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THE “ORANGE” MODEL

OF DATA MANAGEMENT

IRINA STEENBEEK

THE "ORANGE" MODEL

OF DATA MANAGEMENT

Copyright © 2019 Data Crossroads. All right reserved.

Published by Data Crossroads.www.datacrossroads.nl

First edition, 2019.

All rights reserved. This book or any portion thereof may not be reproduced or used in any manner whatsoever without the express written permission of the publisher except for the

use of brief quotations in a book review.

Book design by Natalia Zhuravska.

ISBN: 9781701504745

Introduction 5

Overview of the model 7

Key principles of the "Orange" Model 7

Areas of application 9

Key components 11

1. An overview of the role of data and information in the business lifecycle 11

2. A standard metamodel of data management 12

3. Stakeholder – data management value proposition matrix 13

4. A standard data and information value chain model 14

5. A standard set of data management capabilities 15

6. A detailed model of data management capability 16

7. Data & information value chain – data management capabilities matrix 16

8. Data management maturity assessment techniques 18

Summary 20

References 22

List of figures 23

TABLE OF CONTENTS

INTRODUCTION

7

Almost every data management professional, at some point in their career, has come across the fol-lowing crucial questions:

1. Which industry reference model should I use for the implementation of data management functions?

2. What are the key data management capabilities that are feasible and applicable to my compa-ny?

3. How do I measure the maturity of the data management functions and compare that with those of my peers in the industry?

4. What are the critical, logical steps in the implementation of data management?

On the way to finding answers to these questions, many professionals face several challenges.

Challenge 1: There are several well-known and adopted industry reference guides and they have fundamental conceptual differences.

The primary data management/governance reference industry guides are DAMA-DMBOK by the DAMA International1 and DCAM by the EDM Council2. The third industry guide, TOGAF by The Open Group3, focuses on Enterprise Architecture, but it also covers some data-management-related areas. The fundamental conceptual differences are:

• the perspective on data management;DAMA-DMBOK stands for broad perspective and takes a look at data management from the enterprise viewpoint regarding the lifecycle of data circulating within a company.DCAM chooses the narrow perspective and scopes data management from the viewpoint of the tasks to be performed by data management professionals.

• the role of the IT function;DAMA-DMBOK considers data management as a part of IT, while DCAM separates data man-agement from IT by recognizing this function as a part of a collaborative ecosystem.

• the building blocks of data management metamodels;DCAM describes data management as a set of business capabilities, while DAMA-DMBOK de-fines data management as a business function and describes it using a set of Knowledge reas. Unfortunately, neither of the guides provide a clear definition of a ‘business function’ or a ‘ca-pability’.

• the scope of data management functions;The list of data management capabilities and functions are similar only to some extent.

Challenge 2: There are differences in data management terminology, definitions, and the content of data management business capabilities/functions described in the primary industry guides.

The list of the business functions and capabilities differ between different guides. Furthermore, the capabilities/functions that are identical by name often have different meanings and deliverables.4

Challenge 3: The primary industry guides offer knowledge on data-management-related subjects but don’t provide a comprehensive method regarding the practical implementation of a data man-

agement framework.

The development of the practical approach to implementing data management remains a challenge for each company. The industry reference guides mentioned above offer information about “best prac-tices”, but not the practical approach. Such a challenge always means that each company needs to spend resources and time “‘reinventing the wheel.”

Challenge 4: There are several data management/governance maturity models available, but these models and obtained results can be hardly compared.

The most well-known data management/governance maturity models are: DAMA-DMBOK,5 DCAM,6 CMMI CERT-RMM (Data Management Maturity Model) by CMMI,7 IBM Data Governance Council Ma-turity model,8 Stanford Data Governance Maturity Model,9 Gartner’s Enterprise Information Manage-ment Maturity Model.10 There are a few examples of the differences among these models:11

• the number and name of the maturity levels

• the number of maturity levels varies between 5 and 6 points

• name, amount, and type of subject domains. For example, DAMA-DMBOK operates via the data management functions described by the Knowledge Area. DCAM applies a business capa-bility concept. CMMI and Stanford models take a process as a basis.

