Building an Effective & Extensible Data & Analytics Operating Model

Cognizant 20-20 Insights

Digital Business Building an Effective & Extensible Data & Analytics Operating Model To keep pace with ever-present business and technology change and challenges, organizations need operating models built with a strong data and analytics foundation. Here’s how your organization can build one incorporating a range of key components and best practices to quickly realize your business objectives. Executive Summary

To succeed in today’s hypercompetitive global economy, the gap between their data strategies, day-to-day operations organizations must embrace insight-driven decision-making. and core processes. This is where an operating model can help. This enables them to quickly anticipate and enforce business It provides a common view/definition of how an organization change with constant and effective innovation that swiftly should operate to convert its business strategy to operational incorporates technological advances where appropriate. The design. While some mature organizations in heavily regulated pivot to digital, consumer-minded new regulations around sectors (e.g., financial services), and fast-paced sectors (e.g., data privacy and the compelling need for greater levels of retail) are tweaking their existing operating models, younger data quality together are forcing organizations to enact organizations are creating operating models with data and better controls over how data is created, transformed, stored analytics as the backbone to meet their business objectives. and consumed across the extended enterprise. This white paper provides a framework along with a set of Chief data/analytics officers who are directly responsible for the must-have components for building a data and analytics sanctity and security of enterprise data are struggling to bridge operating model (or customizing an existing model).

August 2018 Cognizant 20-20 Insights

The starting point: Methodology

Each organization is unique, with its own Preliminary step: The pivots specific data and analytics needs. Different sets of No two organizations are identical, and the capabilities are often required to fill these needs. For operating model can differ based on a number this reason, creating an operating model blueprint is of parameters — or pivots — that influence the an art, and is no trivial matter. The following systematic operating model design. These parameters fall into approach to building it will ensure the final product three broad buckets: works optimally for your organization. ❙❙ Design principles: These set the foundation Building the operating model is a three-step process for target state definition, operation and starting with the business model (focus on data) implementation. Creating a data vision followed by operating model design and then statement, therefore, will have a direct impact architecture. However, there is a precursory step, on the model’s design principles. Keep in mind, called “the pivots,” to capture the current state and effective design principles will leverage all extract data points from the business model prior to existing organizational capabilities and resources designing the data and analytics operating model. to the extent possible. In addition, they will be Understanding key elements that can influence the reusable despite disruptive technologies and overall operating model is therefore an important industrial advancements. So these principles consideration from the get-go (as Figure 1 illustrates). should not contain any generic statements, like The operating model design focuses on integration “enable better visualization,” that are difficult to and standardization, while the operating model measure or so particular to your organization architecture provides a detailed but still abstract view that operating-model evaluation is contingent of organizing logic for business, data and technology. upon them. The principles can address areas In simple terms, this pertains to the crystallization of the such as efficiency, cost, satisfaction, governance, design approach for various components, including technology, performance metrics, etc. the interaction model and process optimization.

Sequence of operating model development

Preliminary Step 1 Step 2 Step 3 The pivots Business Operating Operating model (current state) model model architecture

Figure 1

2 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

❙❙ Current state: Gauging the maturity of data and Current-state assessment captures these related components — which is vital to designing details, requiring team leaders to be cognizant of the right model — demands a two-pronged these parameters prior to the operating-model approach: top down and bottom up. The reason? design (see Figure 2). The “internal” category Findings will reveal key levers that require attention captures detail at the organization level. and a round of prioritization, which in turn can ”External” highlights the organization’s focus move decision-makers to see if intermediate and factors that can affect the organization. And operating models (IOMs) are required. “support factor” provides insights into how much complexity and effort will be required by the ❙❙ Influencers: Influencers fall into three broad transformation exercise. categories: internal, external and support.

