Data Maturity Assessment is a must for any companies going on a data-driven transformation journey. Just like a body that needs to be checked up annually to make sure everything goes well, your business should also spend some time measuring your organization’s data governance to improve and increase capability in using data better.
Here is a how-to guide to learn data maturity assessment and how to implement it.
Part 1: What Is Data Maturity And How Important It is?
1.1 Data Maturity Definition
Data Maturity measures how well your business leverages data and makes the most out of it. One company is considered a high level of “data maturity” when data is firmly embedded throughout the whole business and fully integrated into the decision-making processes of every department/division.
When we mention “data”, we would like you to consider it in a broader definition. Data includes all the types of information that your business collects, stores, analyses, and uses. It can be specific to an individual like personal details such as ages, gender, contact information, and job history or exist in the form of metadata related to customer data, data extracted from different departments, etc.
The common thing is that all the data collected is essential for your business, which means more data-driven decisions are made. All data resources are made accessible throughout the organization. In fact, in most surveyed organizations (58%), most business decisions are not approved or acted on until supporting data is provided and vetted. Companies that take data seriously like Facebook, Google, Amazon, or Netflix have already proven to take the lead in the market and dominate their industries. What makes them so successful today is how they utilize data to understand customer behaviour better and then make a significant strategy adjustment.
However, that begs the question of whether organizations can use all their data (from customer data to data collection at the edge to machine data, etc.) in concert and effectively. Most organizations likely underuse their data without even knowing it. What is clear is that without a functional data management practice, many organizations will be strategically paralyzed.
– Here are the data management stumbling blocks that companies might face:
- Data challenges: Data is overgrowing; ESG’s research pegs the average organization’s data growth rate at 27% annually. The prevalence of remote work scenarios and the pervasiveness of emerging Internet of Things (IoT) and data-generating edge technologies exasperate organizations’ issues with data silos. Companies cannot utilize siloed data to drive a business outcome. Many organizations find it hard to locate all their data, consolidate it from different sources, and provide the right people with access to it.
- Infrastructure challenges: Not only do remote work scenarios create silos, but they also make organizations’ data footprints more distributed. In this survey, only 7% of respondents reported their data footprint was completely integrated or centralized, and even those respondents likely have a lenient definition for what that means. This trend has been established by numerous initiatives like IoT projects and intelligent industrial manufacturing plants. Organizations must deploy infrastructure at the edge, potentially adding complexity to – their environment to analyse data at the border in real-time.
- People challenges: For many organizations, the elevation of data management to mission-critical importance is a recent development, which means that organizations must adapt. To effectively drive change, organizations need data leaders that can effectively go strategies, evangelize projects, secure funding, and educate the rest of the C-suite on why data management matters. Once these leaders are empowered, the people challenge is not solved. The newly appointed chief data officer must build a team of skilled but scarce data scientists, which is problematic for many organizations.
Have you ever questioned your business, “Where is your organization on their data management journey?”
To answer this question, we suggest it is high time you did a data maturity assessment for your business.
1.2. How Data Maturity benefits your organization
In a nutshell, data maturity is essential. A data mature organization can decrease cost, yet at the same time increase performance and efficiency.
Below is an example of how data maturity could significantly make an impact on the growth in revenues.
In Competing on Analytics, Tom Davenport and Jeanne Harris provide direct arguments for a “significant correlation between higher levels of analytical maturity and robust five-year compound annual revenue rates.”
Besides, they state that “High performers (in terms of profit) were 50 percent more likely to use analytics strategically compared to the overall sample and five times as likely as low performers.”
This plot shows a correlation between the DELTA maturity model and the market capitalization of companies.
Scatter plot comparing companies’ market capitalization and analytics maturity score according to the DELTA model.
As is evident from the plot, companies with higher analytics maturity are more likely to have a higher market capitalization.
The following chart shows the positive correlation between 5- and 10-year operating income and the company’s analytics infrastructure.