• content of domains.One of the most prominent examples is the difference between the data governance subject domain in DAMA-DMBOK and DCAM viewpoints.

Challenge 5: The metamodels of a maturity model and a data management model should be aligned, but it is not a case with the major models.

Only DAMA-DMBOK and DCAM are consistent in respect to the identity of their metamodels used for data management setup and the maturity assessment. The rest of the data management/governance (maturity) models use metamodels that can hardly be compared with each other and with the models of the primary industry reference guides.12

To achieve trustful results while measuring maturity, the metamodels of data management models and the data management maturity models should be compatible.

Challenge 6: The situation with maturity models hardly allows for one of the key goals of the matu-rity assessment to be reached: creating benchmarks for comparison between different companies.

In the situation when companies battle on their own with the development and implementation of a data management framework, they might want to compare their achievements with peers in the industry. The situation with multiple and not compatible maturity models makes the task impossible.

The “Orange” model described throughout the document strives to mitigate the challenges mentioned above and focuses on the needs of medium-sized companies.

8

OVERVIEW OF THE MODEL

This model got its name because of the famous fruit: the orange. Few people know that an orange is actually a hybrid between a pomelo and a mandarin.13 This analogy inspired the name of the model, as it perfectly symbolized the attempt to cross the “pomelos” of data management metamodels with the “mandarins” of data management maturity metamodels.

Each company can adapt the “Orange” data management (maturity) metamodel to the company-spe-cific structure, culture, and resources. The tool is designed for data management professionals and consultants.

The “Orange” model is based on several core principles.

KEY PRINCIPLES OF THE “ORANGE” MODEL

PRINCIPLE 1: Data management is a business capability.

The key purposes and outcomes of the capability are:

• to be in control of data and information resources

• to create and utilize value of data and information resources.

PRINCIPLE 2: Data management delivers its key value proposition through the data and informa-tion value chain enabled by the set of business capabilities.

The key value proposition of data management is enabling the process of the transformation of data into meaningful information. The data management function delivers this value proposition by build-ing an effective data and information value chain supported by a set of data management capabilities.

The “Orange” model uses the following definition of the data and information value chain:

Data and information value chain is the set of actions supported by the collection of data management capabilities that enable the transformation of raw data into meaningful information in order to deliver the value propositions to the corresponding stakeholder groups.

PRINCIPLE 3: The set up of the data management capability within the company follows the logic of the development of the data and information value chain and business capabilities that enable the chain.

To implement an effective data management function, the data and information value chain and the required set of data management capabilities should be developed. Implementation of data manage-ment function follows the logic of documentation of the data and information value chain.

9

THE “ORANGE” MODEL IS A COLLECTION OF TECHNIQUES AND TEMPLATES FOR THE PRACTICAL ESTABLISHMENT OF THE DATA MANAGEMENT THROUGH THE

DESIGN AND IMPLEMENTATION OF THE DATA AND INFORMATION VALUE CHAIN, ENABLED BY A SET OF DATA MANAGEMENT CAPABILITIES.

PRINCIPLE 4: Data management is an independent business function.

To deliver the data management business capability, a company should establish (a) corresponding business function(s). These business functions should be aligned with the company organizational structure.

A lot of different data stakeholders are involved in the functioning of the data and information value chain. The key role of data management is the coordination of data-related activities among various business stakeholders. Therefore, data management from the organizational perspective should be established as an independent function.

PRINCIPLE 5: The “Orange” model is applicable for mid-sized companies and is industry agnostic.

The model covers the most essential needs and requirements in the data management of mid-sized companies. Data management functions have a similar structure and use identical tools independent of the industry.

