A practical and pragmatic approach to implementation of data management that delivers quick wins is one of the key challenges of any data management professional. Sooner or later, you will deal with this at one point in your career.

In the series of presentations Practical implementation or optimization of data management with the “Orange” model, I share with you my practical experience of the past 10 years. This experience has led me to developing a new model and practical method for implementation and optimization of data management. This method is a collection of techniques and templates that can be used for performing various tasks related to the development and optimization of data management in your company.

The “Orange” model of data management – an overview

 

 

You are probably thinking: there are plenty of different data management and data governance models and industry reference guides. Why would you develop a new model in the first place? The key reasons I have explained in detail in my first article about the “Orange” model of data management and the book where I provide an overview of this model. In short, the three key reasons were:

  1. There are several well-known and frequently used industry reference guides and maturity models and they have fundamental conceptual differences and metamodels of data management/governance.
  2. These differences lead to unaligned data management concepts, terms, definitions.
  3. They all are not sufficiently focused on the practical implementation of data management.

In this article, I would like to give a brief overview of the “Orange” model, addressing:

  • key principles and components that constitute the model
  • the main areas of application of this model.

Key principles

The principles are the “rules” that were put in the design of the “Orange” model.

Principle 1. The key value proposition of data management is enabling transformation of data into meaningful information.

This principle explains why a company needs data management at all with the help of the ‘Data value creation cycle’ shown in Figure 1.

orange model of data management

Figure 1. Data value creation cycle

One of the key value propositions of data management to internal stakeholders is to support decision-making. Information is required for making decisions at any organizational level. To get the required information, you need to first acquire corresponding raw data. Data and information value chain (data chain) ensure the transformation of raw data into meaningful information. Business processes enable the functioning of the data chain. Technology supports the performance of business processes and data processing.

Principle 2. Data management is a business capability.

Business capability means the ability of a company to reach business goals or deliver expected outcomes. I have used the modelof business capabilities developed by The Open Group. This principle assists you to design and implement data management. There are four dimensions that constitute data management (sub)-capability: business process, role, data, and tools (see Figure 2).

Figure 2. Four dimensions constitute data management capability

Principle 3. Data management delivers its key value proposition through a data chain enabled by a set of sub- capabilities.

It stresses the data flows through the company and the structure of data management frame should be based on it. The key components of data management capabilities you can see in Figure 3.

Figure 3. The key components of the “Orange” model

The model includes a model of data chain and the set of enabling capabilities. Data chain is a set of actions that transform raw data into meaningful information. Data chain includes four common phases:

  1. Specification of business stakeholders and their requirements for data processing and transformation
  2. Design or optimization of the data chain
  3. Performance of data processing
  4. Data usage.

Of course, these four phases are at a high-level of abstraction. In reality, each company should have many different data chains. Every chain should be described and optimized at a lower level of detail.

Data management professionals perform the tasks of phases 1 and 2 in collaboration with business stakeholders. The following common data management sub-capabilities enable the execution of phases 1 and 2:

  • data management framework
  • data modeling
  • information systems architecture
  • data quality.

All these data management capabilities are marked in orange in Figure 3.

IT professionals (in collaboration with data management professionals and business stakeholders) usually execute phases 3 and 4. To enable the performance of these phases such capabilities as application and technology architecture, data lifecycle and infrastructure management should be in place. In Figure 3, these are marked in dark grey.

There are also some other capabilities that do not directly relate to data management but still are required to enable the data chain. They are marked in light grey.

You can clearly see in which phase of the data chain which data management and other sub-capabilities are required.

Principle 4. The implementation of data management follows the logic of documentation of data and information value chain.

This is the core insight of my practical experience. The whole “Orange” model is based on this idea. You can read more about this principle in this article.

Principle 5. Data management capability should be set up as an independent data management function.

In practice, you often see that data management belong to either the IT function or to Finance. In my opinion, the key role of data management is coordination of efforts of all data stakeholders, including IT and Finance. Therefore, I always support the idea that data management should be implemented as an independent function, similar to audit.

Principle 6. The “Orange” model is not specific to any industry or company size.

It means that it can be used by any company, regardless of its size and industry.

Main areas of application

There are six key areas of application. I will share with you these areas in a logical order.

  1. Develop data strategy.

Every company needs to align its data management initiative with the business, finance, IT strategy, etc. If your company has no data management function yet, I will not recommend starting the development of a formal strategy document. It is a time- and resource-intensive process. I know a lot of companies that have a data management function in place and yet no strategy document in place. There are some other simple ways to align your data management initiative. For example, you can align the data management initiative with a business strategy by defining business drivers for your initiative. Alignment between IT and data and data management principles ensures alignment between IT and Data management. The data strategy will allow you to specify the scope of data management capabilities that fit the needs and resources of your company.

  1. Perform Data management maturity assessment.

Every company does data management to some extent, either in formal or informal form. Therefore, before starting a data management initiative, it is important to assess what is already available in the company. Maturity assessment also assists in setting up a required level of maturity and asses the gaps between ‘as is’ and ‘to be’ situation. Maturity assessment should result in the development of a roadmap. Data Crossroads offers a free-of-charge anonymous Data Management Maturity scan that, in 10 minutes, will help you get an overview of the level of data management maturity in your company.

  1. Implement the data management function from scratch.

Data Crossroads has developed a practical methodology – the Data Management Star. This methodology is a set of techniques and templates that ensures a practical and pragmatic implementation of data management that fits the company’s needs and resources.

  1. Optimize a particular sub-capability

Your company may need to optimize or develop a new data management sub-capability. The four-dimensional model of data management sub-capabilities will allow you to do that.

  1. Design and implement data management processes and roles

One of the most challenging tasks that a lot of companies face is the development of data management processes, tasks, attributes, and roles. Using the four-dimensional model of a sub-capability you will succeed in developing the set of processes and roles that match your company’s situation.

  1. Set up KPIs and assess data management performance

If your company has already achieved the desired ‘to-be’ maturity level, the task of monitoring its performance is the next on the list. The “Orange” model will assist you in developing a system of KPIs to monitor performance. There are also some additional techniques available to evaluate performance, effectiveness, revenue/cost contribution, and criticality of each data management sub-capability.

If you are interested to know more about the “Orange” model, we invite you to watch our video presentation HERE.

In the six following articles I will work out each of these application areas in details one by one.