Bar chart comparing revenue and analytics maturity score according to the DELTA model.
Analytics maturity significantly influences the organization’s revenue growth and provides new opportunities.
Part 2: Five Stages Of Data Maturity
Analytics maturity models describe the progressive path that analytics take from being just an activity to becoming a critical component of business strategy.
We can break down this path into five key steps:
- No analytics at all. The initial stage of the data analytics maturity model. It may refer to emerging start-ups or companies that sometimes-overlooked analytics processes.
- Descriptive analytics. This stage enables an understanding of the reality and current events through the depiction of data. Descriptive analytics answers the question: “What happened?” Analysts use it to measure the effectiveness of the organization’s efforts.
- Diagnostic analytics. Diagnostic analytics detects relationships between different variables through the analysis of historical data. It answers the question: “Why did it happen?”
- Predictive analytics. This stage is the frontier of advanced analytics. Predictive analysts create detailed forecasts and foresee the outcomes of actions, events, and trends. With predictive analytics, companies can answer the question: “What will happen in the future?” A predictive system helps organizations make informed decisions by analyzing their previous actions.
- Prescriptive analytics. Prescriptive analytics is the top level of analytics that every company should seek. It implements machine learning algorithms to make recommendations on further actions. At this stage, you get the answer to the question: “What actions should be taken?”
The chart below shows data management maturity distribution among companies these days:
As evidenced by the maturity curve, the current state of data management maturity is relatively nascent, with just 8% of organizations surveyed earning the most mature designation, what we refer to as “Data Management Leaders, and nearly 70% of the market falling in either Stage 2 or Stage 3 today.
Part 3: How To Measure Data Maturity – Conducting Data Maturity Assessment.
Typically, companies can measure data maturity through a host of frameworks; business owners and decision makers of your business should take the responsibility of deciding which framework would suit best, based on your own specific needs & purposes.
Every framework conducted is called Data Maturity Assessment Framework. Several data-driven companies have already created their analytics maturity models, some of which could be accepted benchmarks. Some of the most well-known maturity models can be found, as follows:
- Garner’s Maturity Model
- Analytics Maturity Quotient Framework
- Data Analytics Maturity Model (DAMM) by Association Analytics
- DELTA Plus Model
- TDWI Analytics Maturity Model
- SAS Analytics Maturity Scorecard
- Web Analytics Maturity Models
Based on your organization’s ambitions & needs, you could follow one of the aforementioned models to check up on your business’s current level of data maturity!
Part 4: Best Practice #1: Measuring Readiness: Lagging to Leading Framework
Developing a Strategy
A good data strategy should move the organization to a predictive rather than a reactive state. It is essential that “lagging” or “basic” organizations take steps to define their strategy using priorities and that the leadership acknowledges that data analytics are crucial and incorporates these types of projects or initiatives with a timeline and roadmap in their strategic planning. As an organization’s data maturity grows, strategy becomes a critical starting point to begin each year’s corporate data goals and objectives definition process.
An excellent data strategy will also define the vision for a robust and comprehensive data governance model. With the ever-increasing collection of data, like non-transactional social data, often created and accessed outside an enterprise, the organization needs to update and maintain policies and guidelines surrounding the use of its data. Governance of comprehensive data policies and processes should manage compliance, privacy, and security.
Business Intelligence Requirements and Information
Data is not the same as information. Information or intelligence is data that has been transformed, via analytics, into a meaningful and valuable format for users and other systems. Information is content rich and provides visibility into an organization’s operations, finances, competition, and revenue models. Online chatter, blog posts, e-mail blasts, tweets, and LinkedIn “likes” are all examples of data previously not captured in a valid format for analysis. However, this data type can be stored and eventually analyzed with new big data technologies.