10

AREAS OF APPLICATION

11

The leading doctrine of the “Orange” model is its practical orientation. Data management profession-als and consultants might apply it in the mid-sized companies for performing the following tasks:

1. Performance of maturity assessment of the data management function.

This model allows assessing the status of “as is” and “to be” situations. Based on the assessment’s re-sults, a gap analysis can be performed. The results of the gap analysis lead to the development of a data (management) strategy and/or roadmap.

2. Design and implementation of a data management strategy and/or roadmap.

The “Orange” model offers the overview of the content of and relationships between key elements of the data (management) strategy, such as:

• key stakeholders and their concerns

• key data value propositions per stakeholder group

• main activities of the data and information value chain

• core and supporting data management capabilities and their components.

The critical success factor of the development and implementation of the data management strategy is the existence of the operational data management function.

3. Design and implementation or optimization of a data management function.

The “Orange” model is based on the principle that the implementation of the data management frame-work follows the logic of the development of the data and information value chain and related capabil-ities. The implementation or optimization of the data management function means the (re)design and establishment of the set of:

a. data and information value chains

b. corresponding data management capabilities and functions.

To make each data management capability operational, the detailed design of each capability to the level of the processes, procedures, tasks, deliverables, and roles accountabilities is required.

4. Design and implementation or optimizaton of particular core data management capabilities, for example, a data management framework.

The “Orange” model offers a set of techniques and templates in addition to those that are described in the “The Data Management Toolkit.”14 These techniques allow a company to develop a feasible set of data management capabilities and implement them in practice.

A clear set of roles and the unambiguous assignment of accountabilities and tasks make each data management capability fully operational.

5. Design and implementation of data management/governance roles.

The "Orange" model offers the method to develop the precise structure of data management-related roles. It maps data and information value chain activities and capabilities to the corresponding roles.

To ensure that the data management function performs successfully, an analysis of its effectiveness

should be done.

6. Assessment of the performance of the data management function.

The “Orange” model offers techniques that allow a company to assess the performance of the data management function. It can be done by evaluating performance, effectiveness, revenue/cost contri-bution, and criticality of the data and information value chain and particular data management capa-bilities.

12

KEY COMPONENTS

13

The structure of the model elaborates on some of the developed methods and were made available for public use by The Open Group.15

1. AN OVERVIEW OF THE ROLE OF DATA AND INFORMATION IN THE BUSINESS LIFECYCLE

The strategic goal of each business is long-term survival. In order to survive, a business needs to ensure a steady profit and proper asset management. Information is one of the key assets that supports deci-sion making and the overall performance of a company. To get the required information, the company must ensure the delivery of the corresponding data and enable the correct data transformation into meaningful information. "Data and information value chain" enables this transformation. Business processes and required tools and resources empower the functioning of the data and information value chain. The relationship between data, information, business processes, and tools and resources in the business lifecycle is also presented in Figure 1.

THE “ORANGE” MODEL INCLUDES THE FOLLOWING COMPONENTS:

1. An overview of the role of data and information in the business lifecycle.

2. A standard metamodel of data management.

3. Stakeholder–data management value proposition matrix.

4. A standard data and information value chain model.

5. A standard set of data management capabilities.

6. A detailed model of a data management capability.

7. Data and information value chain – data management capabilities matrix.

8. Data management maturity assessment techniques.

Figure 1. The role of data and information in the business lifecycle.

Businessdrivers

DATAINFOR-

MATION

BUSINESSFUNCTIONS

SYSTEMS&

RESOURCES

Performance(i.e. KPI

EXTERNAL

ENVIRONMENT

INTERNAL

ENVIRONMENT

Data management is the means that empower the proper design and function of the data and informa-tion value chain. Data management is a set of business capabilities and is described utilizing a meta-model.

2. A STANDARD METAMODEL OF DATA MANAGEMENT

The core object of data management and the metamodel are data and information resources.

