Information Transformation Workbook - SAP … · Information Transformation Workbook 1 Sponsored by ... business executives ... from factory to shelf — “Turning diapers into insights,”

Information Transformation Workbook

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Sponsored by


AuthorsPhilip Carter

Jan van VonnoSureshpal SinghApril 2017


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This workbook serves as a guide to assist CIOs and their team members in identifying areas in need of enhancement to support their organization’s digital transformation (DX) journey across perhaps the most important and central dimension of DX: Information Transformation. It is one of many resources created by IDC as part of a broader Digital Roadmap Program sponsored by SAP. The tools and exercises in this workbook correspond with IDC’s tested assessment methodologies for digital transformation as well as Information Transformation.

This workbook is appropriate for organizations both new and experienced in driving digital and Information Transformation initiatives and is designed to provide a framework to cover each component of Information Transformation:

1. Information architecture

2. Data discovery

3. Value development

4. Value realization

5. Knowledge and collaboration

A first pass at this workbook can be completed in less than a day, but to implement the learnings contained within it, we recommend users revisit this content several times over the course of the next six to twenty-four months. This workbook was created as a supplemental resource to assist CIOs in their digital transformation journeys.

Objectives

Gain an understanding of what Information Transformation really means and why it is important for your organization’s digital transformation efforts.

Map out your organization’s maturity across all five dimensions of information transformation, which you can use to benchmark your progress as you revisit this workbook over time.

Using the check lists, create an Action Plan that will allow you to focus on enhancing your information strategy over six, twelve, and twenty-four months.

Instructions

SECTION 1 The first section provides templates and tools to track your organization’s Information Transformation maturity and progress. Use the fields and figures to record your action plan and assess how you evolve over time.

SECTION 2 This section provides an introduction to digital transformation, the changes taking place within organizations, and a comprehensive description of all Information Transformation dimensions. Use this section to reflect on your organization to understand where you currently are on your transformation journey.

SECTION 3 The third section provides checklists for a six-, twelve-, and twenty-four-month action plan across all information dimensions and subdimensions. Leverage these checklists to create your own action plan that you can update over time.

SECTION 4 The final section provides a light framework for setting the first project plan and introducing the concept of Leading in 3D.

Introduction

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1 2

YOU ARE HERE WHAT’S INCLUDED

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Welcome to Section 1. This section will help you record your progress as you go through the workbook and familiarize yourself with the concepts of Information Transformation. You can use this section to note down your learnings and action items from the upcoming chapters in this workbook and assess your maturity across all five information subdimensions over time.

IDC recommends that you do not attempt to complete these assignments until you have gone through the whole workbook.

ACTION LIST

SELF-ASSESSMENT


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Self-Assessment: Information Transformation

Carry out a self-assessment of your maturity across all information dimensions. The assignments in Section 2 will ask you to revisit this page to estimate your maturity. For each of the Information Transformation dimensions on the horizontal axis, put an ‘X’ where you think your organization sits across the five maturity stages on the vertical axis.

The chart below shows how information transformation maturity can evolve over time, from the very first baseline assessment to the third assessment taken twelve months later. In the meantime, you will have been executing on the action plan developed for the individual categories. Using this workbook, it is important that you measure and evaluate your progress systematically to capture where your efforts are helping you realize the goals you set out to achieve.

EXAMPLE SELF-ASSESSMENT

Self-assessment

The appendix includes a comprehensive overview on the description of each information dimension and sub-dimension for reference.

IDC Tip

SELF-ASSESSMENT: Now it’s your turn

Note that IDC has found that all information dimensions are interrelated. It is rare to find an organization at the optimized stage in one dimension, but at only at the ad hoc stage for the other categories.

InformationArchitecture

Ad hoc

Opportunistic

Repeatable

Managed

Optimized

First baseline assessment

DataDiscovery

ValueDevelopment

ValueRealization

Knowledge andCollaboration

Six month assessment

Twelve month assessment

Twenty-Four month assessment

InformationArchitecture

Ad hoc

Opportunistic

Repeatable

Managed

Optimized

DataDiscovery

ValueDevelopment

ValueRealization

Knowledge andCollaboration


5

Elaborate on your assessment:

BASELINE ASSESSMENT:

SECOND ASSESSMENT:

THIRD ASSESSMENT:

FOURTH ASSESSMENT:

Self-assessment


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Action Plan: Information TransformationUsing the checklists in Section 3, develop your own action plans to enhance your organization’s digital transformation journey. Focus on the Information Transformation dimensions; define your objectives; and identify which 0-6, 6-12, and 12-24-month action items will help you improve your maturity. Revisit your action plan to evaluate your progress and maintain your focus on your objectives.

Example 0-6 month action plan

OBJECTIVE

• Map out all enterprise information

• Launch initiatives to standardize, consolidate redundant data, and improve quality

• Etc…

• Introduce data sourcing procedures, platforms and portfolios to the organization

• Introduce initiatives to drive data collection and analysis

• Etc…

• Start developing data models encompassing market, revenue, and financials from statistical inference

• Assess data quality across the organization

• Start defining data guidelines for all enterprise-structured and ecosystem data

• Etc…

• Introduce basic innovation management processes to business executives

• Work with the business to define and create a data valuation and monetization strategy

• Experiment with product management data-driven enhancements or initial data product creation

• Etc…

• Start integrating knowledge base and communities of practice by aligning and consolidating repositories of structured and unstructured data

• Evaluate GDPR compliance and potential threats

• Etc…

• To create a new data enhanced product or service offering…

Self-assessment

INFORMATIONARCHITECTURE

DATADISCOVERY

VALUE DEVELOPMENT

VALUE REALIZATION

KNOWLEDGE & COLLABORATION


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Action Plan: Now it’s your turn

Build out your action plans in terms of short-, mid-, and long-term objectives. Define your measures for success and describe what you are aiming to realize. Use the checklists provided in Section 3 to help you define which actions you can take into consideration as you start developing your plan to support digital transformation in your organization.

Your 0-6 month plan


VALUE REALIZATION

VALUE DEVELOPMENT

DATADISCOVERY


OBJECTIVE

Self-assessment


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VALUE REALIZATION

VALUE DEVELOPMENT

DATADISCOVERY


OBJECTIVE

Your 6-12 month plan

Self-assessment


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VALUE REALIZATION

VALUE DEVELOPMENT

DATADISCOVERY


OBJECTIVE

Your 12-24 month plan

Self-assessment


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Welcome! Here in Section 2 you will be introduced to the concept of Digital and Information Transformation. It will address the changes within organizations and provide a comprehensive description of all Information Transformation dimensions. In this section, you will be asked to read and reflect on your organization.

WELCOME TO DIGITAL

BATTLE FOR INFORMATION

INFORMATION MATURITY

INFORMATION DX DIMENSIONS

Did you know 1 in every 5 European businesses can be classified as a digital resistor??


