Plenty of data management models exist. In this article, you learn the benefits of using the “Orange” data management model.

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 orange and data management (DM) models came to my mind when I wrote 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 company’s perspective as a whole. DAMA-DMBOK promotes this approach. The second viewpoint is narrow: data management is viewed from data management professionals’ perspective 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, and Gartner’s Enterprise Information Management Maturity Model) find themselves somewhere in between, between these two cornerstone approaches.
  2. All the models mentioned above are compatible with each other only to some extent.
  3. If a company wants to measure its data management maturity, the data management model they are using should be aligned with a particular maturity model.
  4. Even though 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’s needs.
  5. All said above makes comparing DM maturities and performance between different companies difficult.

This article aims to show a unified model of data management that can also be used as a basis for a DM maturity model. I want to present to you the key principles that form the model.

Key principles of the ‘Orange’ model
  1. The “Orange” model of data management 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 a company’s ability to reach goals or deliver products. Each business capability can be specified by four components: data/ information, process, tools, and roles.
  3. Data management’s key value proposition is decision-making on different operational levels by delivering data and information. This value proposition focuses on two groups of customers: internal and external.
  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 DM value proposition’s delivery while supporting DM and other capabilities that enable the core capabilities.
  5. Data management is a separate business function that closely collaborates with IT and other business functions.
Key data management business capabilities

In this article, I will only elaborate on the first level of the business capabilities of the “Orange” model of data management, which you can see in  Figure 2 below:

Figure 2. The first level of 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, 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 different business functions and activities 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

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

DM function is responsible for the 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 the creation of data models. Data architecture focuses on designing data flows and (meta)data lineage, including data transformation rules. In practice, separating data architecture and data modeling tasks and deliverables is difficult. 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 data quality requirements, resolving data quality issues, and designing corresponding checks and controls.

The third capability is the 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 different data and information stakeholders’ efforts.

From my practical experience, these three capabilities are most often considered core data management capabilities. I have experienced the DM function being either independent or part of the finance function in my practice. I have seldom seen DM being part of the IT function. Therefore, the ‘Orange’ model of data management capabilities focuses on design and coordination activities. IT functions, in this respect, enable 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 designing and optimizing technological components in which physical data flow and processing occur. These parts of the 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. I believe 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 data processing stages.

Other supporting capabilities

There are, of course, many other supporting capabilities that are implemented by different business functions.

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

Change management and process management are also critical capabilities that empower the data & information value chain’s functioning.

The key areas of usage of the ‘Orange’ model

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

  1. You can use it to develop data management functions within your company.
  2. I have used this model to develop a data management maturity scan. An example of such a scan focusing on business users with finance as the key stakeholders to be found HERE

For more insights, visit the Data Crossroads Academy site: //