Context is everything. Facts, events, statements, and statistics without proper context have little value and only lead to questions and confusion. This is true for life in general, but it’s especially applicable to the data you use to power your business.
Data without context is just meaningless noise, and any effort to improve or extract value from your data without considering the larger business context is doomed to fall short. Unfortunately, traditional approaches to data remediation often focus on technical data quality in isolation from the broader data and business ecosystem.
In this blog post, we’ll compare traditional data quality vs data condition — the big picture approach to data improvement we use here at Anmut. We’ll start with basic definitions and then take an in-depth look at the characteristics, strengths, and weaknesses of data quality and data condition. Keep reading to find out everything you need to know about data quality vs. data condition and why understanding your data’s context is so powerful.
Data quality vs data condition: basic definitions & differences
Data quality and data condition share the same basic goal of making sure your data is in good shape and ready for its intended use. Both measure and compare the current state of your data against the desired state, but the approaches — and the results — are quite different.
Data quality assesses your data against standard technical performance measures such as completeness, accuracy, timeliness, validity, and uniqueness. These measures can be applied to any dataset at any company regardless of industry.
In contrast, data condition determines whether your data is fit for its intended purpose by going beyond technical data metrics and diving deep into the larger business context. Data condition includes an assessment of how the data is used, the value it brings to the business, and the systems that support its collection, maintenance, and usage. Since every business uses data differently, data condition assessments are unique to each company and can be tailored to fit your needs. The assessment process is the same, but your data’s fitness for purpose is measured against the specific needs of your business instead of an arbitrary technical standard.
Putting things in perspective: A helpful analogy for understanding data quality vs data condition
Sometimes the easiest way to understand something is to compare it to something else. Before we dive into more details, let’s explore an analogy to help put things in perspective and set the stage for a deeper understanding of the differences between data quality and data condition.
Think about the dashboard in your car. There are gauges to tell you how fast you’re going, how much gas you have, your engine’s RPM, whether your lights are on, what gear the car is in, and other things you need to know to make sure your car is ready to get you where you need to go.
In a data quality approach to reading your dashboard, you would simply read the gauges and observe that your tank is half full, your lights and windshield wipers are off, and you’re driving along at 70 miles per hour. Useful information to be sure, but without more context, it’s unclear what those observations mean and whether you should take action.
A data condition approach considers the context you need, to put what you’re seeing in perspective. In the car dashboard analogy, this context includes factors such as how far you’re going, the time of day, the weather conditions, and the speed limits. Depending on your destination, half a tank of gas may be all you need. If the sun is shining and the weather is nice, there’s no need to adjust anything, but if it’s dark or raining, you should turn on your lights and wipers. On an open highway, 70 miles per hour is perfectly acceptable, but if you’re near a school, it’s time to slow down.
A closer look at data quality: How it works, how it helps & why it’s not enough
How data quality assessments work
Benefits of data quality
Identify technical issues
Why data quality falls short
No link to the business
No connection to performance
No optimised remediation
No root cause analysis
A closer look at data condition: How it works, how it helps & why you should embrace it
- How does your business use different types of data?
- Which data is most critical for your business?
- What does your business need from your data to be successful?
- What is your business trying to accomplish with your data?
- Which business decisions rely on your data?
- What needs to be done to improve the fitness for purpose of your data?
- How does improving data condition unlock value?
- Which data remediation actions will unlock the most value?
The answers to these questions help frame the data in the broader business context and provide a business (and value) centric lens for assessing the data. The goal is to assess your data in a way that includes both technical quality and the people, processes, and technologies that make up the ecosystem around your data. Doing this from a use case perspective keeps business needs and value front and centre.
A data condition assessment starts by building a list of use cases for the data you want to assess. Most data sets have multiple end-users and therefore multiple use cases with unique processes and requirements. It’s important to capture and consider all these use cases. You’ll prioritise your use cases based on the value of each data type and the importance of the business decisions the data supports.
Once you have a prioritised list of use cases, it’s time to interview users to understand how they use the data for each use case and the data-related pain points they encounter. User interviews capture detailed user perspectives on data condition and make sure the voice of the business is heard. The resulting user stories enrich your qualitative assessments and help paint a holistic picture of how well the data and its ecosystem are meeting the needs of your business.
The results of these user interviews are analysed in a series of structured workshops and translated into a set of bespoke, business-centric set of requirements for your data, giving you a clear standard for measuring data condition. As part of Anmut’s data condition process, we walk you through these workshops and help you develop measurable data condition criteria for each use case.
The resulting metrics are all designed to help you measure fitness for purpose and improve your data condition. We use those metrics to measure the condition of your data for each relevant use case. In our work, we produce a data condition score for each data set to help you understand how your data is performing, what’s preventing best performance, and how to identify and address the root causes of your data problems.
Benefits of data condition — and why it’s a better choice
By seeing your data in the context of your business as a whole, data condition gives you the insight you need to understand the value of your data — and to communicate its value to others. You’ll also see whether your data is meeting your business needs and where it falls short.
No other approach to data improvement that we’ve seen (and we’ve looked at A LOT) gives you the tools to assess your data in a way that’s holistic, relevant to the business, and designed to add value to your organisation.
Here are just a few of the benefits it brings:
Tailored processes lead to results
Links data directly to the business
Powers efficient remediation efforts
Enables smart investment for tangible returns
Enables monitoring & tracking of data condition & improvements
We hope you’ve enjoyed reading this article and learning more about data quality and data condition. Here at Anmut, we get to see the benefits of data condition all the time, and we love to share what we’ve learned with others who might be interested in adopting this approach.
We’ve built our business around helping people like you understand and unlock the enormous value of data, for all stakeholders be they customers, investors, employees, suppliers, communities and the environment. If you’d like to learn more about data condition and what it can do for your business, get in touch. Ask all the questions you want — we’ll be happy to answer them. If not, you might be interested in finding out what your data is worth.
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