The “Orange” standard metamodel highlights the data management business capabilities from two perspectives. The first one is the “broad” perspective and takes a look at data management from the enterprise viewpoint in regard to the lifecycle of data circulating in a company. The second one is the “narrow” perspective and scopes data management from the viewpoint of tasks to be performed by data management professionals.

The “Orange” standard metamodel is presented in Figure 2.

14

SECURITY

INFORMATION

TECHNOLOGY

ENTERPRISE

ARCHITECTURE

DATA MANAGEMENT

FRAMEWORK

(DATA GOVERNANCE)

DATA & INFORMATION

RESOURCESDATA & INFORMATION

QUALITY

coordinates

manages

has

describes,classifies,models,designs

describes,classifies,

models,designs

designs

implements

enables

the

lifecycle

of

enables security of

Figure 2. The universal metamodel of data management.

The “narrow” perspective

Data management professionals perform several key data management capabilities that relate to the “narrow” perspective of data management. These capabilities are data and information quality, and data management framework. The boxes with these capabilities are marked green in Figure 2.

Business, data, application, and technology architecture comprise enterprise architecture. The “nar-row” scope of data management includes only data architecture, along with data modeling, and a part of the application architecture limited to the range of data and application flows.

The “broad” perspective

The “broad” perspective of data management includes all capabilities from the “narrow” perspective capabilities and adds capabilities that enable data processing. These capabilities are security, informa-tion technology, and application and technology architecture.

Each company should choose one perspective or another in order to scope the data management func-

15

tion. The content of data management has to correspond to the company strategy, culture, and re-sources.

Data management is a set of business capabilities that delivers particular values to a set of different stakeholder groups.

3. STAKEHOLDER – DATA MANAGEMENT VALUE PROPOSITION MATRIX

The “Orange” model delivers the analysis of different stakeholder groups that have concerns regarding data management deliverables. These groups can be classified based on their relation to the business and split into two categories: internal and external. The examples of the external stakeholders are gov-ernmental and regulatory bodies, external customers, providers of data. The examples of the internal stakeholders are company management at different organizational levels and diverse business func-tions.

For each of these specific groups, data management function delivers particular value propositions. A particular product or service that satisfies the specific needs of a stakeholder group form a value prop-osition.

The “Orange” model elaborates on matching combinations of a stakeholder group and corresponding value propositions. An example of the analysis of a value proposition to the specific stakeholder group can be seen in Figure 3.

Key Partners

1. Internal business units that deliver data

2. Third parties (exter-nal) that deliver data

3. IT function that en-ables data delivery

Key Activities

1. Gathering informa-tion and data require-ments

2. Ensuring data quality checks and controls are in place

3. Ensuring optimi-zation of reporting capabilities

4. Analysing of the data value chain

5. Assisting in identifi-cation of the redundant systems

6. Participating in IT projects

Value proposition

1. Enhanced customer

acquisition

2. Acceleration of the development of new products and time to market delivery

3. Operational cost

reduction due to opti-mized data collection, processing, storage, and dispersal

Customer Relationships

1. Proactive collabo-ration

Customer Segments

1. Internal: Senior man-agement

Key Resources

1. Data management professionals and their knowledge

2. Tooling

Channels

1. Meetings

2. Information delivery

3. Awareness sessions

Cost Structure

1. Data management professionals

2. Tooling

3. Knowledge base

Revenue Structure

1. Increased profit by reducing operational expenses, including IT costs, resources spent on DQ resolutions, etc.

2. Increased revenue by enhanced customer acquisition

Figure 3. An example of the analysis of the value proposition per stakeholder group.

To deliver the specified value propositions, corresponding data and information value chains should be designed and implemented.

4. A STANDARD DATA AND INFORMATION VALUE CHAIN MODEL

The “Orange” model proposes a standard design of the data and information value chain. The data and information value chain is the set of actions supported by the collection of data management capabili-ties that enable the transformation of raw data into meaningful information to deliver the correspond-ing value propositions to the stakeholder groups. The data and information value chain supports the business value chain that delivers value to the internal and external business customers..

