Competitive Landscape: Data and Analytics Service Providers

Published: 15 June 2017 ID: G00327337



As data and analytics become the center of digital transformation, technology product marketing leaders must compete by leveraging an asset-based service approach and augmenting their solutions with intelligent automation in an extended digital ecosystem.


Key Findings

  • Data and analytics are becoming pervasive in all solutions and services, and service providers are bundling them in more than 50% of engagements to drive additional revenue.

  • Many service providers are unable to differentiate because of vastly similar value propositions, investments and service offerings. The difficulty to differentiate will intensify as more providers enter into the competition.

  • Traditional service approaches will be replaced by an asset-based and as-a-service approach in an extended digital ecosystem.


Technology product marketing leaders focused on exploiting IT services market dynamics must:

  • Utilize data and analytics as the core enabler of digital business, and apply a more holistic approach to help clients acquire, organize and analyze the data, as well as deliver the business results.

  • Differentiate by demonstrating thought leadership and creativity, as well as building more AI-enabled use cases, tools and assets to augment their consulting team to provide more industry-specific and/or business-process-specific solutions.

  • Demonstrate value to their clients and focus on their clients' business outcomes by embedding analytics into all engagements and continuously measuring impact.

Strategic Planning Assumption

By 2020, more than 50% of data and analytics service engagements will be AI-enabled, up from 10% in 2017.


As data and analytics become more pervasive in every competitive business and digital transformation, service providers are racing to come up with all sorts of data and analytics assets and integrate them with emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), cognitive computing and machine learning (Gartner's umbrella term for this class of technologies is "Artificial Intelligence") . Adoption of "analytics as a service" (see "Market Insight: Understanding the Flavors of Analytics-as-a-Service Offerings" ) is also increasing in response to the availability of more secured cloud solutions, lack of resources, rapidly evolving technologies and the maturing of analytics infrastructure. As a result, traditional service approaches will be replaced by an asset-based and as-a-service approach in an extended digital ecosystem. In order to compete in this rapidly evolving market, service providers will have to further differentiate themselves by mapping their data and analytics solutions against more specific business processes and industries, and contextualize in use cases of how analytics can be applied for decision-making purposes. For example, Gartner's data and analytics coverage sees a dramatic rise in client inquiries on the topics of analytics innovation and managing "analytics spread." In order to understand and address these inquiries, basic segmentation of analytics will be required, where the natural segments include:

  • Analytics by industry vertical domain

  • Analytics by business domain

  • Analytics on specific data sources/types

  • Analytical methods/techniques

Heat maps will be helpful to describe and prioritize the various capabilities and domain analytics opportunities. Based on analysis of searches, Figure 1 illustrates business process domain analytics demand by industry vertical domain, and Figure 2 depicts data source and analytics methods demand by industry vertical domain (see "Domain Analytics: Harnessing the Pervasive Nature of Analytics" ).

Figure 1. Business Process Domain Analytics Demand by Industry Vertical Domain
Research image courtesy of Gartner, Inc.

CPM = corporate performance management; GRC = governance, risk and compliance

Source: Gartner (June 2017)

Figure 2. Data Source and Analytics Methods Demand by Industry Vertical Domain
Research image courtesy of Gartner, Inc.

BI = business intelligence; IoT = Internet of Things; OLAP = online analytical processing

Source: Gartner (June 2017)

Competitive Situation and Trends

Information management and analytics will continue to be a top technology investment and represent growth opportunities for service providers. However, gaps exist between the enterprises' expectations and service providers' solutions, capabilities and promises (see "Market Trends: Top Five Buyer Expectations of Intelligent Automation in Data and Analytics Services" ). Service providers will continue to compete at different levels and scales in the broad data and analytics service market:

  • Technology — Some enterprises are looking for service providers to implement information management technologies and business intelligence (BI) and analytics software, create models and architectures, provide expertise with scarce skill sets, or manage the data and analytics infrastructure and applications. These are mostly technology-oriented, Mode 1 departmental initiatives and are typically IT-led.

  • Descriptive/diagnostic analytics — Others look for service providers with a portfolio of solutions to assist them with traditional reporting BI, bringing advanced functionality such as forecasting, optimization, decision support and workflow support — and these are mostly descriptive/diagnostic analytics initiatives. Some business stakeholders are involved in making the data and analytics purchase decisions together with IT.

