Data Condition | What It Is & Why It Matters

Technical people tend to look for technical solutions to business problems. Sometimes a technical fix is what’s needed, but other problems demand a more comprehensive approach.

Most data problems fall into the latter category. The world of data is a highly technical space filled with smart people and cutting-edge tools. Successful data leaders know that you need more to unlock the value of data and use it to its full potential.

This is especially true for building and maintaining good data quality. Most data quality programs focus on the technical aspects of data quality and ignore the broader business context. In our experience, this approach results in short-term fixes that address the symptoms rather than the causes of poor data quality. We’ve perfected a more holistic approach known as data condition. 

On this page, we’ll introduce you to data condition. You’ll learn what it is, why it matters, why it works, and why we’ve chosen to endorse this approach.

What is data condition?

Let’s get started by answering the most basic question — what is data condition? 

It’s a measure of how fit your data is for its intended business purposes. To assess the condition of your data, you must go beyond considering technical quality based on traditional data quality dimensions.  

Instead, you must recognise that the fitness of your data depends on all the systems supporting it. You have to broaden your view to include data governance and data management — capabilities that contribute to data condition.

Data condition also depends on specific user requirements driven by business needs, strategic objectives, and the broader business context. Your data is in good condition if users can trust your data to support key operational and decision-making processes. 

To achieve and maintain good data condition, you must develop a thorough understanding of your entire data ecosystem. You need to know what types of data you have, how the data flows, who uses each type, how they use it, and the value each type of data brings to your business.

A key tool for doing this is something we refer to as the ‘use case lens.’ Looking at your data through a use case lens means considering how each type of data is used by the business. Each data type can have multiple use cases, and it’s important to understand them all.

Use cases define the intended business purpose of your data, making them foundational to understanding data condition. The use case lens gives you a holistic view and a firm understanding of your data landscape.

Once you know how users interact with your data, you have a good basis for assessing fitness for purpose. You’re also in a good position to start gathering user perspectives and articulating data needs in language that makes sense to people in your organisation.

Data condition involves additional steps such as gathering user stories and making technical measurements, but use cases are the foundation. They enable you to identify both the value your data provides and what needs to be done to make it fit for your business purposes.

Understanding why data condition matters

Defining data condition is a good start. Now we’ll go deeper to help you understand the importance of data condition and why achieving and maintaining good data condition matters for your business.

To put it simply, focusing on data condition helps your data strategy succeed. Up to 80% of data initiatives fail due to three basic problems:

1. There’s a gap between the data you have and the data the business needs.

The gap could be missing data, inaccurate data, hard-to-find data, outdated data, imprecise data, duplicate data, or something else. Often a gap occurs because businesses don’t know how to translate business needs into data requirements, leading to a data set that doesn’t support achieving business objectives.

Regardless of the cause of the gap, it leads to low trust in the data (and those managing it), slow decision making, wasted time manually verifying and correcting data, and wasted money re-collecting new data.

2. IT and business functions are misaligned and not engaged with one another.

Too often the relationship between IT and business functions is hostile and suspicious rather than cooperative and productive. The business thinks data is an IT problem, while frustrated IT teams display an attitude of “why doesn’t the business fix the data if they care so much about it.” 

The resulting tension leads to ambiguous roles and responsibilities, separate efforts resulting in isolated data silos and duplicated data stores, and wasted investment treating the symptoms of data problems instead of correcting core issues.

3. It’s not clear where you should focus your data investment and remediation efforts.

Which data is most important? Where should you invest first to get maximum returns? Without clear answers to these questions, many data initiatives overrun their budgets fighting fires and investing in expensive projects with no real value to the business.

Data condition matters because it offers a way to address the issues that doom so many data initiatives. Here’s how data condition helps your data and information efforts add value to your business:

1. Data condition closes the gap and makes the state of your data clear.

By measuring your data’s fitness for purpose, the gaps in your data and what must be done to correct them become clear. IT learns what the business needs from the data, and the business understands the condition of key data assets.  

