Take a moment to envision a work world where your data is centrally stored, organized, complete, valid, and maintained regularly. For nonprofit organizations, this data daydream could manifest as complete donor profiles, up-to-date mailing lists, or accurate measures of donations.
Back in reality, data is often messy, unstructured, missing, and/or inconsistent. Not only is poor data quality financially costly, but it can also lead to productivity loss, missing or inaccurate insights, poor donor relationship management, or worse, create a dent in revenue due to missed opportunities. Harvard Business Review points out that “it costs 10 times as much to complete a unit of work when data is flawed in any way as it does when the data is good.”
Could data stewardship alleviate your data nightmare? In this quick guide, we’ll explore the key principles of data stewardship, review the characteristics that make data ‘good’, and share practical tips to getting to a state of clean data.
If you’re thinking, ‘I’m not a data analyst, data scientist, or database manager, why should I care about data stewardship?’, consider how data affects everyone in your organization. Depending on your role, you may support data entry into your centralized CRM database. You likely pull data points to generate KPI reporting. Perhaps you share data with agency partners. Or maybe you use data to determine the value of a donor’s giving history.
All and all, everyone on your team has a duty to protect and maintain data. That’s where data stewardship comes in. Informatica defines data stewardship as “a collection of practices that ensure data is accessible, usable, safe, and trusted.”
Data has become one of, if not the most valuable assets to an organization. Protecting the integrity and usability of data is an integral role for all members of staff. Ask yourself, does your team know what makes data good?
Good data and clean data are related concepts, but clean data is an important component of good data.
Most can agree that clean data is free from errors, inconsistencies, and inaccuracies. Clean data does not have duplicates or spelling errors and is in the correct format.
Good data includes the elements of clean data. However, good data can be trusted to inform business decisions. Below, review a handful of qualities that make data good.
On the contrary, bad data includes missing or incomplete records, a lack of data standards, duplicate accounts, outdated data points, and more.
Explore the elements that make data good by examining a few examples.
Next, let’s find out ways to better your data stewardship to achieve a state of good data.
Clean data doesn’t happen overnight. While professional data services can help clean your data during migration or conversion, day-to-day acts of data stewardship across your organization can positively impact your data quality.
Here’s a non-exhaustive list of ways to become a better data steward and improve data cleanliness.
As mentioned, each team member plays a role in the integrity of your entire database, but not everyone contributes the same. First, identify who is responsible for gathering and maintaining data points. For many organizations, this responsibility would not fall onto a single individual.
Next, determine who needs to have access to data points. Does your finance manager need access to the same data points as a campaign coordinator? Chances are that each role in your organization varies on what data is relevant to them. Safeguard your data by setting data security permissions for sensitive or confidential data. Or limit permissions on data that does not need to be shared across multiple departments or members of your staff.
Data quality scoring is the process of evaluating your data points based on predefined criteria such as accuracy and validity. It helps organizations identify and prioritize data quality issues, track improvements over time, and make more informed decisions based on reliable data. Using an email validation service, such as ZeroBounce or Experte, to test the quality of your mailing lists is an example of data quality scoring.
Propper data formatting contributes to data consistency, accuracy, and usability. There are several types of data formatting to be aware of. Here are two common examples:
Based on what you’ve learned about data stewardship, your individual action of applying the provided tips will only go so far. If the rest of your team continues with bad data practices, your work may go unnoticed. Here are a few tips to spread the message of good data across your organization.
Lastly, if your team is looking for a less hands-on approach to resolving bad data, consider a data policy manager. Andar/360's Data Policy Manager module is a set of policies describing how data should be entered into your database. With a data policy manager, ensure your data is consistent and retrievable while eliminating the need to update, publish and distribute policy documents to every user. Interested in learning how the Data Policy Manager can help you get to a state of good, clean data? Contact our team
Blog Article by Hanna Middleton | Marketing Manager | Andar Software