A Comprehensive Guide To Data Maturity

Gain a practical understanding of what data maturity is, why it matters, and how you can measure yours.

How can you climb the data maturity curve?

How do you do a data maturity assessment?

What is data maturity?

Data maturity is a measure of the reliability, effectiveness, and efficiency of an organisation’s data management. By measuring your maturity, you get a score that represents how well your company manages its data and what you can do to improve.

But your data maturity score is indicative of much more than just your data management. Your maturity score also tells you if your business is, or isn’t, healthy.

Companies with a low data maturity score waste valuable resources finding and correcting low quality data, and risk making poorly-informed decisions based on data they don’t trust. Often, this poor data stems from process and people issues that are embedded within a business.

Meanwhile, organisations with a good data maturity score are characterised by discoverable, understandable data. Data is treated as a valuable strategic asset that can fuel massive growth, and is trusted to inform the most important decisions they make. These organisations are aware of their position on the data maturity curve and how they can further improve their score.

In this article, we break down how data maturity affects the health of your business, the factors that affect your data maturity, and how you can improve your data management to become a high-performing, data mature organisation.

Why does data maturity matter?

1. Data maturity tells you if your data is creating value or driving waste

Data can sometimes be a double-edged sword. On the one hand, it’s an incredibly valuable resource, providing insights and informing decisions. On the other, low-quality data can have the opposite effect, slowing processes and consuming vast amounts of energy and resources.

This means that data can either drive success or damage your business.

While high-quality data represents reliable returns, poor data costs money and creates inefficiencies. How you manage your data determines whether it’s fit for purpose or adds risk to your organisation.

By measuring and assessing your data’s maturity, you’ll be able to develop a comprehensive understanding of whether your data is creating business value, or just costing you money.

🔻 Some of the numbers that highlight how wasteful poorly managed data can be:

$3 trillion

The cost of bad data to the US economy per year, according to research by IBM.

5-7%

Percentage of revenue companies spend on their data. Of which almost 80% goes to waste.

15%

Percentage of time wasted searching for info that well-managed data could readily provide.

2. Data maturity measures the health of your business, not just data

Employing the wrong data management strategy can lead to smaller profits, workplace inefficiencies, and even lost talent. This means low maturity organisations experience a much greater level of risk than their higher maturity counterparts.

🔻 Some of the financial implications for companies whose data issues triggered expensive crises and mishaps:

United Airlines – The US’s premier carrier has suffered massive losses due to data issues on two occasions. In 2008 a data glitch led to the airline accidentally waiving fuel surcharges for a brief period, and again in 2013, a data error caused ticket prices to drop to $5.

Samsung – In 2018, a Samsung data entry employee mistook currency values for shares, paying out 1,000 Samsung Securities shares instead of 1,000 won per share in dividends to workers. This cost the company $300 million.

These examples illustrate how the smallest human errors can seriously hurt profits. The simplest of data management controls could have saved them hundreds of millions.

So, data maturity isn’t just an indicator of how healthy your data is, and how good you are at managing it. It’s primarily a measure of business efficiency and risk. By working with business leaders, data management experts and academics, we’ve determined how data maturity impacts the bottom-line, in the graph below.

Low data maturity translates into higher costs as data is created for a single use. This means organisations repeatably invest in information scrap and rework, instead of ensuring that data can be repurposed in the future for other use cases.

High maturity, on the other hand, means that organisations treat data as a valuable strategic asset. They make intelligent data investment decisions and measure their ROI. This results in savings, which are then re-invested into growth opportunities and value creation.

What are the factors that influence data maturity?

Data is influenced by a myriad of factors. From how a company behaves towards its data, to the tools and methodologies employed to manage it. Each factor plays a role in determining the data maturity of an organisation. Although every business takes a different approach to data management, often their maturity is impacted by the same factors. These can include:

Company culture – An organisation’s culture can greatly impact its data. Teams can either willingly share or hoard data, ignore its value, or treat it as a valuable asset.