Information requirements are not as simple as identifying what kind of data should be analyzed or where to access this data. Conventional analysis documentation such as narratives, bulleted lists and outlines are a good start but are not as effective in engaging business decision makers and subject matter experts in analysis. Therefore, it is crucial to develop model-driven analysis techniques to identify, analyze and define business intelligence requirements. Model-driven requirements are more appealing, reduce the vagueness, and visually represent requirements clearly, concisely and unambiguously.
Key activities and considerations in defining and prioritizing requirements are:
- Document current technology environment and business applications in use that may impact potential new technology requirements
- Address currents data issues and problems
- Determine owners of and the accessibility to data
- Identify big data resource requirements
- Ensure that needs align with the defined data strategy
Another critical aspect of defining these requirements is understanding and documenting data availability, accessibility and accuracy. For example, specific data must be available in real-time or meet minimum acceptable levels of accessibility. Business decision makers need the capability to access information in a usable format instead of querying data scientists and long lead times for responses.
It is essential to have clearly defined user groups and systems. Not all users are power users, nor do all business users need access to all data sources. An organization should document a process by which the correct user groups have the appropriate level of access and receive adequate training. The systems and tools should align governance requirements to the overall data strategy and IT’s governance and security policies.
Capabilities: Current and Future Data Analytics and Management
After determining maturity levels in the lagging to the leading framework, defining a strategy, and gathering requirements, an organization can begin to plan its future state. The methodology and approach to measuring their capability are to perform a fit-gap analysis. The organization should understand and document current data analytics and management capabilities and the requirements of the data strategy (future state). After these current capabilities are identified, a comparison between the present and desired shapes can be drawn. The following gap assessment should document functional and technological gaps and the activities needed to bridge these gaps. After the capabilities assessment, organizations should also have a clear understanding of the range and depth of skills available to them internally, externally and in combination, and should be able to identify the training and skill acquisition required to progress. The deliverables for this process include:
- Requirements developed in the previous section
- Solution Fit Gap Analysis
- Mitigation Approach
Building the desired future state must also encompass building employee skills to match future needs. As displayed in the L2L framework, leading technologies or processes alone will not move the organization to an entire leading state. People are an integral part of a successful transformation. The organization should define and develop a roles and responsibilities matrix and/or RACI model (Responsible, Accountable, Consulted, and Informed) to document data management, analytics participation and accountability across functional and departmental teams. After that, they have to identify and inventory the skill sets of current data workers supporting data collection, analytics and applicable technologies.
A well-defined data analytics strategy often drives core operational changes, re-aligning data collection, tools and technology changes, and data management updates. To manage these changes, companies need to implement a centralized data governance model, empowered to drive common data standards, definitions and usage is the key. Data governance encompasses the people, processes, and technology required to create consistent and proper data handling across the enterprise. Data governance should serve as quality control for assessing, managing, using, improving, and protecting organizational information. Leading practices in data governance are to establish data definitions and maintain consistency and accuracy across all systems. The goal is to improve data integrity and remediate inconsistencies in data usage. The organization needs to promote data literacy through education and data-driven process improvement. Thus, the organization can ensure data security, availability, and accessibility according to regulations and policies.
Implementing the Strategy
Implementing the strategy may require new technology, human capital development, organizational re-alignment and data management change. The information obtained from the capabilities assessment and the subsequent gap analysis will help create a comprehensive plan to evolve your organization from its current maturity level to the next one. Implementing a successful strategy is an iterative approach and begins with successful change management to prepare for big data transformations. These change management activities should be conducted before any implementation.
- Identify potential areas of resistance, opportunities, and project risks
- Identify and leverage sites ready for change
- Establish a baseline for future change readiness assessments
- Prioritization of change management actions and initiatives
- Promotion of change adoption and acceptance
- Establishment of a quantitative mechanism for ongoing monitoring and measurement
For each initiative developed in support of the organization’s strategy, you should identify the key stakeholders and should be able to define the critical project success criteria. Stakeholders should be categorized by role, prioritized by their A&PA value, and included in a sequential and progressive road map. A Stakeholder Analysis is the foundation for all change management activities and plays a vital role in a successful implementation. Identifying stakeholders and engaging them appropriately throughout the change process will enable a big data strategy to:
- Outline the individuals and groups that will need to be engaged, directly or indirectly, throughout the lifecycle of the project
- Help the right people receive the correct information, at the right time, in the right way.
