Better Data, Bigger Impact:

Seven Tools That Make a Difference

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You’ve dedicated yourself to making a difference in public health. Don’t get held back by bad data. Bad data keeps you from understanding who is accessing your services and whether your services are helping these clients. It also prevents you from feeling confident about the story you share with stakeholders and the decisions you make to guide your organization. Ultimately, bad data hinders your ability to improve your clients’ lives and the welfare of the communities you serve.

Fortunately, you don’t have to take on bad data alone. A robust data system, with an array of tools that protect against invalid, duplicate, missing, and inconsistent data, can help you immensely. Think about these tools as a team, much like a construction crew, your favorite band, or your own public health organization, in which each member can have a different role to play, but all work together in pursuit of a collective goal – in this case, data quality. Let’s explore seven of these tools to look for in your public health data system, and how each of them contributes to better data in one or more important dimensions of data quality, including accuracy, completeness, consistency, timeliness, uniqueness, and validity. By helping you get your data “right” across these dimensions, the following tools support you in the important work you do for clients, communities, and the common good of public health.

Real-time Validation Checks

Imagine having a data-checking ninja always at the ready, stealthily validating data as data is entered by hand. Meet your system’s real-time validation checks for key-entered data! These checks use automated rules, set up in the background by software developers, to make sure that key-entered data adheres to predefined criteria before being saved to your system. If data doesn’t meet these criteria for validity, you get an immediate alert or error message prompting you to make corrections, thereby protecting your data’s quality.

Skip Logic

Let’s say you’re talking with a friend who lets slip that they can’t really relate to the topic you’re discussing. How do you respond? You might well change the subject to one that is more relevant, right? After all, continuing to talk about an irrelevant topic with your friend would just be awkward for you both. The same holds true in data collection.

You want a system to respond to your input the way you do to your friend’s. With “skip logic,” your system does: It determines which question you see next based on how you answer the current one. As a result, you never see questions that aren’t relevant or to which answers would contradict the data you’ve just entered. This solution helps minimize the potential for data collection fatigue (which can lead to errors in data) as well as incongruencies in your data. Skip logic thus supports both data accuracy and data validity.

Validation Checks for Uploaded Data

Validation checks for the data you upload to your system work together with the validation checks for key-entered data. We could say that while one of these sets of sentinels watches your systems’ front door, the other watches the back. Validation checks for uploaded data scan your uploaded files for correct formatting and structure, and the data in each record of your files for adherence to predefined standards. If these validation checks detect issues like incorrect formats or missing values, you are alerted to make corrections or add the missing data before the data gets saved to the database. In this way, these checks ensure data quality in four dimensions: accuracy, completeness, consistency, and timeliness (since the complete data will be readily available, when you need it).

Alerts About Missing or Incongruent Data

Alerts about missing or incongruent data notify you when your data is incomplete or doesn’t match expected patterns. An example of the latter may notify you if a client received a service but wasn’t marked as having been assessed for needing that service, for instance, or if a male-at-birth client is also identified as pregnant. These notifications proactively highlight potential data entry oversights and inconsistencies, prompting you to review and rectify any issues and contributing to data accuracy, completeness, and timeliness. Like other tools and processes that help ensure data quality, this solution reduces the risk of making decisions based on incomplete or inaccurate information.

Checks To Reduce Duplicate Data

Checks to reduce duplicate data involve processes that scan and compare incoming data, whether key-entered or uploaded, to data that already exists in the system. If duplications are detected, the system lets you know before data is saved, thus helping to minimize the introduction of redundant data into the system. This solution not only contributes to data quality by helping to ensure the uniqueness of the data you enter, but it also prevents errors and confusion that duplicate information can cause.

Options for Resolving Duplicate Data

And if duplications still manage to sneak into your data, which they sometimes do, you want options for resolving them. Options include tools or processes for identifying, reviewing, and handling duplicate records. These tools and processes may allow you to merge duplicate records, select the most recent or accurate entry to keep, or flag data for manual review. Whatever form these options take, they help you maintain data uniqueness in your system, which contributes to the trustworthiness and reliability of your datasets.

Data Quality Reports

Last but not least, you want reports that provide information about entered data, including details like the user responsible for data entry, the timestamp of data input, and flags for missing, incongruent, or duplicate information. Through such reports, which can show you the “life story” of your data, you’re able to identify problems in any of the six dimensions of data quality – accuracy, completeness, consistency, uniqueness, validity, and timeliness – and then determine when interventions such as additional training, adjusted workflows, or extra tools might help address the causes.

Better Data = Bigger Impact

To accurately understand who is accessing your services and whether these services are helping your clients, you need good data. Your public health data system should ease the burden of ensuring data quality with built-in solutions that check data as it goes in, alert you to problems with data already in the system, and provide options for resolving issues. These tools help you make confident data-driven decisions, tell your organization’s story, change the lives of your clients, and strengthen the communities you serve.

To learn more about how you can leverage our know-how at Luther Consulting to ensure your data quality, please contact us. We’ve been helping public health champions just like you for over twenty years. We’ll be glad to share how you can use the array of tools built into our client-centered public health data system, Aphirm^®, for better data and bigger impact. You see, we don’t just make software. We help you make a difference.

For additional information about accuracy, completeness, consistency, uniqueness, validity, and timeliness as dimensions of data quality, see The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions (DAMA UK).

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