In the past, many companies were content to receive monthly reports for management on financial and sales information. In essence, many analytics infrastructures still date back to this time. These were constantly expanded to meet new requirements, such as advanced and predictive analytics, self-service analytics in the specialist areas or, more recently, the use of AI, driven by the hype surrounding ChatGPT.
New functions and components were simply added to these old infrastructures to meet new business requirements quickly and reactively. Today’s expectation to also use unstructured data or information from sensors and external sources has not simplified the situation.
Although the current infrastructure meets all requirements thanks to the extensions, operation and further development have become inefficient and gaps are difficult to identify.
The Data Maturity Assessment, i.e. a structured assessment of a company’s ability to use data effectively, provides a comprehensive analysis of current capabilities and identifies areas for improvement. Through this assessment, companies can optimize their data strategies, increase process efficiency and ultimately gain a competitive advantage.
The BARC Maturity Model
There is a whole range of data maturity assessment frameworks that make it possible to examine and evaluate the various aspects of a company’s data maturity. The BARC maturity model is one of them. Developed from many years of project experience and considering the results of studies, the BARC maturity model, like other frameworks, is divided into different levels that represent the development status of a company around data and analytics:
1. individual: application and methods are based on individual initiatives (local heroes). There are no formal (written) rules, central coordination or measurement of success. Legal and operational requirements cannot usually be met at this level.
2. repeatable: Initial methods, procedures or instruments are available and repeatable. Their use is generally accepted and established. In some cases, they are only in the process of being developed and are not yet coordinated and incomplete.
An integrated view and overarching control are missing. Legal and operational requirements are only incompletely fulfilled.
3. solid foundation: the use of methods and tools is controlled centrally and is part of an integrated view. Roles, tasks and responsibilities are defined. The focus is on accomplishing daily tasks in compliance with legal and operational requirements.
4. excellent: processes, procedures and methods take risk aspects from different scenarios into account. Risks are consciously identified and dealt with based on their probability of occurrence and impact. The entire company is therefore much more robust in meeting legal and operational requirements, even in special situations, and ensuring stability and quality.
5. best in class: methods and processes have become a natural part of the corporate culture. Continuous improvement and further development is seen as a competitive advantage, through cost efficiency, perceptible quality improvement or ongoing improvement of products and services for customers.
What perspectives should you look at?
A structured assessment for data and analytics starts with the current and expected future requirements of the business and the derived goals of the corporate strategy. Technology and tools are only a means to an end. A complete assessment considers issues from the following perspectives:
- Business management: to what extent are standardized key figure definitions available? What use cases are there? Are there defined role models and what is the significance of self-service analytics?
- Data architecture: Is there an overview of the available data and its quality? Is the data provided appropriately for different analytics requirements, e.g. quality-controlled provision in time slices (classic analytics), operative analytics or Data Science Labs? Is there data governance and are regulatory requirements known and implemented?
- Technology: Does the chosen architecture support the requirements of the business and the corporate strategy in an appropriate manner? Is there a defined tool portfolio and a platform strategy? How high is the degree of automation? Are there any media discontinuities?
- Organization & processes: Is there a defined service portfolio? What form of organization is used to support analytics, including self-service advisory? Is there a defined procedure and a method framework? Are the required skills available? How is contract management regulated and invoiced?
- Strategy & culture: To what extent is a data-driven corporate and decision-making culture achieved? How is data literacy promoted?
Benefits of the Maturity Assessment
The Data Analytics Maturity Assessment offers several advantages:
- Clear Current State Understanding: Companies can clearly recognize their current status in terms of data and analytics.
- Alignment between Business and IT: Clarifying responsibilities to support business processes.
- Performance Documentation: Enables documenting progress and success to the company leadership.
- Guideline for Development: The assessment aids in prioritizing actions and creating a roadmap, especially valuable in a rapidly changing (VUCA) environment.
Conducting a Data Analytics Maturity Assessment is thus a crucial step for companies to optimize their data strategy and position themselves successfully in the digital economy.