  • Advanced analytics — Enterprises that are more advanced in their digitalization journey will expect service providers to come with thought-leading, innovative ideas and engagement models to deliver projects in short sprints (usually within a few weeks to three months), each with measurable business value. These sprints may include advanced analytics and are mostly Mode 2 projects that involve the enterprises and service providers co-innovating or co-creating solutions and/or businesses together. These engagements are usually business-led and may start small but may develop into an enterprisewide initiative.

Data and analytics skills gaps will accelerate since these talents will continue to be in short supply universally (see "The 2017 CIO Agenda: Seize the Digital Ecosystem Opportunity" ). In addition, new skills around IoT, advanced analytics and digital will continue to be in scarce supply. Facing this resourcing challenge, many service providers are developing intellectual property (IP) assets and automation to change the way they deliver services, especially in consulting and implementation. IP assets range from reusable code, process maps, planning tools, impact and readiness assessment frameworks, transformation frameworks, diagnostic tools, and methodologies to analytics, preconfigured solutions and full-stack platform-based business solutions. Automation ranges from basic macros and scripts to full-fledged AI, cognitive computing and machine learning. These IP assets and automation will:

  • Reduce labor input

  • Generate new revenue or increase the potential to improve margins

  • Ensure consistent delivery and operational efficiency across the providers' global workforces

  • Have the potential be used as a differentiator against labor-based competitors

  • Produce new insights with the data for innovative solutions

For service providers to be successful, they must continue to articulate their strategy and improve their approaches and execution capabilities on technology, descriptive/diagnostic and/or advanced analytics. More importantly, service providers must have the ability to apply these approaches within a business process or industry context. They must also excel in developing more industry-specific and/or business-process-specific IP assets and automation. They can do so by:

  • Expanding into new ventures such as startup engagements

  • Co-creating new businesses with partners and clients

  • Establishing innovation facilities

  • Strengthening business consulting skills

Market Players

Major competitors in data and analytics services include global service providers, and regional and local players. All of them are racing to build IP assets, train resources, leverage and embed AI and other advanced technologies into their solutions, form strategic alliances in the digital ecosystems, and refine their value propositions to beat competition.

Table 1 shows information of 20 of the largest global data and analytics service providers by revenue based on "Magic Quadrant for Business Analytics Services, Worldwide." Table 2 shows a partial list of the multinational, regional and local data and analytics service providers.

Table 1.   Top 20 Global Data and Analytics Service Providers

Service Provider

Number of Data and Analytics Professionals* (Excluding Data Scientists)

Number of Data Scientists*

Industry Focus



More than 1,800

Communications, media and technology, financial services, products, resources, health and public services




Consumer packaged goods, retail, manufacturing, healthcare, energy and utilities, transport, finance, public sector, telecom, media, defense and security



More than 900

Financial services, manufacturing, automotive, life sciences, consumer products, retail, distribution and transportation, public sector, energy and utilities, telecom



Not disclosed

Government, financial services and healthcare




Banking, insurance, financial services, healthcare and life sciences, retail, travel and hospitality




Finance, banking, insurance, public sector, health, consumer products, manufacturing and retail, energy and resources, technology, media and entertainment and telecom




Financial services, consumer products and retail, government and public services, health and life sciences, telecom, oil and gas, and energy and utilities

HCL Technologies


More than 150

Banking and financial services, insurance, manufacturing and high-tech, life sciences and healthcare, and consumer services

DXC Technology**

More than 6,150

More than 150

Financial services, consumer and retail, manufacturing, public sector, energy, communications, healthcare and transportation




Financial services, healthcare, retail/consumer packaged goods (CPG), industrial and communications




Financial services, banking, high-tech, retail and consumer packaged goods, energy and utilities, life sciences, manufacturing and services




Financial services, industrial and consumer markets, infrastructure, government, healthcare, technology, media and telecommunications




Public sector, retail, telecommunications, high-tech and chemicals

NTT Data


More than 860

Automotive, financial services, healthcare, insurance, telecommunications and utilities


More than 22,000

More than 1,200

Financial services, healthcare, technology, public sector, retail and consumer products