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Information DX

Digital disruption is a reality every organization faces today. Information technology

(IT) has moved from the back office to the front office, and has now finally embedded itself

into nearly every aspect of our professional and personal lives. Most of the changes we are

witnessing today are being fueled by mobile, social, cloud, and Big Data technologies, which

we at IDC collectively refer to as the “3rd Platform”. We have embarked on the third revolution

in IT and it marks an era during which the technologies and processes that businesses deploy

are so tightly linked to their customers and markets that the boundary between the internal

operations of the enterprise and its external ecosystem (e.g., customers, markets, competitors,

partners, and regulators) is rapidly disappearing. Now business leaders are challenged to

move their enterprises to the next level — not only in terms of operations but also that of the

business models. We are calling this moment of recognition “digital transformation,” and it has

sparked the urgency of change in every industry we know.

Digital transformation is not something that can happen in isolation. It involves the introduction

of digital technologies coupled with organizational, operational, and business model

innovation to create new revenue streams and engagement models. IDC has developed the

Digital Transformation MaturityScape (An IDC Maturity Model for Digital Transformation)

to help business and IT leaders understand and cope with the challenges and opportunities

that digital transformation can bring to their enterprises. It provides a framework for viewing

stages of maturity across five key dimensions: Leadership Transformation, Omni-Experience

Transformation, WorkSource Transformation, Operating Model Transformation, and the key

dimension explored in this workbook — Information Transformation.

It is fascinating to see how many companies are trying to redefine their business models in

the face of digital disruption. For example, BMW’s CEO Harald Krüger expressed his intention

to “become the leaders for digital transformation of the automotive industry”; Francisco

Gonzalez (CEO and chairman of Spanish bank BBVA) wants to build “the best digital bank of the

21st century” by transforming BBVA into a software company and converting information into

value-added services; Barclays Bank in the U.K. is now focused on becoming an “information

business”; Proctor & Gamble is striving to become the most technologically enabled business

in the world, from factory to shelf — “Turning diapers into insights,” according to Robert

McDonald (CEO of P&G); Kloeckner & Co, a leading German multi-metal distributor, is looking

to use its digital platform to connect suppliers and customers with the aim of digitizing the

firm’s entire supply chain to drive a better experience for its customers. Gisbert Ruehl, CEO,

believes the platform will start delivering 50% of its business from its digital initiatives by 2019.

IDC research shows that digital transformation has reached the corporate agenda in close to

90% of enterprise organizations in nearly every industry we know.

of European organizationshave digital transformation on their corporate agenda90%

https://sap-eu.dx-idcmaturityscapesnapshot.com/


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MOST SUCCESSFUL

DIGITAL PROJECTS ARE LED BY THE

CIO, BUT WITH HIGH PARTICIPATION OF OTHER BUSINESS

EXECUTIVES

The examples above show that successful digital transformation will largely depend on how businesses leverage information collected from internal and external sources. The reality is that the amount of information stored within the enterprise is growing approximately 40% every year, and this rapid expansion of the digital universe poses an incredible challenge for organizations. IDC truly believes information sits at the heart of digital transformation projects, and enterprises that become adept at exploiting information will thrive — those that fail to master information will struggle to survive.

The formal responsibility for the management and quality of information has traditionally been assigned to the Chief Information Officer (CIO). These executives have primarily focused on maintaining and improving service level agreements (SLA) to deliver reliable IT services and infrastructure to internal users, while at the same time keeping an eye out for opportunities to lower operating expenses. However, with the advent of new disruptive business models in many industries, business leaders have aimed to increase their competitiveness by launching

digital technologies and competencies outside the realm of the CIO. Business leaders believed (and many still do) that they have the authority to

acquire digital services and information technologies through third parties, without involving the IT department. The shift from business

continuity to business agility and speed has caught many CIOs off-guard, and now they are playing catch up to integrate new business solutions instead of driving them.

In some large organizations, the notion of digital transformation has given birth to a new line of executives known as the chief digital

officer (CDO). These executives are being introduced by many across industries to facilitate the strategic business model implications of

digital transformation. However, the CDO role is not a formal one; IDC has witnessed many different types of CDOs, each addressing a variety of

responsibilities, resources, and expectations, some of which may have historically been within the remit of the CIO — from the promotion of digital media to the creation of new digital products, services, and customer engagement models. Some mature CDOs even run entire digital business units with significant revenue targets.

The fact is that both CIOs and CDOs are looking to collect information to convert it into meaningful insights, and where possible new revenue streams, by augmenting existing and new business products and services across the enterprise. A CIO or CDO capable of understanding the strategic importance of digital transformation, as well as the direction of the organization, should be in the best position to execute on digital objectives. This, however, cannot happen in isolation — it requires the involvement and empowerment of executives across all business functions. Organizations aiming to drive digital transformation require a solid digital platform that collects and connects information while providing the appropriate levels of security, governance, and analytics for business executives.

Information DX


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Information Transformation at the Heart of Digital

People often say knowledge is power. If that’s true, discovering new knowledge, enhancing products or services with intelligence, and acting on those insights, are perhaps even more powerful than the mere possession of knowledge. Business executives now see that the increased adoption of information technology is providing unprecedented computing capabilities and unlimited avenues to access information and knowledge as information itself becomes even more valuable.

However, the fact remains that Information Transformation is an enormous challenge for most. Information Transformation needs to be a critical component of an organization’s business strategy because it underpins all digital transformation efforts — i.e. Leadership, Omni-Experience, Operating Model, and WorkSource.

IDC PREDICTS THAT BY 2020, ORGANIZATIONS WILL MOVE FROM BUILDING THE DIGITAL ENTERPRISE TO BUILDING THE INFORMATION-BASED ENTERPRISE, WITH 33% OF ALL DIGITAL TECHNOLOGY SPENDING DEDICATED TO INFORMATION TRANSFORMATION.

IDC Fact

Figure 1. Information Interdependencies

Informationarchitecture

Acquisition

Smart data

Preparation

Exploration

Visualization

Real-timeorchestration

Digitalt

Serviceinnovation

Productenhancement

Production

Inference

Regression

Prediction

Machine-learningalgorithms

Intellectualproperty

Transparency

Real-timecollaboration

Integration

Riskoptimization

Data discoveryKn

owledge and

col

laboration

Value developm

entValue realization

Omni-Experience DXW

orks

ource DX

Leadership

DX

Operating Model DX

Information DX


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IDC has developed a model to help executives assess their organization’s information foundation through five maturity stages to the information dimension (see Figure 2 and Table 1). Leaders in Information Transformation will treat data and information as they would any critical business asset — with investments in people, processes, and technologies that acknowledge information’s strategic importance and with a road map to maximize information’s contribution to the success of the business. The most advanced companies can accelerate the pace of sophisticated analysis, the mix of data and data types, and the ability to optimize and predict business decisions.

