A maturity model can be a great tool to help you assess the effectiveness of a current group or individual. They describe known states of being at various levels for given disciplines. Likewise, they provide examples companies can use to improve their businesses or processes.
To understand how this works, imagine career employees who might wish to revamp their looks for a new job or promotion. They decide to get a haircut and buy specific clothes to dress the part, hoping others will see them as a good fit for this new role. But, how do they identify what haircut to get or which clothes will do the trick?
If they’re lucky, mentors or peers model the professional looks they believe they need. If not, they might seek models elsewhere, like in fashion magazines, store displays, or even by hiring a fashion assistant to help. Whatever model the person finds and uses, the model allows them to make fashion choices to reach their desired look and hopefully achieve their goal of looking the part in the eyes of their peers.
In other words, like we achieve self-actualization through mental and visual projection, a maturity model provides a proposed vision for a business to self-actualize.
Maturity models are simplified representations of an organization’s capability for continuous improvement in a particular discipline. In other words, the model judges how well your company or system is at improving itself from a given state, allowing you to observe a company’s maturity level in either the quality or the use of the resources of the discipline.
In general, maturity models assess qualitative information when examining people and culture, processes and structures, and objects and technology. For instance, in the following data maturity model example (technology), we identify the maturity levels around a company’s data use. Each level describes what a business looks like when it uses data in a specific way.
Overall, using a maturity model as a foundation for improving practices, performance, and processes provides your company the ability to:
- Benchmark internal performance: Benchmarking helps you determine where the organization is at in its improvement journey. You can then set clear objectives for future investments in performance improvement.
- Catalyze performance improvement: Because the model reflects the discipline’s best practices, you can use it to produce action plans to close performance gaps and improve maturity.
- Create and evolve a common language: Maturity models help knowledge domains grow into disciplines where a common language can translate into consistent, repeatable, and predictable performance over time.
In short, a maturity model tells you where you can improve in a given area to achieve a higher maturity level in your business within that area. It can benefit companies pushing for digital transformations because the models help you identify problem areas to reach your business goals.
Maturity models can also tell you what steps to take next when improving your maturity level. This improvement, in turn, helps your business reach new levels of maturity. So the greater the maturity, the greater the chances that events or failures lead to improvements by your organization.
Businesses use maturity models to learn about themselves. For instance, models help companies learn their maturity level and how to improve within specific disciplines by asking questions and developing action plans.
Maturity models also help organizations make better investment decisions. For example, maturity models can generalize progress estimates by determining what resources it will take to move from one level to the next.
Companies use maturity models to generate crude timing estimates, such as determining how long it takes the IT department to implement new SaaS products. Using achieved work as a reference, the model helps structure generalizations, such as analyzing the number of level two to level three shifts completed and noting that each shift took two to three months to reach.
One of the first maturity models developed is the capability maturity model (CMM). It was created to measure software development processes by the Software Engineering Institute (SEI). It was later succeeded by the capability maturity model integrated (CMMI), which was used to develop maturity models like project management maturity models and others.
CMM provided these five core model levels, which virtually any other model could use as a foundation:
- Initial: Beginner stage
- Repeatable: Proficient stage
- Defined: Savvy stage
- Managed: Expert stage
- Optimizing: Mastery stage
Because of its popularity afforded by its ease of adoption and use and its ability to boost productivity and lower costs, modern businesses’ most widely used model is the business process maturity model (BPMM). This model has a few variations, including the agile ISO maturity model (AIMM)—uses agile business process management tools to attain ISO-level standards—and business process management capability framework (BPM-CF).
According to this systematic literature review, the business process orientation maturity model (BPO-MM) and business process management capability framework (BPM-CF) are the most referred to in academic literature.
Although many maturity models for the BPM field exist, their practice use is limited, so the researchers relied on academic literature for data, according to the review. The researchers speculate that practical evaluations of the BPMMs are rare and that the models are created mainly for the descriptive purpose of use. So no single model is both academically accepted and widely applied in practice, and there is no conclusive evidence that one single BPMM is the best.
The nine known BPMMs in academia which are put into practice are:
- Business process management capability framework (BPM-CF)
- Business process maturity model (BPMM-FIS)
- Business process maturity model (BPMM-HR)
- Business process maturity model (BPMM-OMG)
- Business process orientation maturity framework (BPO-MF)
- Business process orientation maturity model (BPO-MM)
- Process and enterprise maturity model (PEMM)
- Process management maturity assessment (PMMA)
- Value-based process maturity model (vPMM)
Businesses might find this study helpful in their attempts to assess existing models and understand BPMM limitations. Likewise, companies looking to adopt a maturity model for process improvement will likely prefer models that have been tried and tested.