The design of a data and information value chain can be done at different levels of abstraction. An ex-ample of common activities is presented in Figure 4.

16

Figure 4. An example of standard activities of the data and information value chain.

Specify stakeholders’ information and

data requirements

Design and/or optimize data and information

value chain

Process data into information

Use information

DATA & INFORMATION VALUE CHAIN

The first activity, “Specify data and information requirements,” focuses on the definition of the chain’s scope. Information needs can only be covered through the delivery and transformation of needful data.

The second activity, “Design and/or optimize data and information value chain,” pays attention to the design of a new or optimized existing chain. This activity covers such tasks as data and information definition and modeling, as well as the design of the application and data flows.

The third activity specifies the key steps in the required data processing.

The fourth activity describes the steps that end users might perform in order to get value from infor-mation.

An example of the elaborated activity “Design and/or optimize data and information value chain” is shown in Figure 5. For more information about the method that describes the implementation of this activity, consult my earlier book “The Data Management Toolkit.”16

Figure 5. An example of a detailed level of the data and value chain activity.

Specify a value

proposi-tion per

stakehold-er group

Outline the key data &

informa-tion value chains and their com-

ponents

Link value chains to business capabili-

ties

Specify (critical)

data sets and ele-ments

Document infor-

mation and data require-ments

Classify, describe,

and model the critical data (ele-

ments)

Design, describe,

and/or optimize

data & ap-plication

flow

Document data

lineage

Document business process

Design information

& data value chain by mapping all relevant

compo-nents

17

To enable the proper functioning of data and value chain activities, a set of data management business capabilities is required.

5. A STANDARD SET OF DATA MANAGEMENT CAPABILITIES

The “Orange” model offers a set of data management-related capabilities. The proposed set of capa-bilities covers the data management in both “narrow” and “broad” perspectives.

In the context of the “Orange” model, data management capability is a set of data, processes, tools, and roles that enable the achievement of a specific data management outcome.

The design of data management capabilities can be done at a different level of abstraction.

There are three types of data management capabilities.

Core data management capabilities correspond to the “narrow” perspective of data management and include the following capabilities: data architecture, data modeling, data quality, and a data manage-ment framework. Some outcomes of application architecture can also be included in this scope.

Supporting IT capabilities added to the core data management capabilities form the whole scope of data management’s “broad” perspective. Supporting IT capabilities include application and technology architecture, data lifecycle management, and infrastructure management.

Other supporting business capabilities enable the effective performance of data management capabil-ities. Among these capabilities are some that are of exceptionally high importance; for example, the security applied to data and information, application, and infrastructure.

The graphical representation of the “Orange” set of data management capabilities can be seen in Fig-ure 6.

Figure 6. The “Orange” model standard set of data management capabilities.

"Narrow" perspective

"Broad" perspective

CORE DATA MANAGEMENT CAPABILITIES

SUPPORTING IT CAPABILITIES

OTHER SUPPORTING CAPABILITIES

Data architecture Data modeling Data quality Data management framework

Infrastructure management

Data lifecycle management

Technology architecture

Application architecture

Security Changemanagement Audit

Business process management

Business architecture

Project management

Each capability can be decomposed at the capabilities of the more detailed level.

An example of the decomposition of the capability of the data management framework can be found in Figure 7 (see next page).

18

For practical usage of the data management capability model, each capability should be described on a detailed level.

6. A DETAILED MODEL OF DATA MANAGEMENT CAPABILITY

A business capability is comprised of four elements:17 process, role, tools, and data that is used for de-scribing a business capability at a detailed level.

The “Orange” model offers a detailed description of each of the core data management capabilities.

An example of such structuring of a business capability is shown in Figure 8.

Figure 7. An example of the data management capability decomposition.