Manufacturing, financial services, energy and utilities, and consumer products

Tata Consultancy Services


Not disclosed

Financial services, insurance, life sciences and healthcare, manufacturing, retail, telecommunications, media and entertainment, and travel and hospitality

Tech Mahindra


More than 500

Banking, financial services, health and life sciences, manufacturing, telecommunications, and retail



Not disclosed

Financial services, retail, communications, manufacturing and healthcare




Banking, financial services, insurance, energy, natural resources, utilities, manufacturing, high-tech, retail and consumer goods

*Approximate/estimated numbers
**The merger between Hewlett Packard Enterprise's Enterprise Services division and CSC was completed and formed DXC Technology in April 2017.

Source: Gartner (June 2017)

Table 2.   Partial List of Multinational, Regional and Local Data and Analytics Service Providers

Service Provider

Headquarters Country

Service Provider

Headquarters Country

ABeam Consulting







The Netherlands



Hand Enterprise Solutions
















KPI Partners



The Netherlands





Melbourne IT


Business & Decision








CBIG Consulting


PA Consulting Group








PCCW Solutions

Hong Kong









Convergence Consulting Group






SDG Group


Data Agility






Synergic Partners


Definitive Logic


Syntelli Solutions


Dialog Group




EPAM Systems







New Zealand

Source: Gartner (June 2017)

The Future of Competition

Data and analytics will continue to be a top technology priority for most enterprises in the foreseeable future, and will accelerate the mediation and value exchange occurring in digital ecosystems. Figure 3 shows the key technologies that will have the most potential to change enterprises in the next five years based on responses to Gartner's annual CIO survey. 1 Advanced analytics (predictive and prescriptive) will have the most potential to change enterprises and will become fundamental to customer, citizen and user engagement. The exponential growth of data, especially in IoT, will also lead to distributed and embedded analytics, as well as logical data warehouses and in-memory computing.

Figure 3. CIO Perspective: Key Technologies to Change Enterprises
Research image courtesy of Gartner, Inc.

Number of respondents = 2,331

Source: Gartner (June 2017)

Based on responses to Gartner's Research Circle Survey, 2 many departments within an enterprise are currently using or will deploy business applications of analytics, especially in operations, finance, sales and IT (see Figure 4). These departments' most common uses of data and analytics initiatives are to achieve operational excellence, improve customer experience and derive customer insights (see Figure 5), which in turn will drive new sales. Many of them are also considering or piloting predictive analytics, which are characterized by techniques such as multivariate analyses, forecasting, pattern matching, predictive modeling and forecasting.

Figure 4. Heaviest Users of Business Applications of Analytics
Research image courtesy of Gartner, Inc.

Source: Gartner (June 2017)

Figure 5. Operational Excellence and Customer Insights Are the Most Common Uses for Business Applications of Analytics
Research image courtesy of Gartner, Inc.

Source: Gartner (June 2017)

To capture the data and analytics opportunities, service providers must have a combination of the following characteristics:

  • Leverage and/or develop a data and analytics platform 3 with embedded AI and machine-learning technologies to provide intelligent automation 4 services to ensure higher quality of business outcomes (such as improving operational efficiency and deriving better customer insights), reduce the time needed to execute processes, and enable the business operation to be more adaptable and agile in execution. A data and analytics platform is one kind of key asset for service providers that use an asset+ consulting approach. Many service providers are gradually adopting the asset+ consulting approach to focus on delivering business outcomes and nurturing sustainable client relationships in data and analytics-related engagements.

  • Invest significantly in data and analytics capabilities, including strengthening expertise in advisory/business consulting and managed/as-a-service offerings, acquiring new and scarce talent, and building tools and solutions. Service providers expect these large investments to bring in higher volumes of work and an improved value proposition. The ROI of these investments will vary by service provider, solution, technology and complexity, and until service providers reach a scale and volume that allows them to optimize their investments, such engagements will take a longer time to become profitable.

  • Co-create or co-innovate with partners and clients to form new businesses and revenue streams, or add more value in the digital ecosystem. For example, there are emerging usage of the data and analytics platform to create new business opportunities in data monetization including IoT data, and open marketplaces for AIs and algorithms that replace third-party models.