- Proactively manage the pace and amount of change that each stakeholder must undergo to avoid “change overload.”
- Build organizational buy-in, commitment and capability for change
Results of the Stakeholder analyses will be used as the foundation for the following deliverables: Change Impact Assessment & Mitigation Plan, Communications Strategy & Plan, and the Training Strategy & Plan. The Change Impact Assessment will identify critical changes resulting from the new data strategy and will look at ways to mitigate potential impacts. As the organization deploys its big data initiative stakeholders will want to ensure their readiness and that the solution addresses their original business questions.
Communications will be managed through a comprehensive Communications Plan to develop awareness, understanding, acceptance, and commitment to big data and workflow changes. The Training Strategy and Plan will provide an overview of the direction, goals, and objectives for end-user learning. It will also incorporate a detailed plan to lay out the approach to execute the strategy. The organization must have a scalable end-user training strategy and solution to increase user adoption of the underlying business processes built into the platform. The plan should gauge the readiness ensuring data-literate users are adhering to business objectives and policies.
Effective change management techniques drive successful implementation and adoption. These process changes should work to develop the ability to mobilize data from across the enterprise, probe that data intensely, understand its value, and prioritize the data, all with a strict governance discipline to maintain its prevalence within the organization. The chief impact of big data is its effect on how decisions are made and who makes them. When information is lacking, unavailable, or too expensive to purchase, organizations should fill the gap with advantageously positioned people making decisions based on their experience and interpretations. Forming an analytics team is difficult for organizations that lack the in-house knowledge, experience, and/or resources for training. Without data experienced and skilled workers, it can be problematic for an organization to establish the necessary capabilities internally. The cost of setting up an analytics team could make outsourcing an appealing option to achieve an advantage over competitors.
The organization should focus on the problem they must solve and what drives their business. To be data-driven, they must migrate from relying on intuitions and instinct. The organization must stop thinking they are more data-driven than they are. Without the proper strategy, approach, and governance, it is easy to mistake correlation for causation and find misleading patterns in the data. To create and foster a productive work culture around A&PA leaders and managers should gauge the effectiveness of current policies in depth by investigating data sources, their people and how they utilize analytics in their workflow to realize how policies impact efficiency. The organization should establish transparency to drive productivity. By increasing transparency, managers can focus on merging integration across teams, thus allowing insights to drive evidence-based decisions to integrate teams and meet revenue targets. The organizational transformation can be challenging, but the developments, both in the technology and business payoff, are considerable.
Developing, testing and maintaining A&PA requires strong domain experience and business knowledge. The organization should optimize knowledge transfer (from IT, for example) with a centre of excellence (CoE) to share solution knowledge, plan artifacts and ensure oversight for projects can help minimize mistakes. User data requirements and interactions will vary across the enterprise. Consider the user or system’s need to present the right content to the right user at the right time. The users must have proper training and support. The ultimate goal is for the organization to breed and foster a data-driven culture with a high degree of collaboration across departments. An analytics team should include experienced data scientists and statisticians. A mix of technical and business skills, whether from a single person or members of a tightly aligned team, is the key to producing the most successful results.
Many vendors offer diverse data collection, cleansing, storage, security, performance, analytics and reporting technologies. Determining which technology or combination of technologies will best support your organization’s data needs can be a challenge. Companies should approach tool purchase & development, the suite of tools or personal tools and interfaces in a way that will meet your business’s needs and align back to your goals. Once the organization has determined the needed data to fill in data gaps observed during the initial assessment, the organization should choose the best sourcing strategy for analytics generation. This can be done either in-house or by purchasing the data.