Figure 2: IDC Information Transformation Maturity Stages

Ad Hoc

Opportunistic

Repeatable

Managed

Optimized

Data silos

Data warehouseand analytics

ArchitectedInformation

IntegratedInformation

Holistic real-timeInformation

Information is siloed. Data quality & integration issues constrain usage to limited domains. Risks are unknown.

Transactional data is managed. Data warehousing provides basic analytics and reporting. Security is assessed.

Information framework includes internal & external sources, structured & unstructured data, intelligence and security.

Comprehensive information platform includes social, mobile and IoT with advanced analytics generating new revenue streams.

Competitive strength and significant revenue streams from real-time management of information volume, velocity and variety.

Information DX


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Table 1: IDC Information Maturity Stages

Operational systems run multiple instances. Data quality problems combined with integration and compatibility issues enable only limited localized business digitalization. Structured data is primarily presented in predefined reports, and unstructured data is left unexploited. Data security is unknown or guided by a no-risk approach.

Traditional data warehouse and analytics are mainly limited to structured data. Localized business experiments with socialytics, Big Data, and real-time analytic processing have yet to coalesce into a coherent strategic information architecture. Data is presented through dynamic and highly interactive visualization tools. Data security is assessed.

Information architecture, including security, allows internal and external information in a variety of forms — relational or NoSQL — to be leveraged for business-critical domains, enabling instantaneous data analysis and opportunities based on better intelligence and insight.

Enterprise gains a competitive advantage with information and social listening, Web, and mobile Big Data and analytics to assess the current and future state of customers and markets to stay ahead of competition. Enterprise generates new revenue streams from data products and services.

Information in multiple forms is integrated from a myriad of sources and enables realtime predictable analytics. Monetization of data from and about products, customers, and markets is a core part of the enterprise’s business strategy and a significant source of revenue and competitive strength.

INFORMATION MATURITY STAGE

AD HOC

OPPORTUNISTIC

REPEATABLE

MANAGED

OPTIMIZED

DESCRIPTION BUSINESS OUTCOME

The results in limited localized business digitalization.

Business intelligence and insights from structured data are limited.

Standardization and statistical programming provide insights from structured and unstructured data.

Enterprise information enables competitive advantage from digital value-add and service innovation.

Informatioin drives continuous business innovation, creates revenue stream, and fuels customer experience. Information also drives product and service innovation.

Information DX


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Migration from one level to another is a journey that involves stakeholders throughout the enterprise and beyond. Table 2 shows all the information dimensions and subdimensions that require attention in order to build a robust foundation that host any digital initiatives.

As pointed out earlier, Information Transformation is at the core of digital transformation. Information Transformation directly feeds value into each of the other dimensions. Figure 1 illustrates how each of the dimensions of Information Transformation aligns with the four other dimensions of digital transformation — Leadership, Omni-Experience, WorkSource, and Operating Model.

Table 2: IDC Information Dimensions and Subdimensions

Data management & enterprise information model

Work Virtualization

Acquisition & Preparation

Analytics

Monetization

Algorithms

Productization

Program management

Real-time Orchestration

Quality

Services Innovation

IA services

Governance

Visualization

Security

Risk

Datafication

Integration & Synchronization

Knowledge Integration

Exploration

INFORMATION DIMENSIONS

INFORMATION ARCHITECTURE


DATA DISCOVERY

VALUE DEVELOPMENT

VALUE REALIZATION

SUBDIMENSIONS

Information DX


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Data resource management is the development and execution of architectures, policies, practices, and procedures that properly manage the full data life-cycle needs of an enterprise. Data models organize data elements and standardize how data elements relate to one another. In the realm of structured or semi-structured data, data models determine the structure of the data. For unstructured data, modeling approaches are looking at categorizations, clustering, or alternative information models. An enterprise information model (EIM) is essential.

Data integration is the combining of data from different sources to provide a unified view of these data. Though data integration traditionally targets data at rest, today, data in motion, the velocity of data creation, and the need to update a new dimension of data synchronization in real time have created the need to accelerate the timeliness of this integration. Another important aspect of data integration is the level of preservation of the raw data to enable further analysis. In many cases, data integration is complemented by data reduction, a process more accurately called data fusion. With the potential for advanced analytics to extract additional value from discarded information, this process of reduction becomes a key architectural question.

Information architecture services cover the scope and the level of support delivered to the enterprise and beyond in terms of education, information, repositories, and information expert support. The scope of audience varies from IT, business, and enterprises to partners. IA domains include transaction data, reference data, metadata, unstructured data, analytical data, and other Big Data.

Security covers all aspects of data and information security. Data should now be protected no matter where it resides, whether at rest or in motion. Security provides the means of protecting data from breaches and of ensuring data integrity. At a time when multiple apps and systems access the same data, it is necessary to design security with a data perspective so that the protection can be consistent, holistic, and in compliance with regulations.

INFORMATION ARCHITECTURE DIMENSIONS

DATA MANAGEMENT & ENTERPRISE INFORMATION MODEL

INTEGRATION & SYNCHRONIZATION

INFORMATION ARCHITECTURE SERVICES

SECURITY

Information Architecture

The information architecture is the structural design of shared information environments, the art and science of organizing and labelling websites, intranets, online communities, and software to support usability and findability, and an emerging community of practice focused on bringing principles of design and architecture to the digital landscape. Information architecture encompasses processes, tools, and practices aiming at managing information as an asset and delivering business value as the business evolves.

Information DX


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Where are you now?

Information DX

Try to describe how your orangization has designed its information architecture…

Go back to the self-assessment on page 5 and estimate your maturity for information architecture


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Data Discovery

Data scientists spend 50% to 80% of their days “data wrangling” for the business, or in other words, preparing digital data before it can be leveraged for useful insights. A data wrangler gathers or organizes disparate data sets collected by many investigators. The subdimensions of the data discovery dimension describe this process: acquisition & preparation, exploration, visualization, and datafication.

In the traditional world of enterprise data, where most data is created by enterprise systems, acquisition and preparation are scripted in the systems, which implement controls to ensure the quality of the data entry process. Raw data is typically collected from a variety of sources: transactional systems, logs, worksheets, hand-entered data, web pages scrapping, social feeds, video, audio, sensors, or machine-generated data. Data sources can provide a wide range of formats including text, Excel, XML, JavaScript Object Notation (JSON), or binary. Methods for handling this data have changed. Now, for richer value, raw data must not be processed, summarized, or manipulated in any way. All of these treatments directly affect the validity of statistical analysis. Even removing data from a data set changes the information. Preparation starts with a description of the data, including all anomalies, quality issues, or incomplete data. Integrating the data sources, with robust processes for capture, curation, validation, retention, and disposal, is an important part of effectiveness — getting relevant data — and efficiency — getting good data rapidly at an affordable cost.