  • Embed data and analytics expertise in all the key departments while also using a "hub-and-spoke" model where the hub continues to provide deep expertise in data sciences, R&D and innovation. Data and analytics-enabled resources across departments will be required to be skilled in a number of areas, such as data and analytics tools and technologies, methodologies, data governance, change management, digital and business advisory. These resources must stay educated on what is emerging and failing, and at the same time they will increase their time dedicated to client-facing and go-to-market activities.

  • Provide more transparency in the commercial terms to help clients understand and justify their investments, especially in multiyear contracts. Gartner has recently seen increasing frustration among end-user clients with their multiyear as-a-service contracts where service providers include all advisory and as-a-service, infrastructure and software costs in large lump sums with minimum breakdowns.

Competitive Profiles

The service providers profiled below are selected to represent a wide range of different data and analytics approaches, value propositions and investments. Exclusion does not reflect the service providers' quality or credibility. Profiles are listed in alphabetical order.


Market Overview

Accenture is one of the leading service providers in data and analytics through its depth and breadth of functional, industry and technology capabilities, its global presence, a proven track record of large-scale complex transformational projects, and large investments in R&D and innovation. Accenture Analytics is part of Accenture Digital, along with Accenture Interactive and Accenture Mobility. These businesses collaborate to provide relevant capabilities in the digital world that will make the company a good partner for large organizations looking for innovative, flexible and cost-effective solutions in their data and analytics journey.

How Accenture Competes

Accenture offers a full spectrum of services to its clients, such as data integration and management; business intelligence and reporting; advanced analytics and advanced analytical models; AI and machine-learning/deep-learning techniques; and developing insights that drive tangible outcomes. Accenture continues to invest in extending its capabilities, and its advanced analytics, industry, business process, technology, strategy and experience design-led approach enable the firm to deliver end-to-end solutions across multiple technologies, platforms, delivery models and devices. Accenture also uses data and analytics as a key component to help clients achieve their goals in various stages of their digital business journeys — for example, entering new markets, expanding into new geographies and/or discovering new ways to monetize their data — as well as understanding and building capabilities that the clients can industrialize themselves.

Data and analytics are pervasive and embedded as a core value lever across most assignments that Accenture works on with its clients because the demand for data and analytics-driven outcomes continues to increase. Accenture differentiates by using an issue-led, outcome-focused approach that is:

  • Asset-powered (e.g., the Accenture Insights Platform and the advanced analytics apps).

  • Tied to industry with high-performance industry blueprints to drive industry-specific business outcomes.

  • Industrialized to provide speed-to-value with a deep talent bench (e.g., more than 41,000 digital professionals, more than 1,800 data scientists, and more than 2,400 individual big data practitioners, as well as big data learning and advanced analytics academies), strong technology alliance ecosystem and global delivery network.

  • Innovative and design-led in collaboration with its acquired companies, studios, innovation centers and labs, and academia (e.g., Karmarama; more than 30 studios; Accenture Innovation Centers; Accenture Labs; Accenture's Data Science Center of Excellence; Massachusetts Institute of Technology, Duke University, and the Stevens Institute of Technology in the U.S., and ESSEC Business School and German Research Center for Artificial Intelligence [DFKI] in Europe).


Market Overview

Capgemini has a full-service data and analytics capability in its Insights & Data practice, and has expanded its innovation and presence in global markets with a number of acquisitions, including Fahrenheit 212, Idean, oinio and Igate. It also uses its technology partnerships to continuously update its portfolio, bring in new solutions and gain market visibility. It adds innovation through increased focus on digital, incorporating real-time intelligence, cognitive and AI, advanced analytics, the IoT and enhanced data management. Capgemini's Insights & Data strategy and operational model places heavy focus on delivering real and sustainable value, business outcomes and tangible results.

