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The 4 Considerations Before Choosing Data Cleansing Services

With the right tools, data is changing how organizations interact with its constituents and deepening relationships in ways that were not possible several decades ago.

Many organizations are beginning to realize the value of their data, willing to take the necessary precautions to protect it. Beyond helping with fundraising initiatives, the larger picture shows that data has become instrumental in decision making. Maintaining the integrity of data is not only a good idea—it should be a priority.

One of the best ways to ensure that your data is reliable and accurate is your organization’s approach to data hygiene. When many people and systems manage your data, there is an increased likelihood is that data will become “dirty” over time. In the context of this post, dirty is a way to describe data that is either incorrect, missing, or out of date.

For example, if your database is riddled with typos or duplicate records, you have a bit of “dirty” data on your hands. While you can have a strategy to minimize the chance of dirty data, the truth is that even the cleanest, well-kept homes need a deep cleaning once in a while.

You may be in the process of deciding how to cleanse your data best, especially if you’ve never engaged in a similar project before. If you are considering hiring outside help to deliver data cleansing services, check out some of our top considerations to help you choose the right one.

What to Look for in Data Cleansing Services

When tackling a large data cleansing project, choosing the right vendor will ultimately depend on comprehending your organization’s needs. At a base level, the best way to select data cleansing services is first knowing what tasks need implementing.

1. Standardization

Standardization is the process of transforming existing data that was recorded in multiple formats. The variance is due to the fact that each system likely has its preferred format for storing data. For example, one system may retain a donor’s date of birth as 1980-01-01, while another has it as 01.01.80. Despite the information being the same, data standardization becomes necessary.

Defining a new standard and transforming data to reflect this change in format is crucial for data when managing it in a single system of record (SoR). Correcting conflicting data formats is an important first step for data cleansing, so make sure your solution accounts for standardization.

2. Merging & De-Duping

Duplicate data is the bane of any database administrator’s life. Duplicates are oftentimes the single greatest offender when it comes to jeopardizing the accuracy of your data.

Standardization can do a lot to minimize these occurrences, but nonprofits will always have to contend with duplicate data and the havoc it can cause. Every database is bound to have some duplicate records through everyday data entry or importing oversights.

Data integration is a method by which you will merge data from multiple places into a SoR. At this point, the amount of duplicate data may discourage you, but this is a necessary step in guaranteeing the accuracy and cleanliness of your database.

3. Validation

After the process of merging and de-duplicating your data, the next task to take on is improving the quality of your data.

Once duplicates have become less of a concern, there’s still a chance that a host of data remains questionable regarding its accuracy. Whether it was initially recorded incorrectly or something happened during the standardization process, you still want to verify that all information is correct.

Finding the right solution can be tricky because validation typically requires referencing an outside data source. For example, you may have standardized all addresses, but it’s possible that zip codes are not correct for several donors. Another thing to keep in mind is that you may also need to identify missing or incomplete information.

4. Data Appending & Enhancement

Where validation is the process of verifying the accuracy of some of your data, data appending is a similar task for improving data quality. To best engage your constituents, you have to know as much about them as possible.

Data appending is where you can retain more in-depth and comprehensive information. Appending accounts for missing or incomplete information discovered during the data validation process.

Appending data is typically the last step in a concerted data cleansing project, but it’s an extremely valuable exercise worth considering.

How Siloed Data Impacts Hygiene

Before even thinking about how to begin data cleansing efforts, it’s critical to understand why data becomes dirty. It’s less of an issue with people and more of a technology problem.

In organizations managing large sets of data, it’s more than likely that their data lives in many places. With the adoption of cloud technology and web-based software, the landscape of data has become somewhat complicated.

Data becomes increasingly difficult to manage when it resides in multiple places. When data is siloed off in this way, it can be almost impossible to maintain the integrity of data if these systems lack a natural method of integration.

For nonprofits, having data living in countless places requires more tedious work from database administrators. But even with extra effort to manage this data, nonprofits likely have an incomplete picture of their donors. Unless explicitly addressed in a broader data collection strategy, data can become inaccurate across multiple systems, teams, and software.

The most effective way to prevent data from getting dirty is through data integration tools that can help store everything in a single system of record (SoR). From there, better hygiene can be achieved by regularly cleaning data.

Benefits of Data Cleansing

There is certainly a lot to think about when starting down the path of better data hygiene. While you could certainly invest the time and manual effort to cleanse data on your own, it’s at least worth considering the option to have a vendor provide these services for you.

Here’s a quick look at a few of the benefits of data cleansing:

  1. Improves the efficiency of donor acquisition activities
  2. Improves the relationship with recurring donors
  3. Improves decision-making process
  4. Streamline business practices
  5. Increased productivity of database administrators and outreach team members
  6. Increase fundraising potential

In the end, you want to do everything you can to ensure your data can be trusted. Many of us dread the annual spring cleaning of our homes, but databases need the same kind of attention. By working through your database to ensure it is in its cleanest state, you are setting your team up for efficiency and success in the coming year.

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