Exploration involves understanding data properties, finding patterns, debugging analysis, and/or modeling data trategies. Source dictionaries may provide some information to help understand the data features and structures. Statistical summaries measuring mean, median, and spreads are established. Graphs and visualizations are very valuable as they supply quick and dirty representations, and it is vital to create a lot of them quickly to show comparisons, causality, mechanism, pplication, or systematic structure. In this phase, showing multivariate data (more than two variables) is valuable.

DATA DISCOVERY SUBDIMENSIONS

ACQUISITION & PREPARATION

EXPLORATION

Information DX

Information visualization has become essential to effectiveness in many parts of the information value chain. Visualization is used in the exploratory phase and in training, testing, or cross-validation to appreciate performance and in delivering the end result to the business user. The challenge of Big Data is to extract value from very large amounts of data; however, the result is still often too difficult or time consuming to understand. Visualization allows the organization to improve communication and shorten the understanding curve. However, representations cannot be the whole story and, when abused, can even obscure the conclusion. Mastering visualization is a learning curve; it is both art and science.

For some, datafication is about taking a process or activity that was previously invisible and turning it into data. For others, datafication is about turning an existing business into a data business. Datafication applies to many different entities: personality, business process, vehicle, or city. For example, the massive amount of information that can be extracted from a smartphone today goes far beyond the phone number, and the phone itself incorporates a knowledge set “datafying” the personality of the phone user. This datafication may tell more than anybody knows about this user, including the user himself or herself.

VISUALIZATION

DATAFICATION


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Where are you now?

Try to describe how your orangization supports data discovery…

Go back to the self-assessment on page 5 and estimate your maturity for data discovery

Information DX


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Value Development

While data volume is increasing exponentially, our capability to use this data is still very limited. IDC estimates that less than 5% of the usable data is valuable, or “target rich.” That percentage should more than double by 2020 as enterprises take advantage of new Big Data and analytics technologies and new data sources and apply them to new parts of the organization. Unlocking the value is the goal of value development and requires analytics, algorithms, program management, and quality.

Analytics covers the discovery and communication of meaningful patterns in data. While the analysis itself primarily consists of looking at data by slicing and aggregating it into meaningful reports, analytics uses statistics and mathematics to gain and derive information, develop knowledge, and create predictive models. Analytics covers many areas such as decision management, predictive analytics, risk analysis, fraud detection or correlation and causation research.

When it comes to information transformation, a family of algorithms brings disruption in how they help develop value from data. Specifically merging machine learning algorithms are giving programs the ability to learn without being explicitly programmed. Machines learn by using input data, translating data into abstraction, and creating generalizations. The choice of a model for data abstraction is typically not left to the machine but is made by a data scientist. Usually, one or more models are trained, tested, and cross-validated to reach the right model for the generalization phase. Today, machine learning powers voice, handwriting, and facial recognition. It’s also enabled systems that autonomously pilot helicopters and cars, using the most efficient routes. Machine learning has also enabled medical diagnostics, DNA analysis and has many more functions.

Program management includes the process of managing several related projects. In terms of information transformation, program management addresses larger and more complex challenges. Program management requires a well-defined organizational structure and strong leadership, with the challenge of dealing across and outside the enterprise. At the intersection of legacy data, cloud-based data, acquired data, and open source data, the success rate of information initiatives is a direct result of the ability to properly manage these initiatives with a strong cross-organization governance, multidisciplinary team, and development and implementation methodology able to support continuous delivery.

VALUE DEVELOPMENT SUBDIMENSIONS

ANALYTICS

ALGORITHMS

PROGRAM MANAGEMENT

Information DX

Data quality attributes such as accuracy, integrity, consistency, completeness, validity, timeliness, and accessibility need to be addressed. The traditional approach to data quality and these attributes looks at how well the data fits the purpose. Although this remains valid for most enterprise-generated structured data, external and unstructured data require a different assessment: preserving the integrity of source data or using transformed data. Quality is also a concern for analytics and data science because experts risk making incorrect conclusions when using low quality data. The ultimate standard for validating data science is the replication of findings. However, replication is usually unaffordable or even impossible with Big Data. Organizations must implement a process that improves the overall quality of outputs.

QUALITY


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Where are you now?

Try to describe how your orangization drives value development from information…

Go back to the self-assessment on page 5 and estimate your maturity for value development

Information DX


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Value Realization

Realizing the value of data focuses on monetization, productization, real-time orchestration, and service innovation. Most companies are already participating as data buyers, brokers, or sellers. 70% of large organizations already purchase external data; IDC predicts that 100% will do so by 2019.

Monetization is the generation of revenue from information or data, the process by which data is sold or exchanged in an information value chain. Several actors play in this chain: data producers, data aggregators, and data consumers. While only some companies will initially consider participating in this activity as sellers, most companies will participate on the buyer side or be part of other organizations’ data supply chain. Managing information as an asset is key to monetization. Understanding the data market and how to build data valuation rules is essential. Data monetization, however, requires a clear understanding of the data market and data valuation. For example, a company should understand the financial value of data and how it contributes to the revenue generated. It should also know the worth of data confidentiality compared with potential direct income from sharing or selling it.

Productizing data comes in two main forms: data-driven products and data products. Data-driven products are products that are significantly enhanced by the use of data. For example, Netflix and Amazon use data analysis to provide valuable recommendations. Another word for this is “wrapping,”; i.e., exchanging data for increased revenue from core products and services. A data product is created primarily in the packaging of data in a way that a consumer is ready to purchase: it incorporates visualization, intelligence, insightful dashboards, and experience.

Real-time information represents a value by itself with an exceptional premium because other alternatives are often poor substitutes. What is the value of knowing real-time information that somebody is breaking into your house? What is the value of a GPS navigation system compared with a printed itinerary? What is the value of real-time traffic information and integrating that traffic information with your schedule to have a dynamically updated reminder? Real time is far more than an operational improvement; in most cases, real time creates a unique value or an enhanced experience and serves as the foundation for business differentiation or transformation.

Information Transformation brings a mine of new possibilities in every aspect of the service: delivery, value, outcome, costing model, or risks. Most of the data that a company owns can potentially be leveraged to create a service and enhance an existing product or service with better outcome or value such as self-service reporting, personalization, recommendations, personal history access, collaboration, best practice sharing, and smarter services. Combining company proprietary data with external data and a user’s own data from a mobile data also opens many innovative possibilities. By transitioning aspects of a product from physical to digital, the need for physical version may be eliminated (e.g. books or printed pictures). Most innovative companies create separate organizations or labs to succeed.

VALUE REALIZATION SUBDIMENSIONS

MONETIZATION

PRODUCTIZATION

REAL-TIME ORCHESTRATION

SERVICE INNOVATION

Information DX


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Where are you now?

Try to describe how your orangization deals with value realization…

Information DX

Go back to the self-assessment on page 5 and estimate your maturity for knowledge & collaboration


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Knowledge and Collaboration

Digital transformation has revolutionized knowledge and collaboration. Many users of smartphones employ an augmented reality instead of traditional communication: they may exchange information on their device even when physically facing their interlocutor. Technology can greatly enhance the access, sharing, and integration of knowledge.