Operating model influencers

Internal External Support factor

• Data & analytics vision • Value proposition • Change impact index (employee) • Geography, spread • Customer/business & culture segment & communication • Technology landscape channels • Organization setup • Revenue & headcount (flat vs. consensus; • Competition (monopoly product-driven vs. vs. oligopoly) • Management commitment function-driven) and funding • Regulatory influence • Position in value chain

Figure 2

3 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

First step: Business model of this step is not a literal model but a collection of data points from the corporate business model and A business model describes how an enterprise current state required to build the operating model. leverages its products/services to deliver value, as well as generate revenue and profit. Unlike a corporate business model, however, the objective Second step: Operating model here is to identify all core processes that generate The operating model is an extension of the business data. In addition, the business model needs to model. It addresses how people, process capture all details from a data lens — anything that and technology elements are integrated and generates or touches data across the entire data standardized. value chain (see Figure 3). ❙ Integration: This is the most difficult part, as it We recommend that organizations leverage one connects various business units including third or more of the popular strategy frameworks, such parties. The integration of data is primarily at as the Business Model Canvas1 or the Operating the process level (both between and across Model Canvas,2 to convert the information processes) to enable end-to-end transaction gathered as part of the pivots into a business model. processing and a 360-degree view of the Other frameworks that add value are Porter’s Value customer. The objective is to identify the core Chain3 and McKinsey’s 7S framework.4 The output processes and determine the level/type of

The data value chain

Business value

Data acquisition/ Data Data preparation/ Data ingestion/ Data modeling/ Analytics & Data generation provisioning data synthesis data integration reporting Visualization

Enterprise data management Enterprise data analytics

Figure 3

4 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

Integration & standardization

Business process/data domain mapping Data map: ﬁeld services Customer Product Equipment Order Work management Billing & invoice Employee Location Transmission & distribution Field services x x x x Metering x x x Installation services x x x x x x x Services Customer interaction x x x x x Billing & payment processing x x x Customer administration X x x x x Service request management x x x x x x x Third-party settlement x x x x Receivables management X x x

Data standardization requirements Cross-functional process map

Customer

Product

Equipment

Order

Work management

Billing & invoice

Employee

Location

Figure 4

integration required for end-to-end functioning (the process) or how (data generation process) to enable increased efficiency, coordination, within the enterprise. Determine what elements transparency and agility (see Figure 4). in each process need standardization and the extent required. Higher levels of standardization A good starting point is to create a cross-functional can lead to higher costs and lower flexibility, so process map, enterprise bus matrix, activity- striking a balance is key. based map or competency map to understand the complexity of core processes and data. In our experience, tight integration between processes and Creating a reference data & analytics functions can enable various functionalities like self- operating model service, process automation, data consolidation, etc. The reference operating model (see Figure 5) is customizable, but will remain largely intact at this ❙❙ Standardization: During process execution, level. As the nine components are detailed, the data is being generated. Standardization ensures model will change substantially. It is common to see the data is consistent (e.g., format), no matter three to four iterations before the model is elaborate where (the system), who (the trigger), what enough for execution.

5 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

For anyone looking to design a data and analytics objects and relations over time, such tools will help operating model, Figure 5 is an excellent starting greatly in maintaining the operating model. point as it has all the key components and areas. The objective is to blend process and technology to achieve the end objective. This means using Final step: Operating model documentation of operational processes aligned to architecture industry best practices like Six Sigma, ITIL, CMM, etc. Diverse stakeholders often require different views for functional areas. At this stage it is also necessary of the operating model for different reasons. As to define the optimal staffing model with the right there is no one “correct” view of the operating skill sets. In addition, we take a closer look at what the model, organizations may need to create variants organization has and what it needs, always keeping to fulfill everyone’s needs. A good example is value and efficiency as the primary goal. Striking the comparing what a CEO will look for (e.g., strategic right balance is key as it can become expensive to insights) versus what a CIO or COO would look attain even a small return on investment. for (e.g., an operating model architecture). To accommodate these variations, modeling tools like Each of the core components in Figure 5 needs Archimate5 will help create those different views to be detailed at this point, in the form of a quickly. Since the architecture can include many checklist, template, process, RACIF, performance

Reference data & analytics operating model (Level 1)

Business units / functions Customer / employee Third party (vendor/supplier) Regulatory compliance Industry value chain

1 Manage process 7 8

Manage 2 A. Manage demand/requirements + b. Manage channels Manage support change 3 Manage data 3a. Data acquisition/ 3b. Data provisioning/ 3c. Data preparation/ 3d. Data ingestion/ 3e. Data modeling/ 3f. Data analytics & data generation data storage data synthesis data integration reporting visualization

4 Manage data services

4a. Data management services 4b. Data analytics services

5 Manage project lifecycle

People 5a. 5b. 5c. 5d. 5e. 5f. 5g. 5h. Strategy, Analyze & Architecture Detailed Iterative Iterative Rollout & Sustain & Process Plan & align deﬁne design design development Testing transition govern

Business Organization Channel

It Data Technology

6 Manage technology/platform

9 Manage governance

Figure 5

6 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

metrics, etc. as applicable. the detailing of three levels involve detailing each block in Figure 6 until subcomponents one level down. Subsequent task/activity level granularity is reached.