Stakeholder management and

coordination

DATA MANAGEMENT FRAMEWORK

Goal setting Rules and processes set-up

Specification of roles, tasks and accountabilities

DM deliverables documentation

Figure 8. An example of the detailed data management capability.

PROCESSES

• Define and plan data management initiatives

• Develop, implement and maintain data manage-ment framework

DATA (DELIVERABLES)ROLES

TOOLS

DATA MANAGEMENT FRAMEWORK

• Business drivers• Planning of data

(management) related initiatives

• Data (management) principles

• DM operating model• DM professional orga-

nization• Data (management)

maturity assessment• DM roadmap, policy,

processes, procedures

• Data governance / management repository (tool)

• Business process tool• DM initiatives funding

and budgets

• Data management / governance body

• Enterprise architec-ture body

• Data stewards or-ganisation (business SME)

• Data management professionals organi-zation

• IT professionals orga-nization

7. DATA & INFORMATION VALUE CHAIN – DATA MANAGEMENT CAPABILITIES MATRIX

The core idea behind the “Orange” model is that the implementation of the data management func-tion follows the logic of the design and optimization of the data and information value chain and relat-ed data management capabilities.

The performance of different activities along the data and information value chain requires involve-ment and usage of various data management capabilities.

For example, such a capability as a “data management framework” will be required for the perfor-mance of each activity of the chain.

The core idea behind matching the chain to the data management capabilities is presented in Figure 9.

19

CORE DATA MANAGEMENT CAPABILITIES

SUPPORTING IT CAPABILITIES

OTHER SUPPORTING CAPABILITIES

Data architecture Data modeling Data quality Data management framework

Infrastructure management

Data lifecycle management

Technology architecture

Application architecture

Security Changemanagement Audit

Business process management

Business architecture

Project management

An example of matching one of the activities of the chain and some of the data management capabil-ities can be found in Figure 10.

Specify stakeholders’ information and

data requirements

Design and/or optimize data and information

value chain

Process data into information

Use information

DATA & INFORMATION VALUE CHAIN

Figure 9. The core idea behind matching the data and information value chain to the set of capabilities.

Figure 10. An example of matching the chain and capabilities.

Specify value

proposi-tion(s) per stakehold-

er group

Outline the key compo-nents of

the infor-mation &

data value chain

Specify key DM

business capa-

bilities, processes,

tasks, roles and delivera-

bles

Specify relevant to

the busi-ness briver

& value proposi-tion (crit-ical) data

sets & elements

Classify, describe

and model the critical

data

Design, describe and/or

optimize data & ap-plication

flow

Document verti-

cal and horizon-tal data lineage

Document business process

Design informa-

tion & data value

chain by map-

ping all relevant compo-

nents

Data archi-tecture check check check check check

Data modeling check check check check check

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8. DATA MANAGEMENT MATURITY ASSESSMENT TECHNIQUES

The “Orange” model also includes a tool for assessing the maturity of the data management function and capabilities.

The maturity model considers five levels of maturity. The model allows for the assessment of the ma-turity at the level of:

• the core data management capabilities (see Figure 11)

• each of the four components of the core data management capabilities (see Figure 12).

Figure 11. An example of the data management capability maturity assessment.

Figure 12. An example of the maturity assessment per capability component.

DATA MANAGEMENT CAPABILITY MATURITY ASSESSMENT

Data Management Framework

Information Value Chain

Data ModelingInformation Architecture

Data Quality

DATA MANAGEMENT CAPABILITY MATURITY ASSESSMENT

Process

Roles

Tools

Data

21

The maturity at the level of data management capability component is assessed using the following criteria:

LEVELLEVEL NAME

DATA (DELIVER-

ABLES)

PROCESS-ES

TOOLS & RESOURCESROLES

TOOLSRECOURC-

ES

1Uncon-trolled

Unavailable Inexistent Inexistent Unavailable Inexistent

2 Ad-hocOn ad-hoc

basisInformal Inexistent

On ad-hoc basis

Informal

3In develop-

mentIn design In design

In investiga-tion

In assess-ment

In design

4 CapableImplemen-

tationImplemen-

tationImplemen-

tationJust budget-

edImplemen-

tation

5 EffectiveOn regular

basisOperational

Implement-ed

Budgeted on a regular

basisOperational

The “Orange” model applies these standard criteria to each of the core data management capabilities and their components.