You can quantify fitness for purpose and set clear goals for monitoring and improving your data condition. The resulting transparency fosters trust in your data, supports faster decision making, and increases the value of your data to the business.

2. Focusing on data condition brings business and IT together and establishes a quantifiable case for change.

Data condition is primarily determined through a “use case lens” based on user interviews with key stakeholders for each data asset. This brings business and IT teams together and reveals data-related pain points for both sides. The pain points can be related to the data or the organisation and support systems around it.

Business users get to tell their stories and explain how they use data, the problems they experience, and how data could add more value to their work. By listening to these user stories, you discover the root causes of data problems. Business and IT teams develop a mutual understanding of what actions will add the most long-term value to the business and establish a unified and quantifiable case for data remediation.

3. Assessing data condition enables you to prioritise your data investment and remediation efforts.

Data condition helps the business understand the true costs and benefits of data remediation projects. This understanding helps prioritise your investments and improve the condition of your data in an organised and sustainable manner.

With data condition as your foundation, you eliminate duplicate efforts and reactive, fire-fighting activities. Instead, you choose when and how to invest based on business needs, strategic objectives, and a quantifiable case for change. Focusing on data condition gives you the insight you need to spend time and money on the data initiatives that add the most value to your organisation.

The difference between data quality and data condition

Before addressing our approach to data condition, let’s quickly mention the difference between data quality and data condition. The basic purpose of data quality and data condition is similar: to ensure that your business has good data you can rely on for operational processes and decision-making.

The difference is in the approach — and the outcome. Data quality efforts usually have a narrow focus on technical quality such as 100% completeness, proper formatting, or collection frequency without considering the larger business context or systemic factors. The result is often short-term reactive measures that treat symptoms without addressing root causes.

Data condition is based on fitness for purpose within the context of specific use cases and higher-level factors such as data governance, data architecture, and data management that impact data condition. It allows you to discover the true data needs of the business and quantify value, so you can strategically invest to correct short-term issues and make sustainable improvements to the whole data ecosystem.

In summary, the issue is not a question of data quality vs. data condition. Data quality helps make data condition measurable, and data condition gives you the context you need to make smart data investment decisions.

How Anmut approaches data condition

We believe so strongly in the value of data condition that we’ve made data condition and remediation one of our core solutions. Our approach to data condition is integrated and focused on business value within the context of data asset management and creating value from your data throughout its life cycle.

Our data condition solutions are based on four key principles supported by our proprietary 60-point diagnostic assessment, our proven data condition framework, and flexible measurement tools.

At Anmut, data condition is always:

1. Value Focused

Our approach to data condition ensures that key stakeholders in your business understand which data assets are most valuable, what’s at stake if their current condition is not improved, and which data condition improvements will add the most value. We work with you to quantify the value of your data so everyone has a clear and common basis for strategy and investment decisions.

2. Use Case Driven

The foundation of our data condition solution is a fit for purpose assessment based on user interviews. These user stories bring the value of your data to life by revealing how users use data, the issues they experience, and the benefits data remediation could bring.

3. Holistic

We do a deep dive into the entire ecosystem surrounding and supporting your data. This allows us to consider both the qualitative and technical aspects of your data’s condition and to discover the underlying causes of persistent problems. The result is a comprehensive understanding of your data’s condition you can use to prioritise your remediation efforts.

4. Repeatable

Improving and maintaining your data condition is an iterative process. We teach you how to do it and then leave you with a repeatable process you can use as part of your ongoing data condition assurance and assessment program. Our solution includes standardised assessments and templates you can apply to all your data assets, keeping everyone on the same page and on a path to sustainable data investment.

Curious about assessing data condition at your business? Let's talk.

Data condition is incredibly effective at helping you understand and improve the value of your data, but it’s not as well-known as some other methods. Through our work with clients around the world, we’ve seen data condition transform how companies approach their data. 

We’re excited about the measurable results these efforts have achieved and passionate about continuing this work to help more companies get real value from data. If you have questions or would like to know more about how we can help you improve your data condition, contact us. We would love to talk with you and hear about your business.

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