The operating model components The nine components shown in Figure 5 will be Operational efficiency and the enablement present in one form or another, regardless of the of capabilities depend on the end-to-end industry or the organization of business units. management and control of these processes. Like any other operating model, the data and For example, the quality of data and reporting analytics model also involves people, process and capability depends on the extent of coupling technology, but from a data lens. between the processes.

❙ Component 1: Manage process: If an ❙ Component 2: Manage demand/require- enterprise-level business operating model exists, ments & manage channel: Business units are this component would act as the connector/ normally thirsty for insights and require different Component 1: Manage Process: If an enterprise- types of data from time to time. Effectively level business operating model exists, this managing these demands through a formal component would act as the connector/bridge prioritization process is mandatory to avoid between the data world and the business world. duplication of effort, enable faster turnaround Every business unit has a set of core processes and direct dollars to the right initiative. that generate data through various channels.

Sampling of subcomponents: An illustrative view

2 a. Manage demand/requirements 6 Manage technology/platform

Data checklist & templates Technology, application, platform

Business/System requirements Business case justiﬁcation Infrastructure Capacity

Requirements traceability matrix RACI matrix Tool/Vendor selection model Backup & disaster recovery

Business IT alignment Wireframes/Templates Stang Data center operations

Process/Data flows Meta model representation Application inventory License & renewals Infrastructure Backup & disaster recovery 9 Manage governance Support requirements Security & access management Data/Information governance Regulatory & compliance Usability requirements Policy, process & procedures Framework & methodology Eort & ROI estimation Beneﬁt/Value realization KPI/Metrics Issue management Value proposition matrix Impact/Dependency analysis RACI/CRUD DG organization model

Standards & controls Charter Figure 6

7 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

❙❙ Component 3: Manage data: This component process, controls, frameworks, service catalog manages and controls the data generated by and technology components. the processes from cradle to grave. In other Enablement of some of the capabilities as a words, the processes, procedures, controls and service and the extent to which it can operate standards around data, required to source, store, depends on the design of Component 3. It is synthesize, integrate, secure, model and report it. common to see a few IOMs in place before the The complexity of this component depends on subcomponents mature. the existing technology landscape and the three v’s of data: volume, velocity and variety. For a fairly ❙❙ Component 4b: Data analytics services: centralized or single stack setup with a limited Deriving trustable insights from data number of complementary tools and technology captured across the organization is not easy. proliferation, this is straightforward. For many Every organization and business unit has its organizations, the people and process elements requirement and priority. Hence, there is no can become costly and time-consuming to build. one-size-fits-all method. In addition, with advanced analytics such as those built around To enable certain advanced capabilities, the machine-learning (ML) algorithms, natural architect’s design and detail are major parts of language processing (NLP) and other forms of this component. Each of the five subcomponents artificial intelligence (AI), a standard model is requires a good deal of due diligence in not possible. Prior to detailing this component, subsequent levels, especially to enable “as-a- it is mandatory to understand clearly what the service” and “self-service” capabilities. business wants and how your team intends to ❙❙ Component 4a: Data management services: deliver it. Broadly, the technology stack and data Data management is a broad area, and each foundation determine the delivery method and subcomponent is unique. Given exponential extent of as-a-service capabilities. data growth and use cases around data, the Similar to Component 4a, IOMs help achieve the ability to independently trigger and manage end goal in a controlled manner. The interaction each of the subcomponents is vital. Hence, model will focus more on how the analytics enabling each subcomponent as a service adds team will work with the business to find, analyze value. While detailing the subcomponents, and capture use cases/requirements from the architects get involved to ensure the process industry and business units. The decision on the can handle all types of data and scenarios. Each setup — centralized vs. federated — will influence of the subcomponents will have its set of policy, the design of subcomponents.

Business units are normally thirsty for insights and require different types of data from time to time. Effectively managing these demands through a formal prioritization process is mandatory to avoid duplication of effort, enable faster turnaround and direct dollars to the right initiative.

