Did you know that an orange has not been created by nature, but by humans? This delicious sweet juicy fruit is a hybrid of two other fruits: a pomelo and a mandarin The analogy of an orange and data management (DM) models came to my mind when I was writing about my analysis of well-known data management metamodels and different maturity models of data management. The orange symbolized my attempts to cross the ‘pomelos’ of DM metamodels and ‘mandarins’ of DM maturity models and create a new flavor as a result.

I will spare you the details of the long and hard analytical process, and will go straight into some of my conclusions:

  1. There are at least two cornerstone viewpoints on data management. The first one is broad: it views data processing (data lifecycle) from the perspective of the company as a whole. DAMA-DMBOK promotes this approach. The second viewpoint is narrow: data management is viewed from the perspective of data management professionals and the tasks they need to accomplish. This perspective is presented in DCAM. The rest of the models (CMMI CERT-RMM (Data Management Maturity Model by CMMI), the IBM Data Governance Council Maturity model, Stanford Data Governance Maturity Model, Gartner’s Enterprise Information Management Maturity Model) find themselves somewhere in between, between these two cornerstone approaches.
  2. All of the models mentioned above are compatible with each other only to some extent.
  3. If a company wants to measure its data management maturity, first, the data management model they are using should be aligned with a certain maturity model.
  4. Despite the fact that there are several well-known meta- and maturity models of data management, each company still needs to ‘invent the wheel’, as whatever model is chosen to be used, needs to be adjusted to the company needs.
  5. All said above makes it very difficult to compare DM maturities and performance between different companies.

The goal of this article is to show a unified model of data management that can also be used as a basis for a DM maturity model. I would like to present you the key principles that form the model.

Key principles of the ‘Orange’ model
  1. The model is based on the commonalities between the key industry models that can be summarized in 7 key components: data governance, data, data quality, data and system design, systems and technology, data security, and other supporting capabilities as shown in Figure 1:

    Figure 1. Common data management subject areas.

  2. The model is based on the concept of business capabilities developed by the Open Group. By ‘business capability’ I mean the ability of a company to reach goals or deliver products. Each business capability can be specified by four components: data/ information, process, tools, and roles.
  3. The key value proposition of data management is support of decision-making on different operational levels through delivery data and information. This value proposition is focused at two groups of customers: the internal and external ones.
  4. Data management capabilities on the first level can be split into two categories: the core and the supporting capabilities.
    The core DM capabilities ensure the delivery of the DM value proposition, while supporting DM and other capabilities enable the core capabilities.
  5. Data management is a separate business function that operates in close collaboration with IT and other business functions.
Key data management business capabilities

In this article I will only elaborate on the first level of DM business capabilities which you can see in  Figure 2 below:

Figure 2. The first level of the data management business capabilities.

Core DM capability

To promote and support effective decision-making, data management is accountable for the organization and optimization of the data & information value chain capability, which is a set of internal activities a company engages in when transforming data into meaningful information. These activities focus on adding value to data through the whole chain of its transformation from the source to the end users. Data management plans and coordinates activities of different business functions to establish and optimize the data & information value chain. There are several capabilities that data management should develop to enable the core capability.

Supporting DM capabilities

There are three key supporting capabilities that are required to enable the core capabilities: data architecture and modeling, data quality, and data management framework.

DM function is responsible for definition of data and information needs. This is done by establishing data architecture and modeling capability. Data modeling focuses on two key activities: data classification (i.e. reference, master, transactional and metadata) and creation of data models. Data architecture focuses on designing data flows and (meta)data lineage, including data transformation rules. In practice, it is difficult to separate data architecture and data modeling tasks and deliverables. That is why I presented them as one capability.

The second focus is on the data quality capability. Decision making can only be effective when based on information of required quality. Data quality ensures gathering of data quality requirements, resolution of data quality issues and designing corresponding checks and controls.

The third capability is data management framework. I prefer using this term instead of data governance, as it better expresses the coordinating role of data management within a company. A data management framework provides policies, processes, and roles that allow data management to coordinate the efforts of different data- and information stakeholders.

From my practical experience, these three capabilities are most often considered as core data management capabilities. In my practice, I have experienced the DM function being either an independent function or a part of the finance function. I have almost never seen DM being part of the IT function. Therefore, in the ‘Orange’ model DM capabilities focus on design and coordination activities. IT function in this respect enables the physical implementation of the data & information flow.

Supporting IT capabilities

There are two key supporting IT capabilities that are included in the model.

Application and technology architecture is responsible for the design and optimization of technological components in which physical data flow and processing takes place. These parts of architecture are extensively discussed in TOGAF, the Open Group guide for Enterprise architecture.

The Data life cycle capability ensures the whole operational cycle around data flow, processing and transformation. On my opinion, DAMA-DMBOK 2 has perfectly specified the sub-capabilities: data storage and operation, data integration and interoperability, and DWH & BI. These three sub-capabilities (or capabilities of the 2nd level) fully cover the main stages of data processing.

Other supporting capabilities

There are, of course, a lot of other supporting capabilities that are realized by different business functions.

One of the most important is Data/Information security. You can fairly argue whether Data/Information security is really an IT related capability or it should be considered as a separate business function. The key argument to put it outside IT accountability is that data/information security is applicable for both structured and unstructured data.

Change management and process management are also very important capabilities that empower functioning of the data & information value chain.

Key area of usage of the ‘Orange’ model

There are two key areas where you can apply the ‘Orange’ model:

  1. You can use it for development of data management function within your company.
  2. I have used this model to develop a data management maturity scan. An example of such a scan with focus on business users with finance as the one of the key stakeholders, you can find HERE.