Work virtualization converts all aspects of work including physical aspects to a computer-generated simulation of reality. Work virtualization focuses on data and information aspects. Virtualization provides not only a replacement of the physical reality but also additional possibilities in an augmented reality. Work virtualization requires a rich collaboration platform, business rules, technical support, and a robust digital collaborative culture. Work virtualization may range from simply enabling remote work to an extended working experience including an endless possibility of tools and functionalities. In addition, as for all digitally enabled experiences, information can automatically be captured or recorded.

Knowledge integration is the process of synthetizing multiple knowledge representations or models into a common one. Knowledge integration is a continual and real-time process that must incorporate new information into a body of existing knowledge while determining how the new information interacts. This integration of knowledge represents multiple perspectives and interpretations in a consistent whole. With social business and Massive Open Online Courses (MOOCs), the knowledge universe is expanding at increasing speed.

Governance covers data, information, and knowledge. Governance is the set of structures, stakeholder accountabilities, procedures, policies, and processes supporting all the enterprise needs to manage and protect these valuable assets. Data governance traditionally focused on standards, definition, integrity, or quality throughout the data supply chain from entry to business outcome, with a primary focus for data quality at the capture and entry phase. In the world of Big Data, however, data governance requires a new, adapted approach. The focus is more on the business outcome, which ultimately dictates appropriate levels of controls, data preparation, and validity.

While risk concerns are often now an integral part of transaction and reporting systems, the power of new tools and the ability to correlate data across many data sets unleashes new risks. When bringing a data set in-house, a company may easily expose itself to privacy and legal issues. The source of data must be investigated to understand if it is open data, free for the specific use, paid for the specific use, or restricted or even illegal. Anonymizing data limits exposure but is a weak solution. A comprehensive approach — including rules for vetting, segregation, location, retention, and auditing — must be implemented, and it must address all types of data.

KNOWLEDGE & COLLABORATION SUBDIMENSIONS

WORK VIRTUALIZATION

KNOWLEDGEINTEGRATION

GOVERNANCE

RISK

Information DX


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Where are you now?

Try to describe how your orangization deals with Knowledge and Collaboration…

Information DX

Go back to the self-assessment on page 5 and estimate your maturity for data discovery

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1 2


3 4

Welcome! In this third section, you will be provided with an elaborate overview of checklists for a six, twelve and twenty-four-month action plan across all information dimensions and subdimensions. Use the checklists to create your own action plan that you can focus on over time.

INFORMATION MANAGEMENT REBORN

CHECKLIST: INFORMATION ARCHITECTURE

CHECKLIST: DATA DISCOVERY

CHECKLIST: VALUE DEVELOPMENT

CHECKLIST: VALUE REALIZATION

CHECKLIST: KNOWLEDGE & COLLABORATION


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Disruptive CIO

Now that you’ve performed a baseline assessment of your maturity across the five information transformation dimensions, you should have a rough idea where the gaps are in your information strategy and what you need to focus on. The tables below provide a comprehensive overview of potential activities you can consider for your action plan (page 5). As you go through the check lists, identify the action items that will be relevant for your information transformation objectives. Note that the checklists describe activities that need to be pursued over a six, twelve or twenty-four-month period.

Map enterprise information

Design and implement a basic information architecture

Develop a standard process for extracting business insights

Start identifying all internal and external sources of both structured and unstructured data

Champion a data quality initiative

Audit data security and compliance

Assess the market for data-enhanced products

Champion an initiative to create or improve dataenhanced products or data products

Educate both business and IT leaders about the benefits and importance of information

Develop and enforce information governance and compliance with a community of practice

Champion a knowledge-centric culture and provide a collaboration platform that enables continual global cooperation

Develop advanced analytics capabilities including infrastructure, PMO, and quality centrally

Share analytics capabilities with business functions to drive engagement

Set a priority to enhance all products, services, and solutions with data

Make extraction of data value a priority for all products and services

Democratize advanced analytics capabilities including tools, training, and program management

Set a goal that information represents the highest-value differentiator for all products and services in the company offering.

Develop processes, standards, and infrastructure to support semistructured and structured data

Acquire data scientist skills and create a basic platform for experimentation, evaluation, and knowledge acquisition

Manage datasets as a portfolio of assets

Extend advanced analytics to enterprise wide capabilities

Start exploring realtime predictive analytics and machine learning

Develop an information Architecture that includes internal and external data sources

Optimize information architecture to cover all data varieties, volume, and velocity




DATA DISCOVERY

VALUE DEVELOPMENT

VALUE REALIZATION

6 months 12 months 24 months

CHECK LIST: Information Transformation


29

Map out all enterprise information

Identify areas of consolidation of business-critical data

Assess risk levels for consolidation

Launch initiatives to standardize, consolidate redundant data, and improve quality

Define and publish the vision and guiding principles for the information architecture

Align the design to business objectives

Establish an EIM team to influence application development and integration

Create inventory with security control for all data in an enterprise dictionary – not just structured data

Develop a security policy for data varieties, both regulatory and arbitrary data

Encrypted repository of critical data resources

Methods, procedures, and access privileges tracked

Ongoing assessment and standardizing of Big Data collection handling and compliance with public document source

Data protected at rest, in motion, and in use

Master directory and auditing of all data resources in compliance with corporate systems and development specifications; ensured compliance for all sources

Publish all artifacts through a repository

Champion and write up best practices and standards

EIM team influences SaaS and vendor selection

Include information and parameters on data value and risk

Deliver autonomous access to all stakeholders, including partners

EIM team drives application and product design

Consolidate data sources

Centralize data management with a corporate framework

Develop a data service layer that maintains a service repository with embedded synchronization

Support all data formats as dictated by regulatory practices

Move all applications to a single, comprehensive master data repository with stewardship and KPIs

Enforce information management on varieties of data with toolsets

Introduce automated oneand two-way synchronization, duplication checks, and validation through a service layer


DATA MANAGEMENT & ENTERPRISE INFORMATION MODEL (EIM )


INFORMATION ARCHITECTURE (IA) SERVICES

SECURITY


Disruptive CIO

CHECK LIST: Information Architecture


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Introduce company guidelines for data acquisition and preparation

Move data sourcing responsibility from individual to project development

Create a standardized platform or leverage IaaS resources to data scientists to conduct exploration

Acquire data analytics visualization software to harmonize reports and dashboards

Introduce basic visualization techniques for reporting systems and dashboards

Align digital initiatives to (digital) persona, business concepts, or business processes to drive data collection

Digital initiatives are introduced to support data collection and analysis

Communicate and operationalize findings and implications of data

Introduce the datafication of persona, services, products as a way to drive business value by delivering a differentiated experience or business service

Educate company executives on information visualization and interpretation

Distribute information visualization standards and guidelines across the enterprise