8 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

❙❙ Component 5: Manage project lifecycle: The risk, architecture, infrastructure, data center and project lifecycle component accommodates applications (web, mobile, on-premises). projects of Waterfall, Agile and/or hybrid As in the previous component, it is crucial to nature. Figure 5 depicts a standard project detail the interaction model with respect to how lifecycle process. However, this is customizable IT should operate in order to support the as-a- or replaceable with your organization’s existing service and/or self-service models. For example, model. In all scenarios, the components require this should include cadence for communication detailing from a data standpoint. Organizations between various teams within IT, handling of live that have an existing program management projects, issues handling, etc. office (PMO) can leverage what they already have (e.g., prioritization, checklist, etc.) and ❙❙ Component 7: Manage support: No matter supplement the remaining requirements. how well the operating model is designed, the human dimension plays a crucial role, too. Be it The interaction model design will help support business, IT or corporate function, individuals’ servicing of as-a-service and on-demand data buy-in and involvement can make or break the requests from the data and analytics side during operating model. the regular program/project lifecycle. The typical support groups involved in the ❙❙ Component 6: Manage technology/ operating-model effort include BA team platform: This component, which addresses (business technology), PMO, architecture the technology elements, includes IT services board/group, change management/advisory, such as shared services, security, privacy and

9 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

training and release management teams, the horses. Although each component is capable infrastructure support group, IT applications team of functioning without governance, over time and corporate support group (HR, finance, etc.). they can become unmanageable and fail. Hence, Organization change management (OCM) is a planning and building governance into the DNA critical but often overlooked component. Without of the operating model adds value. it, the entire transformation exercise can fail. The typical governance areas to be detailed ❙❙ Component 8: Manage change: This include data/information governance component complements the support framework, charter, policy, process, controls component by providing the processes, standards, and the architecture to support controls and procedures required to manage enterprise data governance. and sustain the setup from a data perspective. This component manages both data change Intermediate operating models (IOMs) management and OCM. Tight integration between this and all the other components is As mentioned above, an organization can create as key. Failure to define these interaction models many IOMs as it needs to achieve its end objectives. will result in limited scalability, flexibility and Though there is no one right answer to the question robustness to accommodate change. of optimal number of IOMs, it is better to have no more than two IOMs in a span of one year, to give The detailing of this component will determine sufficient time for model stabilization and adoption. the ease of transitioning from an existing The key factors that influence IOMs are budget, operating model to a new operating model regulatory pressure, industrial and technology (transformation) or of bringing additions to the disruptions, and the organization’s risk appetite. existing operating model (enhancement). The biggest benefit of IOMs lies in their phased ❙❙ Component 9: Manage governance: approach, which helps balance short-term priorities, Governance ties all the components together, manage risks associated with large transformations and thus is responsible for achieving the and satisfy the expectation of top management to synergies needed for operational excellence. see tangible benefits at regular intervals for every Think of it as the carriage driver that steers the dollar spent.

IOMs help achieve the end goal in a controlled manner. The interaction model will focus more on how the analytics team will work with the business to find, analyze and capture use cases/ requirements from the industry and business units. The decision on the setup – centralized vs. federated – will influence the design of subcomponents.

10 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

To succeed with IOMs, organizations need a tested ❙❙ Understanding that the organization cannot approach that includes the following critical cover all aspects (in breadth) on the first attempt. success factors: ❙❙ Avoidance of emotional attachment to the ❙❙ Clear vision around data and analytics. process, or of being too detail-oriented. ❙ ❙❙ Understanding of the problems faced by ❙ Avoidance of trying to design an operating customers, vendors/suppliers and employees. model optimized for everything. ❙ ❙❙ Careful attention paid to influencers. ❙ Avoidance of passive governance — as achieving active governance is the goal. ❙❙ Trusted facts and numbers for insights and interpretation.

DAOM (Level 2)

Business units/functions Customer/Employee Third party (Vendor/Supplier) Regulatory compliance Industry value chain

Operations Finance Sales ... Fiserv Potelco NERC FERC Generation Transmission Distribution