SUMMARY

Every data management professional experiences some challenges when it comes to the practical im-plementation of data management functions. There are several common challenges that almost every professional must face, such as:

• There are several well-known industry reference guides. They all have conceptual differences. Which one best meets the requirements of the company in question?

• The industry reference guides are inconsistent with respect to data management components, terminology, and corresponding definitions. Which model of data management fits the compa-ny business model?

• Each industry reference guide is based on the best practices but has a lack of practical imple-mentation recommendations. Each company should invent the implementation methodology on its own.

• Data management maturity assessment is a helpful tool in assessing the gaps in the “as is” and “to be” status of the data management function, developing the roadmap, and comparing the company’s achievements with peers in the industry. There are several industry maturity models available, but their metamodels are incompatible with the metamodels of data management functions presented in the industry reference guides.

• The results of maturity assessment performed using different maturity models are incompat-ible.

To overcome the challenges with the existing data management meta- and maturity models, the “Or-ange” model has been developed. This model focuses on the needs of small- and medium-sized com-panies.

The “Orange” model is a collection of techniques and templates for the practical establishment of the data management function through the design and implementation of the data and information value chain and enabling data management capabilities.

There are several key principles that create the foundation of the model:

1. Data management is a business capability.

2. Data management delivers its key value proposition through the data and information value chain enabled by the set of business capabilities.

3. The set up of the data management function within the company follows the logic of the devel-opment of the data and information value chain and business capabilities that enable the chain.

4. Data management is an independent business function.

5. The “Orange” model is applicable for mid-sized companies and is industry agnostic.

The “Orange” model serves to achieve the following goals:

1. Perform maturity assessment of the data management function.

2. Design and implement a data management strategy and/or roadmap.

3. Design and implement or optimize a data management function.

4. Design and implement or optimize particular core data management capabilities.

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5. Design and implement data management/governance roles.

6. Assess the performance of the data management function.

The “Orange” model delivers the set of standard techniques and templates, including:

• a standard metamodel of data management

• value proposition templates

• a standard data and information value chain model

• a standard set of data management capabilities described and detailed using four dimensions

• data management maturity assessment techniques.

The “Orange” model is the toolkit for advanced data management professionals and consultants that are involved in the data management function implementation.

REFERENCES

1. DAMA International, DAMA-DMBOK: Data Management Body of Knowledge. 2nd ed., Bradley Beach, N.J.: Technics Publications, 2017

2. EDM Council, “About DCAM.” EDM Council, 24 Sep. 2019, edmcouncil.org/page/aboutdcamreview.

3. The Open Group, “Welcome to the TOGAF® Standard, Version 9.2, a standard of The Open Group.” The Open Group, 2 Sep. 2009, pubs.opengroup.org/architecture/togaf92-doc/arch/.

4. Steenbeek, Irina,The Data Management Toolkit. 1st ed., Data Crossroads, p.47-48.

5. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd ed., Bradley Beach, N.J.: Technics Publications, 2017

6. EDM Council, “About DCAM.” EDM Council, 24 Sep. 2019, edmcouncil.org/page/aboutdcamreview.

7. CMMI Institute, “Data Management Maturity (DMM)SM.” CMMI Institute, 22 Mar. 2018, cmmiinstitute.com/data-management-maturity

8. Chessel, Mandy, “IBM Information Governance Model.” IBM Corporation, 2010.

9. “Data Governance Maturity Model.” University of Standford, 2011.