How Capgemini Competes

Capgemini combines technology, data science, business and industry expertise to help organizations drive insights and outcomes from internal and external data sources. Capgemini leverages its global network of Applied Innovation Exchange (AIE) centers, data and analytics solutions and managed end-to-end BI and data and analytics platform to enable organizations to discover, contextualize and experiment with relevant innovations to drive growth opportunities for their businesses. It has developed the 7 Guiding Principles approach to drive the transformation of its clients' organization to become an insights-driven enterprise. These principals are:

  • Embark on the journey to insights, within your business and technology context

  • Enable your data landscape for the flood coming from connected people and things

  • Master governance, security and privacy of your data assets

  • Develop an enterprise data science culture

  • Unleash data- and insights-as-a-service

  • Make insight-driven value a crucial business KPI

  • Empower your people with insights at the point of action


Market Overview

Cognizant is the largest service provider in terms of the number of its data and analytics professionals. The firm focuses on delivering business outcomes through strategy and ideation, implementation and managed/as-a-service offerings that are enabled by its IP assets including the Analytics and Information Management solutions stacks, platforms around digital, business and technology (e.g., BigDecisions Business Solutions Platform and BIGFrame), as well as industry expertise with more than 40 industry-specific analytics solutions and a team of more than 5,000 industry and domain consultants across Cognizant.

How Cognizant Competes

Cognizant's Analytics and Information Management (data and analytics) is the center of its digital business strategy to help clients with their cross-organizational transformation in the digital economy. Cognizant uses an integrated approach to help clients by "turning insights into action." Cognizant also promotes its prioritizing of bimodal work styles to balance innovation and execution through:

  • Aligning data and analytics services to market demand and delivering industry-specific outcomes and business impact as part of its digital business solutions, business process and platform, and next-generation IT solution offerings. These solutions and offerings include AI-driven process management, as well as modern IT backbone to run and enable digital business.

  • Investing in data and analytics platforms/solutions, digital readiness consulting, data monetization strategy, big data value assessment, next-generation analytics, operational analytics, robotic process automation, deep learning, cognitive intelligence, commercial analytics, data consolidation, data on cloud, information economics, enterprise data management and optimization, BigDecisions (Cognizant's System of Intelligence platform imparting predictability, discovery and agility to core data and analytics, and connecting insights to actions), BIGFrame (mainframe and enterprise data warehouse offload to Hadoop), and its patented Platform for Information Value Management.

  • Investing in innovation by setting up more than 15 stand-alone and Collaboratory Integrated Techquarium locations to facilitate business ideation and experimentation; incubating innovation ecosystem with more than 30 emerging technology vendors in analytics, machine learning, big data and cognitive; crowdsourcing business and technology ideas across Cognizant's 230,000 associates; and establishing dedicated technology R&D and center of excellence.

  • Strengthening core capabilities across industries, with key priorities in banking, manufacturing, retail, consumer goods, information services, media and entertainment, healthcare, technology and travel; and expanding in key regions through organic growth and acquisition.


Market Overview

Deloitte Analytics brings together data science, digital design and technology capabilities, business expertise, and industry insight to help clients transform their businesses in the digital economy. Deloitte Analytics is a global provider of analytics services and solutions that are focused on delivering business outcomes. It combines industry-specific insights, cognitive technologies, advanced cloud-based platforms and solutions in its client engagements to accelerate speed to action, and offers flexible commercial contract structures, including outcome-based contracts. Its key strengths are the depth and breadth of skills, including business consulting, functional, digital design and technical. The cultural and execution characteristics include global presence, client-centric approach and complex project execution leveraging the broad range of professional competencies.

How Deloitte Competes

Deloitte competes by helping organizations maximize the value of data and analytics to drive smarter insights and stronger outcomes through delivering operational excellence, new products and services, competitive agility, and growth. Deloitte has a broad range of solution and platform offerings, and Deloitte uses an integrated approach that helps organizations shape strategy; leverage new capabilities such as cognitive science; generate, manage and use data; apply industry solutions; and integrate with clients' ongoing operations through its suite of as-a-service offerings. Deloitte continues to advance its data and analytics business through:

  • Outcome focus — Driving business outcomes that address industry-specific issues to help organizations achieve the insight-driven advantage using the Insight-Driven Organization (IDO) Framework.

  • Platform expansion — Continuing to invest in establishing global platforms to drive productivity for client organizations, including Cognitive Advantage, Cognitive Platform and ClearLight.

  • Nurture client connection — Fostering long-term relationships by client ventures, value-focused deals, subscription services and managed environments.

  • Monetize data — Curating public and private data to power the global platforms and accelerate outcomes with data-backed insights.