Ayca Tarhan, Oktay Turetken, and Hajo A. Reijers, the review’s authors, summarize, “We suggest practitioners to collect data about their BPM improvement efforts, such that the effectiveness of the model they have adapted can be studied.”
Data maturity is the degree to which a company uses its data, often measured in stages. It’s a partnership between IT and the business to expedite using data to make a decision.
For businesses, data maturity looks like analyzing data and looking for insights, asking questions like, “How can we leverage data to discover new insights and innovations?” In other words, a company’s data maturity level indicates its ability to focus on turning ideas into reality.
According to CIO’s article, there are “Four Stages of the Data Maturity Model” with which you gauge a company’s data maturity level. In a later article, Scott Castle of Towards Data Science adds the fifth stage in “Using the 5-Stage Data Maturity Model for Organizational Impact.” We’ve compiled all five levels here:
- Data-aware: Businesses take a manual approach to compile non-standardized reports from various systems to standardize reporting. Their challenges include lacking data and app integration, developing ad-hoc reports, and distrusting those reports.
- Data-proficient: Organizations begin tracking key performance metrics and indications (KPIs) and questioning the data’s quality despite having many databases, an incomplete data warehouse, and no app integration. Their challenges include lacking executive support and not knowing how to handle or use unstructured data.
- Data-savvy: Companies now use data to make crucial decisions for key ambitions, bringing the business-IT partnership up a level with executive support breaking down organizational and data silos. Their challenges include integrating all applications and data sources for better on-demand service, using data as a competing differentiator.
- Data-driven: Business and IT partnerships reach the ultimate stage of data maturity, working together as a cohesive unit. IT integrates all apps and data sources and installs an advanced analytics program. Meanwhile, the organization identifies business processes to embed analytics. Their challenges include scaling the data strategy while reducing costs and maintaining competitive advantages.
- Data-predictive: Data scientists invent or use machine learning, statistical technologies, and predictive capabilities to predict actions, answer business questions, and optimize operations at scale. Organizational challenges include relying on data scientists and predictive technologies to deliver value and invest more time and money into these resources.
For your organization to achieve a high level of data maturity, data must be deeply ingrained and incorporated into all decision-making practices. But what do you do if you’re behind the competitive curve, and how can you “level up” quickly?
Castle writes, “Going from ground zero to significant investments in machine learning may or may not be what’s right for your firm — a good hard look at the maturity curve, however, is the best way to a path forward.” He also says that not every company must have the most advanced data team.
Determining your maturity level requires an honest self-assessment to create a realistic view of your current state. So using the five-stage data maturity model as a mirror, ask key questions to determine at which maturity level your company lies. Here are some examples:
- Do you have different platforms reporting on other business functions?
- Is your system of record siloed across different divisions, or is reporting integrated across your various business tools?
- What percentage of data does your business incorporate into a single source of truth?
- Unattainable as it might be, how well are you moving toward your goal?
- Can you blend model data with raw data from many sources?
- Does the blending of data occur before or after it arrives at the warehouse?
- Does your data comprise all customer journey phases, or does it favor specific areas?
- How many within your company have access to data, and at what levels?
- Does your firm have model management and live-production-environment capabilities?
- Have you deployed machine learning systems, and are they used in your products or analytics?
- Does your analytics workflow include machine learning models?
After answering the questions and knowing where your company lies in the data maturity model, you can chart a path to achieving the next level. You can start this process by identifying the gaps in skills, knowledge, tools, and practices that can get your business there.
Note: It’s important that your company goes through the maturity model step-by-step. Otherwise, it might bypass some fundamental capabilities, potentially limiting the organization from achieving results from advanced analytics. Castle summarizes, “Firms that follow this curve are better positioned for long-term competitive advantage, building the connective tissue that lets an entity operationalize insights for real business value.”
Regardless of what business process or discipline your company aims to improve through maturity models, Blissfully offers a complete IT platform that can help keep you organized and focused while securing every aspect of your technology stack. From empowering team collaboration to defining and executing consistent IT processes, Blissfully furnishes:
- A system of record to replace your spreadsheet
- Improved SaaS operations and SaaS spend optimization
- Efficient employee workflows
- IT automations to save you time and money
- API integration
- And more
To learn more about how Blissfully can help you maximize your use of maturity models, request a demo today.