1 Manage Process 7 8 a. Manage Demand/Requirements + b. Manage Channels Manage 2 Manage Support change 3 Manage Data 7a. BA services 3a. Data acquisition/ 3b. Data provisioning/ 3c. Data preparation/ 3d. Data ingestion/ 3e. Data modeling/ 3f. Data analytics & 8a. Process Data generation Data storage Data synthesis Data integration reporting visualization 7b. 8b. Controls Architecture 4 Manage Data Services 8c. Framework 7c. Research 4a. Data management services 4b. Data Analytics Services 7d. Release mgmt. 4a.1. Metadata management 4b.1. AI, IoT, Big data & blockchain 7e. Infrastructure 4a.2. Master data management Un-structured data 4b.2 Search & discovery (Self-service) 7f. Training 4a.3 Data quality management Oensive 7g. PMO 4b.3 Visualization & UI/UX

7h. OCM 4a.4. Information lifecycle management 4b.4. Cognitive

7i. IT Defensive Structured data 4a.5. Data security & privacy 7j. Security 4b.5. Predictive & prescriptive 4a.6. Audit and compliance

4a.7 Backup & disaster recovery 4b.6. Descriptive & diagnostic

5 Manage Project Lifecycle 5a. 5b. 5c. 5d. 5e. 5f. 5g. 5h. Strategy Analyze Architecture Design Develop Test Transition Sustain Plan & Align Deﬁne Design Deliver Operate

People Business case, Use case elaboration Proof of Use case prioritization, concept (Pilot) User UX/UI Requirements detailing acceptance enhancements testing

& analysis Training Business Workflow design Workflow devl. assessment Process alignment OCM impact checklist Process deﬁnition Process design Process devl. KM base Business Process & Process workflow /Workflow testing improvements Stakeholder Organization involvement matrix RACI / CRUD Conﬁguration Handover

Delivery channel UX/UI testing Channel (UX/UI) UX/UI design

Data identiﬁcation IT / Data & collection Data alignment Knowledge Enhancements IT checklist IT solution design, IT solution transfer & & upgrades Data proﬁling & model design testing & training Data quality & selection deployment Master data management Data provisioning infrastructure

Technology Development & conﬁguration

6 Manage technology/platform

9 Manage governance

Figure 7

11 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

Methodology: The big picture view

Corporate business model (existing)

Extract Data Core Entities & Information Step 1 data points domains processes relationships architecture Business model (Precursory The Design Current state Influencers steps) pivots principles (data)

Reference Data & Reference data & Step 2 points analytics vision analytics model Operating model Level 1: (data focused) Customized data Integration & Level 1 daom & analytics operating standardization (illustrative) model (daom)

Level 2: DAOM 1 Manage process 4 Manage data services 7 Manage support

Manage project Manage demand Manage change 2 5 lifecycle 8

Step 3 3 Manage data 6 Manage technology 9 Manage governance Operating model architecture Level 3: Each subcomponent in level 2 is detailed as required from a people, process and DAOM technology standpoint. The physicalization/implementation of the same for some subcomponents can get into the next level of detail (level 4).

Issue Issue Issue Governance Raci, Conceptual & Kpi/ management resolution Register/ Structure r&r Logical metrics, & reporting process tool & skillset data models thresholds ...

Figure 8

12 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

Moving forward

Two factors deserve highlighting: First, as making becomes a daunting task. This is where a organizations establish new business ventures and robust data and analytics operating model shines. models to support their go-to-market strategies, According to a McKinsey Global Institute report, their operating models may also require changes. “The biggest barriers companies face in extracting However, a well-designed operating model will value from data and analytics are organizational.”6 be adaptive enough to new developments that it Hence, organizations must prioritize and focus on should not change frequently. people and processes as much as on technological Second, the data-to-insight lifecycle is a very aspects. Just spending heavily on the latest complex and sophisticated process given the technologies to build data and analytics capabilities constantly changing ways of collecting and will not help, as it will lead to chaos, inefficiencies processing data. Furthermore, at a time when and poor adoption. Though there is no one-size- complex data ecosystems are rapidly evolving and fits-all approach, the material above provides key organizations are hungry to use all available data principles that, when adopted, can provide optimal for competitive advantage, enabling things such outcomes for increased agility, better operational as data monetization and insight-driven decision- efficiency and smoother transitions.

13 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

Endnotes

1 A tool that allows one to describe, design, challenge and pivot the business model in a straightforward, structured way. Created by Alexander Osterwalder, of Strategyzer.

2 Operating model canvas helps to capture thoughts about how to design operations and organizations that will deliver a value proposition to a target customer or beneficiary. It helps translate strategy into choices about operations and organizations. Created by Andrew Campbell, Mikel Gutierrez and Mark Lancelott.

