What are data maturity models?
Data maturity models are frameworks that help you understand how reliable, efficient, and effective your organisation is at managing data. These models place you on a maturity scale that is broken up into stages, often 1 to 5, and as your organisation improves its data management, you progress to the following stage.
Discovering your stage of data maturity is a crucial first step in improving your organisation’s data. That’s because it identifies where you need to improve and how. It’s like driving a car. If you don’t know where you are and where you’re heading, you can’t figure out how to best get there.
To find out where your company falls on the data maturity model, all you need to do is conduct a data maturity assessment – a process that informs you and other business decision-makers about the state of data and suggests improvements.
This ultimately allows for more effective goal-setting, with targets determined according to both your data maturity right now and the desired stage you want to attain in the future.
Why do we need data maturity models?
A data maturity model helps your company measure its data and business health.
Good data adds value, bad data drains resources and skews decision-making. Data maturity models inform stakeholders whether they’re taking the right approach to their intangible assets – whether their data is helping grow the company, or holding them back from achieving their full potential.
Data maturity models:
- Pinpoint the data health of your organisation
- Unearth how better business processes can improve your data
- Provide insights into how your company can better manage data across its lifecycle
- Help you identify high-risk data areas that need improvement
- Link data maturity to business efficiency
Poor data management poses a threat to your company’s survival, one that’s difficult to see without a data maturity model.
Anmut’s own clients estimate that poor data quality and availability causes at least 16% additional cost per year. And that is hardly a surprise, since our survey with 86 data leaders reveals that organisations that manage data poorly spend four times more money on fixing data, hindering them from being proactive when it comes to generating value from data.
Worse still, these organisations’ competitors are actually pouring twice as many resources into creating value from their data assets, giving them a massive advantage.
Hence, low data maturity is not only expensive but unsustainable–especially when your competitors are investing in improving their data quality.
What does a data maturity model look like?
The Anmut 5-step data maturity model
At Anmut, we’ve created a data maturity model in consultation with data managers, academics, and business leaders. This five-step framework allows us to understand how your company utilises data and map out how operations improve when you begin to climb the ladder.
Stage 1: Short term profit
At this stage, data investments are focussed on short-term profit. This means data quality issues are remedied with quick fixes and sticking plaster solutions, without resolving the underlying people and process problems that have caused them.
This means data quality remains poor for long periods of time. Not much of it is formally owned or managed, nor shared across the organisation. As such, rudimentary data is used for reporting purposes, but it doesn’t influence wider business operations or strategic decision-making.Â
This stage is typical for organisations that are just starting to develop their data strategy.
Stage 2: Risk
While there is a general awareness of the benefits of data governance, roles are poorly understood. Data ownership is not formal and often there is no CDO in place, or if there is, they report to a CIO, CTO, CISO or other more senior position that is not the CEO. Sadly, this lack of senior data ownership means technology investments are often confused with data investments.
Even though the company is undergoing a culture shift and has implemented basic data management policies, they have yet to spread across the organisation. Often there is no enterprise data model, meaning more time is spent on data preparation than analysis, and reporting is predominantly manual: KPIs are siloed, and performance is difficult to measure.
Stage 3: Information managed
At this stage, employees and stakeholders are beginning to understand that data could be an asset to the organisation, but still aren’t sure how to manage it properly. The importance of intangible assets to a company’s market value is becoming clear.
Functioning data governance contributes to a growing level of trust in the data held by the organisation and ensures sensitive information is protected. Data problems are identified and remedied closer to the source instead of relying on quick fixes, but data science proof of concepts and MVPs are being produced that are not scalable and provide little business value. Data lineage is understood, but only partially mapped.
Stage 4: Informed decision making
Data is actively incorporated into decision-making across the entire organisation. Clear data governance enables full transparency of data performance at every level of the organisation. They regard data as the valuable asset it is, at the same actuarial level as property, equipment, and other tangible assets. The company rewards good data management and outcomes at the same level as other employee performance indicators.
On an operational level, the organisation takes a proactive approach to using data. It leverages automated processes to ensure the smooth flow of data as well as user-friendly processes for access and collection. Moreover, the company promotes data literacy and teaches basic data skills to almost all employees, not just those who work directly with the CDO.
Stage 5: Data-driven leadership with trust & transparency
Executives consult data when setting company goals and utilise it to track and measure outcomes, disseminating it promptly to all appropriate parties. Data empowers managers to keep employees informed on matters such as key decisions, goal-setting and performance metrics.
Because of this, employees report high levels of job satisfaction. The ecosystem is thriving, all thanks to data becoming the major driving force behind impactful change within the company. All of the above translate into an increase of up to six times the organisation’s market value.
How to climb the data maturity curve
The data maturity curve is a visualisation of the pathway necessary to achieve a high level of maturity. The better your data maturity, the better your business.
To climb further up the curve, you need to know the right initiatives to pursue to improve data management in your organisation. You need to take steps to acquire the right people with the right skills, as well as assign them to the tasks that make the greatest impact.
1. Define your data
First, you need to identify your data’s current state. What data do you have and what is it used for? Establish your starting point by evaluating your data’s value – data valuation – and maturity. Pinpoint both where data could create the most value in your organisation, and where data health and maturity is letting you down.
For example, are there priority business areas with a low data maturity? Do you know which data assets have the potential to unlock enormous value, if only you took the steps to improve maturity? Find out what you need to fix, and then take steps for improvement.
2. Invest in data
With the root causes of low maturity identified, the next step is to invest in data that boasts the lowest maturity scores but provides the most value to your organisation. Implement sustainable, long-term solutions for data management that suit your company’s specific needs. Remember, a solution isn’t automatically right for you just because it’s trendy or complex.
3. Manage your data
Now you’ll need to introduce organisation-wide governance structures, as well as disciplines for managing data across its lifecycle. As a result, you’ll be able to ensure data remains healthy and high-quality.
By combining these resources and encouraging ownership and accountability based on data performance, your teams will come to believe in and cherish their data. And data governance both starts and depends on people.
4. Monitoring data performance
- Align your data strategy to operational needs. Investing in what the business needs is important. You need to align your data strategy to your operational needs and focus on investing in the data that aligns with the organisations strategy and operational needs. What is the organisation trying to achieve? What data does it need, or where does it need to improve, to make that happen?
- Nurturing internal support. As a CDO, you need a good network. Get the entire company involved in your data maturity project and utilize your established governance structures to keep track of future data investments.Â
- Optimise data investments: To optimize your data investments, it’s crucial to track the return on investment (ROI) and identify which data initiatives are not generating value for your business. By deprioritizing spending on these areas, you can concentrate on what matters most to the organization.
- Manage data up as well as down. Bad data can flow in both directions, and without leaders who emphasize the importance of a data-driven culture, data management may suffer, leading to compromised decision-making. It’s essential to maintain transparency at all levels, particularly at the senior management level, by communicating and helping them understand data investment expenditures.
Next step: take action with a data maturity assessment
- Prioritise: know where your data risks lie by comparing maturity across business units, data assets and geographies.
- Learn: discover granular insights into which data management disciplines you need to invest in.
- Improve: associate data to business efficiency to set and achieve measurable goals.Â