Leadership – Leaders play a vital role in reaching and maintaining a high data maturity score. How they speak to the team, communicate the importance of data for the organisation, and how they manage investments in data, are all critical factors.

Skills and capabilities – It’s important to have data-literate staff, as they help your organisation understand the value of organised, up-to-date data and can act as data management advocates.

Data uses – Among others, whether data is shared and repurposed, or siloed and duplicated.

Data analysis – The techniques and methods you use to analyse your data will impact the results and insights you can derive from it.

Data maturity is measured across seven layers

So, how can you know if these factors are impacting your data maturity?

That’s where the 7 layers for data maturity come in.

These layers help you assess your data management strengths and weaknesses across the data lifecycle, from strategic areas like capital allocation, through to tactical ones, like analytics.

They are listed below, in order of how they should be assessed. This means you should consider your score for Data Value and Data Governance before discussing the technical details of Technology Architecture and Data Usage. After all, you can’t have good data without a good strategy and the right investment.

Assessing the below, where do you think you’re data immature? What could you do to improve your maturity in areas of weakness?

1. Value

This level considers the monetary worth of data – data valuation – and explores how your data generates value for your stakeholders. This first phase in measuring data maturity lays the foundation for everything else.

How data valuation improves maturity: Once you understand your data’s inherent value, and potential ROI, you’ll be able to make smarter investment decisions.

What measuring this layer tells you: Do you know your data’s monetary worth and which data assets generate the greatest returns?

Why this layer matters: Without knowing the monetary worth of your data, you can’t invest in the data that creates the most business value, or demonstrate results.

2. Governance

This measures the impact of your organisation’s structure, capital allocation decisions, and operating model on data maturity. Good data governance is key to good data management.

How good data governance improves data maturity: Without protocols to dictate how it should be governed, data quality quickly degrades. From undocumented updates to obstacles to innovation, teams operating in an unstructured environment cannot maximise data’s potential.

What measuring this layer tells you: Are there clear data roles and responsibilities in place to ensure your data investments are effective?

Why this layer matters: Inaccurate data stemming from bad governance can have severe consequences for a company. From flawed business decisions to the risk of data breaches and compliance violations. Effective governance is essential to ensuring data maturity.

3. Architecture

The process of designing models which accurately depict how data flows within an organisation, and how it can be used to meet the business’s needs.

How data architecture improves maturity: The right, high-quality data architecture allows organisations to collect, engineer, and analyse data to create business value, consistently and efficiently.

What measuring this layer tells you: Are you making sure that your data is organised and accessible whenever any business unit needs it?

Why this layer matters: Data architecture dictates the flow of data across the organisation. It helps make data accurate, complete, and available for the right business users and decision-makers. Without data architecture, it’s impossible to ensure data consistency and correctness.

4. General data management

Effective data management depends on how well you’re able to plan, control, and execute the processes and policies designed to enhance data’s value to the organisation.

How data management improves maturity: With the right data management practices, your organisation has access to reference data, systems of record, as well as master data and metadata.

What measuring this layer tells you: Do you have good, whole-lifecycle data management practices, from how data is collected to disposed?

Why this layer matters: It allows users to easily find the data they need via systems of record/metadata.

5. Data quality management

Data quality is what sets the ‘good’ data apart from the ‘bad’. This measure looks at the processes responsible for improving overall data quality and how you can better control and measure its performance.

How data quality improves maturity: Good data quality management means understanding what the data is used for and by whom. It helps with putting the right rules and processes in place to ensure data’s fitness for purpose.

What measuring this layer tells you: Do you understand how data is used across your organisation, and how those uses affect your data quality requirements?

Why this layer matters: Data quality is essential to empowering data to do its job effectively. It allows you to avoid inaccuracies and redundant or obsolete information.

6. Technology architecture

Technology amplifies your ability to manage data, interpret it, and store it accurately. As part of your data’s overall architecture, technology must function smoothly and help you analyse and interpret your data better.

How technology infrastructure improves data maturity: A good tech infrastructure allows you to rapidly collect, store and analyse data, to meet the needs of business users.