Launch an expert team with advanced visualization

Distributed teams provide flexible business support

Introduce data sourcing procedures, platforms and portfolios to the organization

Data sourcing and preparation is documented for all projects

Launch multi-disciplinary teams to conduct data exploration within an industrial class standard environment including directory, library, tools, and documented procedures

Develop a portfolio of datasets available to data scientists and business analysts to source all data needs

Offer data sourcing and preparation as a service, with realtime provisioning of tools and expertise

Focus on knowledge sharing by introducing coaching and mentoring programs to talents

Top experts drive data exploration with partners or customers

DATADISCOVERY

ACQUISITION &PREPARATION

EXPLORATION

VISUALIZATION

DATAFICATION


Disruptive CIO

CHECK LIST: Data Discovery


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Educate business executives on the types of statistics/ analytics to drive decision making

Start developing data models encompassing market, revenue, and financials from statistical inference

Deploy a central analytics infrastructure or IaaS to give model builders ample computing resources to work with large batches of data

Expand traditional program management office (PMO) to address IT projects, digital initiatives, and analytics

Record and share best practices across the enterprise

Assess data quality across the organization

Start defining data guidelines for all enterprisestructured and ecosystem data

Create a data quality framework that includes policies, governance, and tools for all ecosystem data

Launch an enterprisewide data quality framework that supports continual improvement and real-time business-driven performance

Make calculating business outcomes, resources, risks, and progress default to program deliverables

Start communicating achievements across the enterprise to harmonize various practices

Introduce “agile” program and portfolio management in processes, talent sourcing, and platforms

Focus on continual delivery of innovative projects and retirement of obsolete systems

Introduce forecasting and predictive analytics on internal and external data using correlations and multivariate analysis

Infrastructure team operates infrastructure and provides IaaS

Start building a basic API library

Design and share instrumentation with functions

Start trialing with prescriptive analytics to suggest decision options to leverage predictive analytics combined with impact analysis and performance evaluation

Expand library of APIs

Define service levels for supporting Big Data sets with real-time performance capability

VALUE DEVELOPMENT

ANALYTICS

ALGORITHMS

PROGRAM MANAGEMENT

QUALITY


CHECK LIST: Value Development

Disruptive CIO


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Work with the business to define and create a data valuation and monetization strategy

Identify the most valuable information for every business function

Experiment with product management datadriven enhancements or initial data product creation

Map out what business areas will benefit from realtime operations

Assess real-time orchestration aptness on information processes, storage, motion, and access systems

Establish a service innovation community

Introduce basic innovation management processes to business executives

Launch a dedicated innovation management unit including budget, personnel, and infrastructure

Move to agile service innovation life cycle processes to enable fast turnover

Launch an autonomous digital innovation lab

Include innovation lab in all internal and external marketing communications to secure participation and/ or joint ventures

Acquire infrastructure and resources to embed real-time operations

Introduce real-time data processes for mission-critical requirements

Embed real-time orchestration of information (including processes, storage, motion, and access) into products and services for customers and partners

Start treating information as an asset by calculating financial value

Introduce a companywide approach to information valuation

Champion best practice and competitive edge

Start introducing data enhancements to all products, services, and solutions

Ensure customers understand and leverage data enhancements and insights

Make the assessment of data value a mandatory practice

Introduce return on information (RoI) metrics for business to use

Differentiate existing and new portfolio of products and solutions through its data enhancements

VALUE REALIZATION

MONETIZATION

PRODUCTIZATION

REAL-TIMEORCHESTRATION

SERVICE INNOVATION


CHECK LIST: Value Realization

Disruptive CIO


33

Introduce flexible working practices by supporting offline work capabilities for office functions

Move traditional communications tools to digital alternatives

Start integrating a knowledge base and communities of practice by aligning and consolidating repositories of structured and unstructured data

Define governance framework on a role-based approach, applying this to access, protection, ownership and compliance

Assess all data risks and privacy concerns of enterprise transactions systems and reporting systems

Evaluate GDPR compliance and potential threats

Create a comprehensive overview of risks related to the vetting, segregation, location, retention, and auditing for all types of data

Embody common practices for managing risks across transactional systems, social media data, machinegenerated data

Govern information by ensuring IT and business accountability

Define range of policies and penalties for full stakeholder involvement

Centralize governance and provisioning to support speed of business change

Unify business communication platforms to allow employees to perform their work at any place, any time, as part of a global team and with access to all needed information

Integrate knowledge platform with persona access policies, subscription, communities, and decision-making tools

Launch virtualized devices, clients, apps, and data to enable a full omniexperience with continuous synchronization, whether tethered or offline

Extend knowledge management center from frontend to back-end or vice versa


WORK VIRTUALIZATION


GOVERNANCE

RISK


CHECK LIST: Knowledge & Collaboration

Disruptive CIO


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1 2


3 4

Welcome! This short, final section provides a light framework for setting the first project plan and introducing the concept of Leading in 3D. By now, you should be well on your way to becoming the disruptive CIO!

PROJECT FRAMEWORK

LEADING IN 3D


35

Leading in 3D

YOUR PROJECT PLAN

Use this project plan template as a framework to document the details and various elements of your Information Transformation maturity project. You can leverage your action plan and information maturity benchmark in Section 1 as a way to get the buy in you need from the rest of your organization. It will also come in handy when engaging supporting partners.

Phase 1: Digital Strategy

• Identify the digital business strategy for your organizations

• Align the business strategy to your IT goals

• Define the measures for success

Phase 2: Data at the Core

• Identify the information you need

• Map the data sources that can deliver this

• Identify the risks associated with change

Phase 3: Preparing yourself

• Assess the talent you need and have

• Assess your current processes

• Estimate the resources you require to realize your goals

• Master the tools you need (e.g., distributed computing, data management)

Phase 4: Operationalize

• Anticipate and connect to new digital initiatives from the business

• Automate legacy data processes as much as you can

• Introduce data-driven innovation resting in your information environment

• Revisit this workbook from time-to-time to assess your progress


36

BE A DISRUPTIVE CIO BY LEADING IN 3D

Make sure you use the checklists, maturity model, and guidelines that are outlined in this

workbook to enable your organization to successfully execute on this digital transformation

strategy. Wherever you are on your digital journey, and no matter the size of your project, with

this in your arsenal we’re confident you can address the many challenges coming at you. Just

don’t forget to be tactful with your resources and stay focused on developing processes and

skills that are transferable, scalable, and constantly improving. Maintain a long-term focus,

combined with short-term objectives to support all digital transformation requirements as the

organization matures.

At IDC we call this digital transformation leadership framework “Leading in 3D.” It allows

enterprises to enable transformative innovation while driving operational excellence —

incorporation. This approach depends on the art of integration: managing a continuous 50

exchange of lessons learned and technology developments achieved. It is now up to you to

navigate yourself around the many pitfalls and maintain a strong commitment to your vision

for this project.

