your customers and prospects.
• Opportunity costs: What could you have accomplished in the time you spent planning and executing a giant data maintenance project—especially given that you’ll have to repeat the process in the future?

Treating data maintenance as separate standalone projects means that each project will be unique. You’ll have new sources of data with new issues to identify and correct. Each time you launch the project, you’ll incur the above costs repeatedly.

One-off data cleanups are like the ancient Greek tale of Sisyphus. In Greek mythology, Sisyphus, the King of Ephyra, was punished by Zeus for cheating death. So the king of the gods sentenced Sisyphus to roll a heavy boulder up a hill—only for it to roll down every time it neared the top. Sisyphus was thus doomed to push the boulder for eternity.

Data maintenance is a Sisyphean task if you treat it as a one-off project. You push the heavy boulder up the steep hill with each cleanup, only for it to roll back down again as your data quality degrades between each project.

But there is an alternative to paying for the same thing over and over. The alternative is using automation to continually clean and standardize your data while building processes and policies to improve data quality over time. With continuous data cleanups you’ll save money, time, and frustration.

A One-Off Data Cleanup Project is More Expensive Than it Seems

Planning and executing one-off data cleanup projects is often more complicated and expensive than companies anticipate. As you dig into the details, the scope of your data issues becomes clear, and the task grows. Here’s an example of the progression of steps in a typical one-off data cleanup project:

Analyze: First, you need someone with the expertise to analyze the condition of your data and plan the data cleanup project. You can’t plan a data cleanup project without knowing what needs to be fixed and what resources you’ll need to fix it. This step is critical, and handing it off to an inexperienced colleague will not likely produce great results.

Allocate: Next, you have to allocate time to plan the project. This task includes collecting necessary information, gaining access to systems, defining goals, understanding cross-departmental data issues, and coordinating with stakeholders.

Identify and Fix: Then you move on to identifying the problematic data. Your database includes many types of issues: inconsistent data, missing data, improperly formatted data, and outright clutter, to name a few. Identifying and fixing each type of issue requires a specific solution. While many issues can be identified using slick Excel formulas, other issues will be more complex. You may need development resources to find and correct them. And as complex data problems stack up between cleanup projects, it will require more effort and time to fix them. This brings us to the fourth step.

Repeat: Data issues in your CRM will change between cleanups. There is no guarantee that previous solutions will be a good fit for the next round of data issues. You essentially have to start from scratch each time. And beginning from zero means a longer, more error-prone process.

That’s a lot of steps to go through only to have to start over again each time. Contrast this with consistent data maintenance that allows you to create and hone processes continually.

Costs of Degraded Data Quality Incurred In Between Cleanups

Data quality degrades between cleanups, hindering the effectiveness of your teams and the experiences of your customers. Data quality issues are often overlooked or accepted until they inhibit something critical. But by then, the damage is done. How does this happen?

Data continually flows into your CRM. That data comes from visitors filling out forms, manual data entry by team members, API calls, CSV data imports, and integrations with third-party apps. And all of that data is certain to have issues, inconsistencies, and outright inaccuracies that will inhibit critical processes. Infrequent data cleanups mean that data issues will pile up, and embarrassing mistakes will creep into your marketing automation and communications with customers. You’ll find that you have issues with:

Personalization: Between cleanups, your ability to personalize messaging degrades with your data. Even simple things, such as calling a prospect “JANE” instead of “Jane” in your automated emails, can impact how they feel about your brand. When your marketing teams do not have faith in the accuracy of the data in your CRM, they are less likely to use that data, leading to less personalized experiences across the customer lifecycle.

Segmentation: Those data issues also impact your ability to segment your data, which impacts personalization, lead scoring, and reporting. Segmentation relies on standardized data and impacts your ability to prioritize prospects and customers. For instance, you may want to give more or specialized attention to CEOs that enter your CRM. But if your job titles aren’t standardized and the data include titles like Owner, President, C.E.O. and Chief Executive Officer, some CEOs may not be flagged by your system. This causes them to be inaccurately scored and prioritized internally, missing important opportunities at every step of their journey.

Engagement with your Customers and Prospects: Issues like duplicate data and low-quality data can also impact your company’s ability to connect with customers. Companies with high duplicate rates will never have a clear understanding of their historical interactions. Reps will step on each other’s toes. Those issues skew reporting and influence decision-making processes. Data issues mean that all communications with customers—whether from marketing, sales, or customer support—have an increased likelihood of missing context or including outright inaccurate information at all points in the customer journey.

All of these issues lead to real-world revenue impact for companies, which adds to the cost gap between project-based and ongoing data maintenance.

Opportunity Costs of One-Off Data Cleanups

Treating data maintenance as a series of one-off projects opens your organization up to significant opportunity costs. What could your employees have accomplished in the time they spent planning and executing your data cleanup project? Likely quite a bit.

Data cleanup projects are notoriously complex and difficult to organize. They require expertise and high-level project management. The highly skilled employees you’ll have to pull for the job could be focusing their time on other strategic projects rather than working on data maintenance tasks.

When sales reps have to step away from their normal duties to focus on data management, they can’t spend that time building relationships with prospects. Marketing team members who are pulled into data maintenance projects can’t focus on deepening targeted campaigns through segmentation and personalization.