Once the organization is ready, a deployment plan describes how the big data strategy and/ or tool will be installed and transitioned to an operational system. It should contain an overview of the system, a brief description of the major tasks involved in the deployment, the overall resources needed to support the deployment effort, and any site-specific deployment requirements. The implementation will transition people, processes, and technology from the current state to the new environment. Companies should measure the results of the transition against the business case objectives.
The organization must be able to measure success based on the defined acceptance criteria created at the beginning of this transformative endeavour. The success of any data analytics or transformation initiative depends on the complete adoption and transition of the data users and systems. Measuring and re-measuring will be required to ensure that newly introduced processes and technologies have been fully adopted and that these processes and technologies are consistently meeting the needs of the business.
Critical success factors should be agreed upon during the project’s onset while developing the data strategy. They are the outcomes necessary to reach A&PA goals and attain expected benefits. The following are typical performance measurements for an A&PA initiative:
- Recommended Tools adoption
- Verified and agreed upon requirements
- Agreed upon timeline
- Access to appropriate resources
- Decided upon the hypothesis that aligns with business goals and objectives
To become more data-mature, the organization should employ its data analytics strategy in parallel with its big data strategy to validate new findings and insights against traditional methods. The organization should use defined business goals, create hypotheses, identify problems and opportunities, then use analytics to test and refine assumptions and create a feedback loop. In the iterative approach, they should continue to refine their tools and methods to perform technology configuration, customization, and/or integration based on business and technical requirements. The pertinent test is that of utility in improving management practice.
Part 5: Best Practice #2: Garner Data Maturity Framework
Gartner, Inc is the US’s leading technological research & consulting firm.
Cre: Gartner’s analytics maturity model
In a recent survey, Gartner showed that 87,5% of respondents had insufficient data and analytics, falling into “basic” or “opportunistic” categories.
Companies with the essential data maturity level typically rely on spreadsheet-based analyses and personal data extracts. At the same time, those in the opportunistic category might have different departments pursuing their data analytics initiatives, yet there is no connection among these departments. It is independent of each other.
Often, this stems from limited budgets, a lack of visions and skills to explement, or even a lack of resources to invest in infrastructure and human resources for planning and development.
Melody Chien, Senior Director Analyst at Gartner suggested that leveraging data analytics to modernize and go digital must first start with data maturity improvement by taking simple steps in the internal areas of business: Strategy, People, Governance, and Technology. This is also a significant point to look forward to in the Garner maturity model, which looks at enterprise information management (EIM) as a whole.
As said, “A good data and analytics strategy starts with a clear vision”, in this context, vision can be defined as the value that business owners expect to gain from data analytics activities.
When the vision is well set, you need to coordinate with IT and leaders of departments/divisions to develop a holistic BI strategy. Then, be ready to break the long-term goals into short-term roadmaps following the SMART framework – with achievable goals, clear milestones, and metrics to measure and monitor. Most importantly, you must ensure that every related party joining the data maturity assessment thoroughly understands and is aware of mutual strategies.
People with specific roles in the data maturity assessment process take much responsibility for supporting the work to pursue the strategy. In case you have limited capabilities in-house, strive for any outsourcing model that can benefit your business.
Data Governance is crucial to enable your business to balance opportunities and risks in the digital environment. Companies must set rules if your company would like to win this game. To have a simplified data governance program, you might start by:
- Create an inventory of your information assets, where they are located, who uses them, and who takes responsibility for managing and keeping them.
- Establish a framework for working with the data.
Low-maturity organizations often have BI platforms that are more traditional and reporting-centric, embedded in ERP systems, or they may use simple yet disparate reporting tools, limiting users and the number of reports to extract.
For data maturity improvement, don’t hesitate to create integrated analytics platforms that extend your current infrastructure to include more modern technologies.
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