Leading in 3D


37

Next Steps?Ready to start applying the things you’ve learned? Or do you want to explore the realm of

Information Transformation a bit more?

To find out more click here

Leading in 3D

https://www.sap.com/cmp/dg/crm-xm17-gtm-ccp-ita01/index.html

38

No single coherent information architecture

Local and insulated approach to data management

Integration is driven by applications

Rare documenta-tion, process,workflow, orartifacts helpbusinesscope withfragmentedinformation

Data mainlyprotected bycontainment;minimalsecurityapproachdriven by aneffort tosatisfyregulatorycompliance;little control ofdata; limitednumber ofdatabasesprotected

Information architecture framework targets basic operational structured data

Enterprisewide consolidation of select business critical data; data consolidationeffort

Multiple initiatives to standardize, consolidate redundant data, and improve quality

Informationarchitecturevision andguidingprinciples arepublished.Design is alignedwith businessobjectives. Aknowledgeableteam is in place.EIM influencesapplicationdevelopment

Informationinventory withsecurity controlfor all data inenterprisedictionary —structured dataonly

Information architecture framework includes business critical patterns for Big Data and analytics

Centralized data management with corporate framework is in place. Data stewardship and KPIs maintain quality

All applications are integrated through a service layer that maintains a service repository with embedded synchronization; interoperability of data formats as dictated by regulatory practices

Information architecturepublishes all artifactsthrough a repository;center of excellencechampions best practicesand promotes standards;holistic approach toinformation; EIMinfluences SaaS andvendor selection

Data protected at rest, in motion, and in use; shared, encrypted repository of criticaldata resources;methods, procedures,and access rivilegestracked; ongoingassessment andstandardizing of Big Data collection handling andcompliance with public document source

All applications and repository refer to a single, comprehensive master data repository with stewardship and KPIs

Synchronization, data duplication checks, and data validation are automated through a service layer. Data rules built into master data tool support dynamic interoperability and adaptation to changes

Informationarchitectureprinciples aredynamically alignedwith businesspriorities andobjectives; IArepository includesinformation valueand risk; auto-mated access foall stakeholders,including partners;EIM drivesapplication andproduct design

Defense in depthapplied to data;master directoryand auditing of alldata resources incompliance withcorporate systemsand developmentspecifications;ensured compliancefor all sources

Information architecture encompasses volume, velocity, and a variety of capabilities for both structured and unstructured data

Information architecture underpins digital business transformation, supporting the full range of dynamic capabilities of velocity, variety, and volume

Data management encompasses all data and information variety with toolsets

Real-time and dynamic integration of information at the speed of business change; supports streaming data and real-time monitoring data

Informationarchitectureservices aredelivered as selfservicein realtime and coverholisticinformationdomains for andwith allstakeholders

Seamlessintegration ofsecurity andregulatorycomplianceenables bothbusiness andpersonal usage inan integratedexperience


DATA MANAGEMENT & ENTERPRISE INFORMATION MODEL (EIM)


INFORMATION ARCHITECTURE (IA) SERVICES

SECURITY

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Only structured transactional data is managed and analyzed methodically

Data acquisition and preparation are individuals’responsibilitywith no companyguidelines

Data exploration is left to individual experts

Visualization is unmanaged and left to individual judgement

Data is collected without a unifying strategy

Data warehouse and operational data store are in place. Web and content management targetunstructured data

Data sourcing andpreparation are partof project development and project managementfor enterprise systems. Businessintelligence andother data expertteams have theirown standards andtoolsets.

Standardized platform or infrastructure as a service is provided to enable data scientists to conduct exploration

Basic, and largelystatic, visualizationtechniques are usedfor reporting systemsand dashboards.Office suite is theplatform of choice

Company identifiesd i g i t a l pers ona,business concepts, or business processesthat can focus on itsdigital efforts.

Data sourcing procedures, platforms,and sourcing portfolios are in place. Data competency center champions standards, methods, and education.Sourcingand preparation aredocumented for allprojects.

Multidisciplinaryteams conduct dataexploration within an industrial-class standard environmentincluding directory,library, tools, anddocumented procedures

Information visualizationexpertise is availableas a competency center.Company executivesare educated on information visualization and interpretation.Information visualization standards and guidelines are available throughout the enterprise.

Business process, business concept, digital persona, or other business entities are defined and systematically targeted for data collection andanalysis. Conceptsare communicated and operationalized.

Portfolio of datasets, data extraction and proparation toolset, and preparation environment allows data scientists and business analysts to source all data needs efficiently and rapidly

Experiencedmultidisciplinaryteams conduct dataexploration andcoach other teamssupported by anenvironment provided as a service that includes directory, library, tools, and automated procedures

Advanced visualization capability with expert team provides state-of-the-art information visualization. Distributed teams provide flexible business support.

Datafication providesacompetitive advantage byproviding a betteruser experience,process value, and/or other holisticsubject knowledgeadvantage.

Top experts drivedata explorationsupported by astate-of-the-art comprehensive data exploration environment provided as a service and shared across the enterprise and withpartners or customers

Real-time advanced visualization capability with interactivity is supported by expert teams that provide state-of-the-art information visualization. Distributed teamsprovide flexiblebusiness support.

Datafication allows creation of unique business value through delivering a differentiated experience or business service

Data Discoveryplatform includes data storage strategy, standard toolset for data acquisition and preparation, exploration,visualization, andaccess to competentdata skills

Data Discovery environmentprovides data setsfro multiple sources and powerfultoolsets to uncovervalue both for datascientists and forbusiness analysts

Data Discovery is aservice thatincludes real-timeprovisioning of allneeded resourcesincluding data sets,expertise, andtools.

Data acquisition and data preparation are provided as a service that includes real-timeprovisioning of allneeded resourcesincluding data sets, expertise, and tools


DATADISCOVERY

ACQUISITION & PREPARATION

EXPLORATION

VISUALIZATION

DATAFICATION

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Information value is provided in hindsight

Descriptive analytics is analytics focusedon reportingthe past

Insulated experimentation at departmental level on smal data sets

Program management addresses IT projects, but analtics projects use their own approach independently

Data quality rulesare embedded inother quality programs

Information enhances value stream, but information’s intrinsic value is not assessed

Diagnostic analytics is basic analyticsthat inform decision making

Central analytics infrastructure provides model builders with computing resources and theability to work onlarge data sets inbatch mode

Program management is arecognized priority,and a PMO champion implements best practices acrossthe company

Enterprisewide data quality is defined for enterprise-structured data. Guidelines exist forall other data.

Information ismanaged as an asset, and its intrinsic value is established

Reporting and decision analytics provide insight to improve operational efficiency and encompass markert, revenue, and financials from statistical inference

Enterprisewide analytics infrastructure operated by theinfrastructure teamprovides PaaS or IaaS for Big Data sets — dashboards and instrumentation and basic API library

An enterprisewideapproach includes all aspects from prioritization of program deliverables to the achievement ofbusiness benefits while managing resources, mitigatingrisks, and reportingand communicatingprogress

An enterprisewidedata quality framework includes policies, governance, and tools. Scope covers all data variety.