  • Cultivate talent — Combining data science and cognitive intelligence and integrating into Deloitte's global talent model, investing into Deloitte Greenhouses, 5 Digital Studios and global delivery resources and centers.


Market Overview

EY embeds analytics across its EY Advisory practice, which includes the key areas of cybersecurity, digital, innovation, risk and transformation. EY uses an issue-led and technology-enabled approach and combines the expertise from the EY member firms and its Global Analytics Center of Excellence to help clients gain new insight and drive the right judgments in the digital world. EY has been very active in forging alliances with a large number of partners to expand its domain analytical capabilities, such as Ebix (insurance, finance, health), LinkedIn (social analytics), Johns Hopkins University (patient safety, operation efficiency, clinical outcomes) and GE (IoT).

How EY Competes

EY focuses on building a sector solution portfolio that is based on two workstreams of activity running in parallel: "Infusion" and "Innovation." Six industries have been prioritized: financial services; life sciences; retail and consumer products; health; power and utilities; and public sector and government. For each of these targeted industries, EY is developing a set of accelerators, assets and products that are supported by its big data platform. These assets and tools can be categorized by the following functional domains:

  • Business strategy and finance

  • Customer service and experience

  • Marketing and sales

  • Operations

  • Organization and human resources

  • Research and development

  • Risk and security

  • Technology

IBM Global Business Services (GBS)

Market Overview

IBM Global Business Services (GBS) has assembled one of the market's broadest and deepest portfolios of data and analytics services and solutions capabilities. IBM GBS has an intense focus on cognitive and analytics and customer experience as the growth engines that are aligned with IBM Watson strategies and IBM Software. It offers business and technology consulting services spanning business design, cognitive and analytics, IoT, mobility, customer and data platform services. IBM GBS leverages design centers and global service centers in more than 40 locations that are integrated with other resources, such as IBM Systems Lab Services, IBM Software and IBM Research. It utilizes IBM Design Thinking and its IBM Institute for Business Value to support its consulting services, and leverages Watson and its analytics resources to enable clients to realize business value through operational efficiencies and new business models.

How IBM GBS Competes

IBM GBS is building up its cognitive app store and has embedded cognitive capabilities into each service line to harvest and deliver analytical assets throughout the full IBM ecosystem. IBM GBS is transforming from a traditional service integrator into a provider of cognitive solutions, leveraging IBM's overall investments in cognitive and analytics technologies while focusing on industry-specific business outcomes. IBM GBS has five main industry focus areas (see Table 1) and four main practices in its Cognitive Business Decision Support service line to provide repeatable platforms and solutions to help clients transform, make decisions and drive business value using advanced analytics, cognitive technology, industry and domain-specific expertise and data:

  • Advanced Analytics — Drives advanced insights for client experience, marketing and processes using cognitive and decision science; builds repeatable industry applications that tap IBM assets, including Watson APIs, IBM Watson Data Platform, IBM Metro Pulse Powered by Watson and The Weather Company.

  • Watson AI and IBM Watson Data Platform — Delivers the suite of Watson core applications and APIs to clients; leverages the IBM Watson Data Platform to enable and train Watson and transform client data environments.

  • Watson IoT and Watson Supply Chain — Combines Watson IoT and GBS to provide IoT solutions as a service using IBM's platform; reinvents and optimizes businesses with analytics-led, IoT-enabled business models to maximize efficiency, customer centricity, economic growth and asset productivity.

  • IBM Watson Health and Curam — Helps clients design, develop and implement innovative healthcare programs based on IBM Watson Health solutions and technologies, and to help clients realize the benefits of these solutions.


Market Overview

KPMG has created a strong vision for data and analytics that is embedded into its core practices, with specialist Insights Centers based across the globe to provide resources to supplement local teams. It is growing its data and analytics service portfolio within member firms to help clients with specific data issues such as privacy, security and forensics, as well as expanding current services into new markets. KPMG combines its credentials in tax, advisory and audit with its digital investments, and leverages its Value Delivery Framework (VDF) and a message based on trust to help clients in their business transformation.

How KPMG Competes

KPMG continues to invest heavily in its data and analytics acquisitions, partnerships and innovation, as well as rapidly scale resources, technology, IP and know-how required to build solutions and foundational platforms in advance. KPMG's strategy focuses on:

  • Delivering trusted analytics solutions and services so that business leaders can rely on the analytics and trust that their data is safe.