3 First described by Michael E. Porter in his 1985 best-seller, Competitive Advantage: Creating and Sustaining Superior Performance. This is a general-purpose value chain to help organizations understand their own sources of value — i.e., the set of activities that helps an organization to generate value for its customers.

4 The 7S framework is based on the theory that for an organization to perform well, the seven elements (structure, strategy, systems, skills, style, staff and shared values) need to be aligned and mutually reinforcing. The model helps identify what needs to be realigned to improve performance and/or to maintain alignment.

5 ArchiMate is a technical standard from The Open Group and is based on the concepts of the IEEE 1471 standard. This is an open and independent enterprise architecture modeling language. For more information: www.opengroup.org/ subjectareas/enterprise/archimate-overview.

6 The age of analytics: Competing in a data-driven world. Retrieved from www.mckinsey.com/~/media/McKinsey/ Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20 in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx.

References

• https://strategyzer.com/canvas/business-model-canvas.

• https://operatingmodelcanvas.com/.

• Enduring Ideas: The 7-S Framework, McKinsey Quarterly, www.mckinsey.com/business-functions/strategy-and- corporate-finance/our-insights/enduring-ideas-the-7-s-framework.

• www.opengroup.org/subjectareas/enterprise/archimate-overview.

14 / Building an Effective & Extensible Data & Analytics Operating Model Cognizant 20-20 Insights

About the authors

Jayakumar Rajaretnam Senior Manager, AI and Analytics Practice, Cognizant Consulting

Jayakumar Rajaretnam is a Senior Manager within Cognizant Consulting’s AI and Analytics Practice. He has 13 years of experience working on business intelligence (BI), data management, data governance and operating model engagements across industries (hospitality, telecom, banking and utilities) and geographies (India, APAC, Middle East, UK and the U.S.). Jayakumar is a Six Sigma green belt, holds an MBA from SP Jain Center of Management, Dubai-Singapore and a B.Tech. from University of Madras. He can be reached at [email protected] | www.linkedin.com/in/jayakumarr.

Mohit Vijay Senior Consultant, AI and Analytics Practice, Cognizant Consulting

Mohit Vijay is a Senior Consultant within Cognizant Consulting’s AI and Analytics Practice. He has seven- plus years of consulting and product/program management experience in providing BI, data strategy and business analytics advisory to clients across industries including retail, hospitality and technology. Mohit holds an MBA from SP Jain, Mumbai, and a B.Tech. from Rajasthan University. He can be reached at Mohit. [email protected] | www.linkedin.com/in/mohit-vijay-57510b6/.

15 / Building an Effective & Extensible Data & Analytics Operating Model About Cognizant Consulting With over 5,500 consultants worldwide, Cognizant Consulting offers high-value digital business and IT consulting services that improve business performance and operational productivity while lowering operational costs. Clients leverage our deep industry experience, strategy and transformation capabilities, and analytical insights to help enhance productivity, drive business transformation and increase shareholder value across the enterprise. To learn more, please visit www.cognizant.com/consulting or e-mail us at [email protected].

About Cognizant Digital Business Cognizant Digital Business helps our clients imagine and build the Digital Economy. We do this by bringing together human insight, digital strategy, industry knowledge, design and new technologies to create new experiences and launch new business models. For more information, please visit www. cognizant.com/digital or join the conversation on LinkedIn.

About Cognizant Cognizant (Nasdaq-100: CTSH) is one of the world’s leading professional services companies, transforming clients’ business, operating and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build and run more innovative and efficient business- es. Headquartered in the U.S., Cognizant is ranked 195 on the Fortune 500 and is consistently listed among the most admired companies in the world. Learn how Cognizant helps clients lead with digital at www.cognizant.com or follow us @Cognizant.

World Headquarters European Headquarters India Operations Headquarters 500 Frank W. Burr Blvd. 1 Kingdom Street #5/535 Old Mahabalipuram Road Teaneck, NJ 07666 USA Paddington Central Okkiyam Pettai, Thoraipakkam Phone: +1 201 801 0233 London W2 6BD England Chennai, 600 096 India Fax: +1 201 801 0243 Phone: +44 (0) 20 7297 7600 Phone: +91 (0) 44 4209 6000 Toll Free: +1 888 937 3277 Fax: +44 (0) 20 7121 0102 Fax: +91 (0) 44 4209 6060

© Copyright 2019, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means,electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.

Codex 3579.02