What measuring this layer tells you: Does the technology you use make data available across the entire organisation, with a single source of truth?

Why this layer matters: To provide universal access to irrefutable data, these technologies must reflect the organisation’s needs and ambitions.

7. Usage

Once you’ve measured and gauged data maturity across so many levels, you’re now able to put your data to work.

How data usage improves data maturity: When data is used correctly, the benefits are endless. Teams are more confident and able to make better decisions, they have accurate information upon which to base their selections, and data-driven outcomes significantly reduce waste.

What measuring this layer tells you: How usable and available is your data? Also, what is the complexity of the analyses you perform on your data?

Why this layer matters: Organisations that deploy more complex analytics in an ethical and effective manner can ensure that their decisions are accurate and informed. What’s more, they are also capable of accelerating their decision-making processes.

How can you climb the data maturity curve?

Improving data maturity depends on pursuing objectives and benchmarks which reinforce good data management and allow an organisation to climb the data maturity curve.

1.Defining your data

To climb the data maturity curve, you first have to establish your “as-is” state. Where are you starting from? Begin by assessing what data you have and what it is currently used for. Next, seek to evaluate your data’s value and maturity. Where could your data create the most value? Also, where are your health and maturity lacking, i.e., where is your data letting you down?

2. Investing in the data

Next, invest in the data which boasts the lowest maturity scores but provides the most value to the organisation. Identify the root causes of low maturity and invest in sustainable, long-term solutions.

An organisation with higher levels of maturity is capable of dealing with data quality issues as soon as they arise, rather than when they cause problems. Instead of expending resources patching up data problems late in the data cycle, higher maturity means these errors have been spotted and fixed before they affect the user.

3. Managing the data

Organisations that have created governance structures, operating models, policy procedures, and set standards and guidelines around their data are able to ensure its quality throughout its lifecycle.

These structures allow companies to align their efforts around their most important data. By combining these resources and encouraging ownership and accountability based on data performance, teams come to believe in and cherish their data.

Technology can lull organisations into a false sense of data governance security, as they come to rely on it to ensure good practice, until something goes wrong. Data governance depends on people, and the technology is simply there to support them.

After all, computers aren’t as good at recognising value, anticipating strategic potential, or taking accountability as people are.

4. Monitoring your data assets’ investment performance

Once you’re able to measure and invest in your data, you need to closely monitor it. By putting your governance structures to work and keeping tabs on shifts in ROI, organisations can use their data observations to allocate the right capital to the right opportunities.

How do you do a data maturity assessment?

To make informed investment decisions in your data, you need to know your data maturity. This starts with a data maturity assessment. Maturity assessments help verify where an organisation’s data management and strategies are working and where their data practices are letting them down. But some data maturity scores are more helpful than others. Good ones:

Associate data maturity to the strategy and performance of the business. This helps data and business leaders identify if they have unhealthy data assets that are creating risk or excess costs, and focus their investments accordingly;

Demonstrate that data maturity is a company-wide effort. Good assessments compare data maturity across the geographies, business units, and seniorities of an organisation. This helps data and business leaders have informed conversations about what immature business areas can do to improve; and

Provide granular insight across multiple data management disciplines. Knowing your organisation’s maturity score is a 3 out of 5 doesn’t really help you prioritise. You need to be able to understand which data management disciplines are having a detrimental effect on your maturity. For example, whether your immaturity stems from your governance practices, your data strategy, or maybe your analytics capabilities.

Helping you prioritise, being relevant to the business, and providing granular insight are what separates the useful data maturity assessments from the rest.

Next steps

We’re often seeing generic data maturity assessments that don’t help data leaders prioritise their resources or demonstrate business value and impact.

We’ve built our Data Diagnostic to do the opposite, giving data leaders the information they need to invest in immature areas and improve business performance.

We’re regularly assessing the data maturity of our clients to help them achieve just that. If you’re interested, reach out, and learn how we can help you on your path towards better data maturity.