Your support teams will also feel the effects. Rather than helping customers and closing more tickets, they will have to spend their time fixing issues, digging through duplicate records, and double-checking data, leading to slower response times, poor customer experiences from incomplete context, and lower morale. 

There is also opportunity cost associated with allowing bad data to accumulate in your system. Your sales reps will step on each other's toes due to duplicate records or miss out on sales because they lack context. Your marketing teams will be reluctant to depend on newly added data if there is no mechanism to ensure its quality. As a result, they will still avoid using data in complex ways, lowering conversions and morale in the process.

Usually, management is well aware of the opportunity costs associated with data cleanup projects. They have a top-down view of the resources necessary for these projects—which means they often shy away from them entirely. This exacerbates the inflated costs of infrequent data cleanups.  

The opportunity costs associated with one-off data cleanups alone negate any perceived “savings” from cleaning data occasionally rather than continuously.

Why Continuous Data Cleanup Automation Costs Less

The process of cleaning data relies heavily on mundane, repetitive tasks. Fixing issues in a data set is often a long and unrewarding process. It can be morale-killing.

But nowadays, software can take many of those repetitive tasks off of your plate through smart automation. By automating the painstaking mechanical aspects of cleaning data, you free your employees from tedious data maintenance while improving morale.  

Over time, you can refine this automation to identify and solve more data issues. You don’t have to reinvent the wheel or plan a complete data cleanup project multiple times per year. With automation, the amount of time your employees spend on data management will decrease even though you are collecting more data.

With automation taking care of your most important issues, you can focus on less common but impactful issues and respond more quickly to data problems as they arise. Clean data also impacts the costs of other internal projects, increasing company agility and avoiding data issues that can stall projects and slow progress.

Comparing the Effort of One-Off Data Cleanup vs. Continuous Data Cleanup

Starting from scratch and building each data cleanup project from the ground up will not save you money. It will inflate your costs.

Compare that to the continuous cleanup approach. Here, the time required to coordinate and execute cleanup projects is minimal because you are always building on the groundwork you’ve laid. You’ll have automated solutions in place for common problems and can tackle new issues as they arise. Employees will be better trained because they engage in data maintenance regularly rather than occasionally.

Continuous data maintenance ensures that your resource requirements are more predictable, making the project more likely to stay on budget.

Here’s a sample analysis of the effort involved in a typical data cleanup project, compared to continuous data cleanup strategies:

There is a stark difference between these approaches.

While initial costs are similar, a continuous cleanup strategy quickly results in significant savings that grow over time. You still have to do all of the initial planning—identifying stakeholders, auditing your data, project planning, hiring experts, getting approvals, and other tasks. These will always be present when you start a new data cleanup project. But with continuous cleanup, these tasks only need to happen one time.

With one-off data cleanup projects, you bear the full brunt of the costs of each project. Because you start from scratch each time, there are very little cost savings from one project to the next.

Continuous cleanups do not require the same level of planning as large-scale projects. Instead, you focus on building on what you already have in place. You do this by installing data management automation to solve issues, building processes to limit issues over time, and ensuring that your teams are constantly involved in data management to reduce the learning curve and avoid knowledge loss.

Then you have to consider that, even with your most informed estimates, larger, sporadic data cleanup projects are more likely to run behind schedule and over budget than the opposite. There will always be unforeseen issues with identifying or fixing data issues that will slow you down. And this is especially true when you run multiple “one-off” data cleanup schedules, where the scope of issues is more difficult to determine. Many data cleanup projects remain half-finished or require additional rounds of approvals as the scope expands.

Data Maintenance Automation is Cheaper and More Effective

When it comes to data maintenance, your goal is to fix issues quickly and save money.

There are two approaches companies typically take:

They may conduct “one-off data cleanups” which are often large-scale projects to improve data quality that happen sporadically as needed or at defined intervals, such as yearly.

Or some companies tackle data management and data quality improvement as a continuous task, building on the systems and processes they have in place over time. 

Doing occasional, one-off data cleanups costs you more than continuous data maintenance while eating away at the morale of your teams. Band-aids aren’t a real solution and only work for so long.

With one-off data cleanup projects, you end up paying those startup costs each time you launch a cleanup. Those costs include:

• Planning and orchestration costs: You’ll have to find someone knowledgeable enough to analyze your data and plan the project. Then you have to allocate time to plan the project.
• Between-cleanup data degradation costs: Between cleanups, your bad data snowballs in your system. As a result, your sales teams will lack critical context due to duplicate records and will step on each other’s toes. Your marketing teams won’t effectively segment and personalize their messaging. And your support teams will have to spend time double-checking data, leading to slower response times and the decline of your brand reputation.
• Opportunity costs: When your sales and marketing team members are pulled into data maintenance, they can’t work on their usual, growth-focused tasks.

One-off data cleanups appeal to many companies because they represent a short-term commitment with a defined scope. But often, that scope grows as your data issues are analyzed, and solutions developed. Then you must pay the full project costs with every new cleanup project kickoff.

Significant data management savings are unlocked with continuous data cleanups and automation, enabling you to keep your database clean while building on what you have over time.