Everybody goes through phases and all, don’t they?
“Look, sir. Don’t worry about me,” I said. “I mean it. I’ll be alright. I’m just going through a phase right now. Everybody goes through phases and all, don’t they?” Indeed, Holden, everybody goes through stages of growth, including organizations. CMMI, ITSM/ITIL, Agile, DevOps, Project management, Capabilities, Knowledge management, Processes, Risks, Cybersecurity, Cloud, Enterprise architecture, Testing, People capabilities, and the list of functions goes on.
The goal of this article is to briefly describe maturity models concepts, give an overview of the Data Management Maturity Model (DMM) and list other data-related maturity models. How does this article differ from other comparison research papers and white papers and why data-related maturity models comparison?
When I was researching data-related maturity models, I realized that in many cases, you are presented with the model outcome description and rarely with the questionnaire itself to do the self-assessment. And then, you have two paths: (1) to develop your own maturity model aligned to the model outcomes or (2) to undertake a subscription-based assessment. If you decide on the former, be prepared that development of a maturity model is not for faint-hearted as there are multiple phases involved, namely scoping, design, population, testing, deployment, and maintenance care [1], not even mention that, in order to complete the populate phase, exploratory research methods such as Delhi technique, Nominal Group technique, case study interview, and focus group need to be employed. If you opt for the latter, the price tag could go as far as thousands and thousands of USD. As such, in this article, I’ve prepared a battery of actual questionnaires — I left those models that require a subscription/consulting services to get access to without a link.
Why data-related and data management maturity models, in particular? DATAVERSITY defines data management as “a comprehensive collection of practices, concepts, procedures, processes, and a wide range of accompanying systems that allow for an organization to gain control of its data resources” [3] or to put it simply data management practices help creating a complete view of all data assets in an organization and managing them throughout the entire data lifecycle. Thereby, the data management maturity models are intended to evaluate a company’s maturity status to address the data management questions, ranging from data governance, data security, metadata management to data quality, and more.
What and Why?
A maturity model is “a set of characteristics, attributes, indicators, or patterns that represent progression and achievement in a particular domain or discipline” [2]. It allows an organization evaluating a company’s ‘as-is’ performance; assessing the gap between the current situation of the company and ‘to-be’ situation; indicating what the next steps are to take to get to the next level of maturity; comparing its performance with peer companies, among others.
How?
In general, maturity models are structured as a series of levels along an evolutionary scale and can be classified as progression models and capability models [2].
The progression models are characterized by a simple progression or scaling of a characteristic, indicator, attribute, or pattern where the maturity levels being labeled relative to a ‘state’ or ‘step’. As such, the objective of the progression models is to provide a roadmap of progression or improvement, expressed as increasingly better versions of the characteristic, indicator, attribute, or pattern. For example, if we are thinking of data, the maturity progression will be:
Data >> Information >> Knowledge
The capability maturity models are described by a number of process maturity levels and some focus areas; thus, such models are aimed to describe the state of organizational maturity relative to process maturity. An often-observed depiction of the maturity phases can be depicted as:
ad hoc >> managed >> defined >> quantitatively managed >> optimized
Data Management Maturity Model (DMM)
Developed by CMMI Institute and released in August 2014, it was based on a three-years research to assess a company in the following areas: Data Management Strategy; Data Governance; Data Quality Platform and Architecture; Data Operations, and Supporting Processes. The model has five levels of maturity with the following characteristics:
Related data-maturity models
- Data management
- Data capability assessment from HESA at https://www.hesa.ac.uk/support/tools/data-capability/full/assess-maturity accessed 12/29/2019
- Data management Maturity Scan from Data Crossroads at https://datacrossroads.nl/dm-maturity-scan/ accessed 12/29/2019
- Gartner’s Enterprise Information Management Maturity Model
- Information Evolution Model from SAS
- Method for an Integrated Knowledge Environment (MIKE 2.0) Information Maturity Model (IMM) at http://mike2.openmethodology.org/wiki/Information_Maturity_QuickScan accessed 12/29/2019
- Data maturity framework by Data Orchard CIC at https://www.dataorchard.org.uk/
- Information Governance strategy from SaP at https://blogs.sap.com/2014/07/09/self-assess-your-capabilities-for-executing-on-an-information-governance-strategy/
2. Data governance
- IBM Data Governance Council maturity model
- Oracle data governance maturity model
- DCAM Maturity assessment from Enterprise Data Management Council
- DataFlux Maturity Model
- Stanford Data Governance Maturity Model at https://fdocuments.in/document/stanford-data-governance-maturity-model.html accessed 12/29/2019
3. Storage:
- Data warehouse maturity assessment from Google Cloud and TDWI at https://dwassessment.ilumivu.com/ accessed 12/29/2019
- TDWI Hadoop readiness from TDWI at https://tdwi.org/pages/assessments/tdwi-hadoop-readiness-assessment.aspx accessed 12/29/2019
4. Data-type specific:
- IoT data readiness from TDWI at https://tdwi.org/pages/assessments/arch-all-iot-data-readiness-assessment.aspx accessed 12/29/2019
5. BI & Analytics:
- Advanced analytics from TDWI at https://tdwi.org/pages/assessments/adv-all-tdwi-advanced-analytics-maturity-model.aspx accessed 12/29/2019
- Self-service analytics from TDWI at https://tdwi.org/pages/assessments/tdwi-self-service-analytics-maturity-model-assessment.aspx accessed 12/29/2019
- Analytics Maturity from rsystems at http://analytics.rsystems.com/analytics-maturity-self-assessment/ accessed 12/29/2019
- Analytics Maturity Model from informs at https://analyticsmaturity.informs.org/ accessed 12/29/2019
- Analytics maturity from latentview at https://www.latentview.com/analytics-maturity/survey/ accessed 12/29/2019
- ITScore for BI and Analytics from Gartner
- PeopleInsight at https://form.jotform.co/70078317158862
Ojo, though! When deciding on the framework to use, keep in mind that the maturity model should also be ‘mature’ and ‘solid’, but what I’ve observed is that many models lack detailed validation / a methodology to justify the framework or are not evidence-based. DAMA, a global community of Data Management Professionals, came up with the following assessment criteria [4]: -Accessibility or easy-to understand; -Comprehensiveness across a wide range of data management practices; -Extensible and flexible or being able to enable enhancement of industry-specific or additional disciplines; -Future progress path built-in or helping to build a roadmap to move to the next level; -Industry-agnostic vs. industry-specific; -Level of abstraction or detail or providing sufficient level of detail to ensure that they can be related to the organization and the work it performs; -Non-prescriptive or giving general directions not suggesting concrete measures; -Organized by topic; -Repeatable; -Technology neutral.
“Look, sir. Don’t worry about me,” I said. “I mean it. I’ll be all right. I’m just going through a phase right now. Everybody goes through phases and all, don’t they?” “I don’t know, boy. I don’t know.” I hate it when somebody answers that way. “Sure. Sure, they do,” I said.
Reference:
[1] De Bruin T., Rosemann M., Freeze R., Kulkarni U. (2005). Understanding the Main Phases of Developing a Maturity Assessment Model.
[2] Caralli, R., Knight, M. and Montgomery, A. (2012) Maturity Models 101: A Primer for Applying Maturity Models to Smart Grid Security, Resilience, and Interoperability.
[3] DATAVERSITY Education’s website — https://www.dataversity.net/what-is-data-management/ accessed 12/29/2019
[4] The DAMA Data Management Body of Knowledge (DAMA-DMBOK). CHAPTER 15- Data Management Maturity Assessment