10. Gartner Inc., “The Gartner Enterprise Information Management Framework.” Gartner Inc., 24 Oct. 2016, blogs.gartner.com/andrew_white/files/2016/10/On_site_poster.pdf.

11. Steenbeek, Irina. “Data Management maturity models: a comparative analysis.” Data Crossroads, 16 Dec. 2018, datacrossroads.nl/2018/12/16/data-management-maturity-models-a-comparative-analysis/

12. Data Crossroads, “A Comparative Study of Data Management Maturity Models.” Slideshare, 13 May 2019, slideshare.net/datacrossroads/a-comparative-study-of-data-management-maturity-models.

13. “Orange (fruit).” Wikipedia, 29 Jul. 2019, en.wikipedia.org/wiki/Orange_(fruit).

14. Steenbeek, Irina,The Data Management Toolkit. 1st ed., Data Crossroads

15. “The Open Group.” The Open Group, 15 Mar. 2006, www.opengroup.org/

16. Steenbeek, Irina,The Data Management Toolkit. 1st ed., Data Crossroads

17. The Open Group. The Open Group Guide. Business Capabilities. Prepared by the Open Architecture Forum Business Architecture Work Stream. The Open Group, March 2016,p.2.

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LIST OF FIGURES

Figure 1. The role of data and information in the business lifecycle. ©Data Crossroads, 2019.

Figure 2. The universal metamodel of data management. ©Data Crossroads, 2019.

Figure 3. An example of the analysis of the value proposition per stakeholder group. ©Data Crossroads, 2019.

Figure 4. An example of standard activities of the data and information value chain. ©Data Crossroads, 2019.

Figure 5. An example of a detailed level of the data and value chain activity. ©Data Crossroads, 2019.

Figure 6. The “Orange” model standard set of data management capabilities. ©Data Crossroads, 2019.

Figure 7. An example of the data management capability decomposition. ©Data Crossroads, 2019.

Figure 8. An example of the detailed data management capability. ©Data Crossroads, 2019.

Figure 9. The core idea behind matching the data and information value chain to the set of capabilities. ©Data Crossroads, 2019.

Figure 10. An example of matching the chain and capabilities. ©Data Crossroads, 2019.

Figure 11. An example of the data management capability maturity assessment. ©Data Crossroads, 2019.

Figure 12. An example of the maturity assessment per capability component. ©Data Crossroads, 2019.

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The “Orange” (meta)model of data management provides a collection of tech-niques and templates for the practical set up of data management through the design and implementation of the data and information value chain, enabled by a set of data management capabilities.

This book is a toolkit for advanced data management professionals and con-sultants thatare involved in the data management function implementation.

This book works together with The Data Management Toolkit. The “Orange” model assists in specifying the feasible scope of data management capabili-ties, that fits company’s business goals and resources. The Data Management Toolkit is a practical implementation guide of the chosen data management capabilities.

ABOUT THE AUTHORDr. Irina Steenbeek is a data management practitioner with more than 10 years of ex-perience. The key areas of her professional expertise are the implementation of data management frameworks in mid-sized com-panies and data lineage. Additionally, Irina has practical experience in software imple-mentation such as ERP and DWH/BI, man-agement consultation, financial and busi-ness controls, and data science.Through the years, she has worked for global institutions as well as large- and medium-sized organiza-tions in different sectors, including but not limited to financial institutions, professional services, and IT companies.

In 2016, she founded Data Crossroads - a training and coaching services enterprise in the area of data management. Data Crossroads focuses on assisting companies in improving their decision-making and company performance by getting control over their data and information resources.

Irina is a strong believer that the success of data management initiatives is based on the combination of a pragmatic approach, clear and transparent methodology, and time. She has shared her approach and implementation experience by publish-ing The Data Management Toolkit and The Data Management Cookbook. She is also the author of various white-papers and articles on the topic of data management.