Information provides competive advantage by adding proven value to all company products, solutions, and services

Predictive analyticsand statisticalanalysis of internaland external dataallow insightfulcorrelations andassociations

Federated analyticsinfrastructure provides basic and advanced capabilities to thebusiness organi-zation. A large library of APIs and different service levels support BigData sets with realtime performancecapability

VC-like portfoliomanagement, failfast and succeedfast agile processes,dynamic and flexible talent sourcing, andcorporate portfolioand program management platforms

An enterprisewidedata quality framework is instrumented, and quality is monitored with KPIs.

Information represents the highest value differentiator for the company.

Prescriptive analytics suggests decision options to leverage predictive analytics combined with impact analysisand performanceevaluation

Distributed analytics infrastructure witha large library ofmodels and resourcesempowers a BigD a t a f a c t o r ymodel. Service ismonetized withecosystem.

Agile program andportfolio management enalbles a continual delivery of innovative projects and retirement of obsolete systems

An enterprisewidedata quality frameworks is in place, with emphasis on continual improvement andreal-time business driven performance

VALUEDEVELOPMENT

ANALYTICS

ALGORITHMS

PROGRAMMANAGEMENT

QUALITY

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Data value isfrom operationsand business process automation

No monetizationstrategy. Dataacquisition isunsupervised.

Company doesnot leveragedata to enhanceits offerings

Real-time is nota company priority

Service innovation is left to the service managementteams

Information valueis from businessprocess improvement andvalue accretionthroughout thevalue stream

Immature datavaluation and monetization strategy; data procurement policy is in place; identification of most valuable information

Experimentationwith data-drivenenhancement orinitial data product creation

Complexenvironment doesnot allow realtimeoperationsexcept for selectand limited businessprocesses

Service innovationis encouraged withsome Web site andcollaboration communities in place. Some service innovation initiatives are championed by business executives.

Information managed as an asset; companywide approach to information valuation; mandatory assessment of datavalue; procurement policy for data acquisition and data marketknowledge

All products, services, and solutions are enhanced with data

Real-time orchestra-tion of information processes, storage, motion, and access is available for business-criticalneeds

Business and ITleadership areformally committed toservice innovation.Innovation labincludes budget,personnel, andinfrastructure.Service innovation portfolio’s managed at the exrcutive level.

Companywide management of information as a asset; a business team with an understanding of data market champions best practices; evaluation of a business’ return on information

Data-driven enhancement to allproducts andsolutions provide acompetive advantage to thecompany. Pure dataproducts are bestsellers.

Real-time orchestration of information processes, storage,motion, and accessprovides a competitive advantage and enhanced customer experience

Service innovationcreates some competitve advantage.The company innovationlab is publicizedexternally and benefits from external participation and/or joint ventures. Service innovation life cycle enalbles fast operationalization.

Company’s line of products and solutions is differentiated by data-driven enhancements that enable the companyto lead its markets

Real-time orchestration of information processes, storage,motion, and accessis available at allexternal touch points, providing ongoing market leadership and competivedifferentiation

Continual serviceinnovation withreal-time operationalizationof new services;company leads as arecognized top service innovator with ongoing publicized successes

Value is from operation optimization, real-time orchestration, and business modelinnovation

Value is from businessmodel innovation,digital fulfillment,product enhancement, anddigital products

Value is realized through agile business model innovation, and digital value is added to all company offerings

Companywide management of information as an asset is deeply embedded in the culture.Management drivesthere turn-on-information asset as competitiveadvantage

VALUE REALIZATION

MONETIZATION

PRODUCTIZATION

REAL-TIMEORCHESTRATION

SERVICEINNOVATION

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Knowledge sharing and collaboration are left to the individual, withstandard communicationcapabilities inplace

Limited capabilityto work remotelybeond basic information exchange

Knowledgeframework

No common governance beyond limited policy

Basic data risk isaddressed mostlyby basic policies

Knowledge sharingand collaborationplatform s are provided, with limited guidance

Basic communication work elements are digitized needs and allow some complemented withoffline workcapabilities

Clearly definedgovernance with arole-based approach to access, protection, ownership, and compliance

Risk and privacyconcerns areintegral part oftransactions systemsand reportingsystems. Big data/social data/externalsource exposure isunassessed

Repositories

Knowledge management framework withgovernance andleadership supportcreates a collaborative culture

Business and communication platforms allow workers to performtheir work at anyplace, any time, aspart of a globalteam and with access to all needed information

Integratedknowledge base andcommunities ofpractice

Strong governancewith IT and businessalignment; a fullrange of policiesare in place andautomated; fullstakeholder involvement

There is a comprehensive approach, includingvetting, segregation, location, retention,and auditing for alltypes of data

Knowledge-centricculture with powerful collaboration platform enablescontinuous globalcollaboration andaccountability

In addition to office environment, mobile platformsallow workers toperform all tasks intethered mode

Integrated knowledge platform with profiles, subscription, communities, and decision-making tools

Strong governanceestablishes trust ininformation withclearly defineddegree of confidence and ofcompliance

Organization structured with automation manages risks fortransactional systems, social media data,machine-generateddata, and all theircombinations and/or associations

Information governance is integrated in allaspects of the business processesat the speed ofbusiness change

Seamless management of risk enables the company to use information,including streaminginformation, in realtime

Comprehensive virtualization of devices, apps, anddata enable a workfull of omni-experience with continuoussynchronization,whether tethered oroffline

Advanced knowledge processes, platforms, and tools

Real-timecollaboration andknowledge sharing,both internally andexternally, withpowerful knowledgeaccess algorithms

KNOWLEDGE& COLLABORATION

WORKVIRTUALIZATION


GOVERNANCE

RISK

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About IDC

International Data Corporation (IDC) is the premier global provider of market intelligence,

advisory services, and events for the information technology, telecommunications and

consumer technology markets. IDC helps IT professionals, business executives, and the

investment community make fact-based decisions on technology purchases and business

strategy. More than 1,100 IDC analysts provide global, regional, and local expertise on

technology and industry opportunities and trends in over 110 countries worldwide. For 50

years, IDC has provided strategic insights to help our clients achieve their key business

objectives.

IDC is a subsidiary of IDG, the world’s leading technology media, research, and events

company. Further information is available on our websites at www.idc.com

About SAP

As the market leader in enterprise application software, SAP is at the center of today’s business

and technology revolution. SAP helps you streamline your processes, giving you the ability to

use live data to predict customer trends – live and in the moment. Across your entire business.

When you run live, you run simple with SAP.

For more information, visit www.sap.com

Documents

Information Transformation Workbook - SAP … · Information Transformation Workbook 1 Sponsored by ... business executives ... from factory to shelf — “Turning diapers into insights,”