  • Leveraging deep industry and process knowledge to define problems, articulate and build domain solutions, and deliver end-to-end services.

  • Supporting the C-suite with full-scale transformation services powered by data and analytics solutions.

  • Taking a business-first perspective and solving complex business issues with analytics, rather than a technology-first approach.

  • Creating differentiated competitive advantage by unlocking the value of data and supplying relevant sources of external data.

  • Accelerating business results with data and actionable insights across its global network.


Market Overview

PwC has a strong and diverse data and analytics service portfolio that includes industry-specific and cross-industry data and analytics solutions, which can be leveraged by the different business units across the company. PwC has strong business acumen and leverages its strengths across service lines, sectors and geographies, and has been investing in new technologies and ventures. These investments include machine learning, cognitive computing, drones and spatial analysis, IoT, acquisitions, innovation and experience centers, networks, and accelerators such as collaboration with academia, venture capital firms and startups.

How PwC Competes

PwC views data and analytics as a firmwide starting point to collectively approach client issues across service areas, geographies and industries to create competitive capabilities for clients powered by data and analytics. Key elements of PwC's strategy include:

  • Powered by data and analytics — Uses information management as a foundation, and offers differentiated data and analytics capabilities across service lines to help clients achieve business objectives such as improved productivity, revenue and profit growth.

  • Execution-oriented — Applies data and analytics to inform decision making on issues ranging from strategy to execution, as well as transforms organizations by designing and operationalizing analytics, reporting and technology capabilities.

  • Industry focus — Aligns data and analytics specialists with industry practitioners and go-to-market strategy through key industry verticals; offers industry-centric strategy, innovation, methods, tools and accelerators.

  • Global delivery — Leverages global analytics hubs, impact centers, offshore and onshore delivery centers to deploy data and analytics and technology professionals to provide solutions and services with increased global acumen and perspective.

  • Multicompetency — Assembles multifunctional teams with experience across business functions, industry drivers and technical capabilities to integrate and implement the data and analytics solutions.

References and Methodology

Gartner used a variety of methodologies to create the information in this report, including vendor briefings, vendor surveys and secondary research, such as publicly available information that included, but was not limited to, published company announcements and financial reports. In addition, regular review of service provider strategies and positioning throughout the year contributed to this point of view. Factual information was reviewed by the profiled service providers.


1 The 2017 Gartner CIO Survey. The survey was conducted between 8 May 2016 and 9 July 2016, based on a number of hypothesis developed by the Gartner CIO research community. The sample included 2,598 organizations from 93 countries around the world (39% from North America, 11% from Latin America, 29% from EMEA and 21% from Asia/Pacific).

2 Gartner Research Circle Survey was conducted via an online survey from 7 October 2016 to 19 October 2016 among Gartner Research Circle Members — a Gartner-managed panel composed of IT leaders, product managers and technical professionals. In total, 152 members participated (includes completes and terminates); 80 members completed the survey. Qualified participants included business end users with either an IT or business focus as a primary role. The survey was developed collaboratively by a team of Gartner analysts and was reviewed, tested and administered by Gartner's Research Data Analytics team.

3 Gartner defines a data and analytics (D&A) platform as a platform that contains information management and analytical capabilities. Data management programs and analytical applications fuel data-driven decision making, and algorithms automate discovery and action (see "Building a Digital Business Technology Platform" ). From Gartner's latest "Magic Quadrant for Business Analytics Services, Worldwide," service providers are all adding AI and machine-learning technologies onto their data and analytics platforms and solutions, making these platforms more "intelligent."

4 Intelligent automation is the umbrella term for a variety of strategies, skills, tools and techniques, in conjunction with artificial intelligence (AI) technologies, that service providers are using to:

  • Reduce internal cost and/or increase quality of service delivery by replacing and augmenting labor.

  • Help clients ideate use cases, select technologies, curate data, build and train models, deploy solutions, assess and mitigate risks, and change talent and processes to successfully adopt new AI solutions.

5 Deloitte Greenhouses are corporate innovation labs intended to help clients with their complex problems. These labs use a combination of behavioral science, analytics, technology and facilitation to solve clients' business challenges.