Organizations across the world have a continual challenge: consuming, processing, and integrating business data into their systems to create actionable insight and drive future plans. This data-driven world we live in is hardly a recent event, with organizations reporting that they have stored more data in the cloud every year since 2015.
Yet, even with a high threshold for collecting data, sheer quantity does always guarantee more effective results. A huge factor that businesses need to account for is the quality of the data they collect and process. High-quality data is easier to feed into analytical engines, allowing you to create insight that you can then use to determine the best course of action.
However, poor-quality data is more tedious to manage, often needing more transformations or sanitizations before it is ready for analysis. These additional processes have a direct impact on resource consumption, increasing the cost of data-related endeavors. Yet, as data-driven decision-making is now a vital part of business strategy, improving data quality across the data pipeline should be a core objective.
In this article, we’ll dive into the leading methods, strategies, and precautions you should take when dealing with data processing. We’ll cover exactly how you can increase data quality in your business, helping you to save resources and drive data adoption across your organization.
Let’s dive right in.
What is data quality?
Data quality is an umbrella term that describes how well data follows certain criteria. These criteria directly correlate with aspects that will make data easier to ingest, collate, and analyze.
Here are some characteristics that define the average quality of data:
- Accuracy: Data that is accurate refers to the level of correctness of the data. Highly accurate data would be free from errors and reflective of the real-world values you have recorded.
- Completeness: Data that is complete is fully filled out and does not contain any gaps or missing values.
- Consistency: Consistency refers to the ability of data to remain uniform over different deployments and datasets. For example, data suggests the same thing despite coming from two different sources.
- Timeliness: Timeliness is a term that defines how up-to-date your data is. Data produced in the last 24 hours might be more applicable to business processes that require a short turnaround. Alternatively, if you’re looking at historical trends, then older data is more timely.
- Relevance: You could have the largest dataset in the world, but if it has nothing to do with what you want to find out, then it is a waste of time. Poor data typically has little relevance to your business objectives.
Low-quality data doesn’t just frustrate data engineers and slow down your business processes. It has a much more direct effect, with low data costing businesses upwards of $3 trillion dollars each year. That figure only reflects businesses based in the US, demonstrating just how significant poor quality data can be worldwide.
Strategies to Improve Data Quality in Your Organization
Improving data quality in an organization won’t happen overnight. Similarly, as data flows across the entire data pipeline, it takes more than just a few small tweaks to change the average quality of your data.
However, there are certain methods that you can employ that will help to set you on the right path. Here are some leading strategies you can use to improve data quality in your organization.
Create and enforce data standards in your business
Without a core data standard that all of your engineers know, understand, and follow, you will never have a consistent standard of data. Your data standard strategy underpins every single interaction you have with data, allowing you to create clear naming conventions, structure strategies, and data entry systems.
If your business constantly runs into completeness and consistency problems, then creating and enforcing data standards can go a long way toward overcoming your core issues. The more extensive your data documentation is, the more likely you are to receive high-quality data after the ingestion process.
Instate data cleansing processes
A fantastic way to improve the average quality of data that you interact with is to implement a number of data cleansing systems that help to locate and neutralize errors. For example, these systems can comb through recently sourced data and locate any duplicate information.
Not only does this strategy help produce a higher data standard, but it also ensures that you waste fewer resources on ingesting duplicate or incomplete data.
Use automation where possible
Automation is one of the most powerful tools that the world of data has at its disposal. By utilizing automation techniques, businesses are able to remove the manual element of data entry and validation. Human error constantly contributes to low-quality data, making the eradication of this step an effective way of improving the quality of your data.
Businesses can also automate their data validation tools and clearing tools, helping to cut back on the most laborious tasks that maintain the quality of data. With all the additional time that automation creates, your data engineers can continue to work on testing for data quality and refining your cleansing parameters.
Use dbt for quality tests
One of the most effective ways of testing for data quality across the data transformation process is to use dbt. Also known as Data Build Tool, dbt is a command-line tool that streamlines the process of data transformation. When establishing data quality, you can run a range of tests with dbt, even creating custom tests that align with your quality investigation.
For example, you could create a dbt data quality test that traces whether there are any duplicated records within your business documents. As rapid and highly effective tests, this can give you a hand when attempting to discover exactly where your business could improve the general quality of its data.
Data is the leading resource of the 21st century, allowing businesses to plan for the future with a degree of certainty that has only been available for the past few decades. With its significance in modern operations strategy, creating a healthy and effective stream of data should be a company’s top priority.
By introducing the strategies and suggestions we’ve made in this article, your business will be one step closer to creating a high-quality, continuous, and dynamic flow of new data for ingestion. With high-quality data in hand, you’ll be able to spend less on data processing and focus more on the revenue-driving results that your company data can provide.
Best of luck adapting winning data practices over the coming months.
Featured image credit: Freepik