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May 28, 2015


Data Science and Analytics Outsourcing – Vendors, Models, Steps

by Ravi Kalakota

feldframework_whyData-driven business processes are not a nice-to-have but a need-to-have capability today. So, if you’re an executive, manager, or team leader, one of your toughest assignments is managing and organizing your analytics and reporting initiative.

The days of business as usual are over.  Data generation costs are falling everyday.  The cost of collection and storage is also falling.  The speed of insight-to-action business requirement is increasing.  Systems of Record, Systems of Engagement, Systems of Insight are being transformed with consumerization and digital.

With this tsunami of data and new applications, the bottleneck is clearly shifting from transaction processing to Analytics & Insight-driven “sense-and-respond” Action. This slide from IBM’s Investor Briefing summarizes the data-driven transformation underway in most businesses.


Better/Faster/Cheaper Analytics Execution

Industrialization of analytics is the new buzzword. Overcoming the jumble of point solutions is a non-trivial challenge in a big firm.  Disparate vendors, disparate capabilities, different interfaces, all acquired over a long period of time.

To meet demand for faster/better/cheaper  innovation around analytics, CFOs and CIOs are rethinking their silo’d sourcing strategies, fragmented tech budgets aligned against one-off projects, and are looking at new ways of doing things via out-tasking,  IT outsourcing and business process outsourcing their Analytics and Data Science functions.

The “should we or shouldn’t we outsource data science” discussion is heating up in board-rooms and executive suites  as analytics becomes core to the firm, C-level execs have to consolidate efforts for delivering the same services to different groups within an organization.

As managers look to execute on outsourcing strategy they have many structural options depending on which variable they want to optimize around (cost, quality, productivity, innovation, time to market):

  • Outsource Analytics vs. Building a Shared Services Analytics Function at the LoB or corporate level?
  • Outsource the Analytics Platform development and support or keep it in-house in the IT function or LoB
  • Outsource the modeling and data science part or hire/build the capabilities in-house?
  • Augment the current staff with domain specific expertise or hire FTEs?
  • Centralize analytics in a shared services model or let the LoBs do their thing?

Build vs. Buy vs. Lease (as-a-service cloud solutions) — What is the right configuration… the answer depends on the organization – internal politics, credibility of IT leadership, ability to execute, maturity of business requirements and so on.


Why Outsource or Out-task Data Science or Analytics?

Data science and analytics capability is becoming table stakes in businesses that haven’t traditionally been thought of as data-focused industries. Who would have thought that maintenance, online dating or renting movies would be an analytically intensive business.

However, enterprise IT is often slow to react. In many enterprise IT budgets, the cost of operations (run the business) is the fastest growing line item—consuming 70+% of budget dollars.  IT organizations are being asked respond to (grow the business or change the business) by enabling new analytics innovation opportunities,  regulatory demands, and building shared private cloud infrastructure that is comparable to Amazon Web Services.  A herculean task for IT to keep up.

The race to implement innovation is often a driver for outsourcing…acquire the right mindset, toolset, skillset and dataset… to get the job done. LoB leaders, instead of waiting for IT,  drive top-line growth by seeking the most direct path to solutions that will support their initiatives. They want solutions that deliver a quick ROI,  can be implemented quickly and affordably, without a huge drain on IT (a source they may not have much control over).

The result of this approach is a one-off  pragmatic, get something-to-market fractured environments. Typical scenario in a large firm… business units leveraging different service providers, different storage and processing technologies, and different front-end visualization tools.  In some cases, I have seen organizations with multiple teams contracting with different vendors within the same business units to solve similar problems (e.g., customer retention/attrition, next best offer/action), creating a nightmare for IT who have to support multiple overlapping solutions concurrently in production and customer-facing organizations getting conflicting insights from the different solutions.

This scenario typically forces a centralization and subsequent outsourcing discussion.  But the nature of centralization (from BI Platform or Datawarehousing) is changing.  See figure below.


What are Some Areas to Outsource?

The different areas of data sciences or analytics outsourcing (based on lifecycle of a project) include:

  • Analytics Consulting (strategy, platform selection, model development, decision process re-engineering)
  • Analytics Platform Deployment, Customization and Integration
  • Analytics “as-a-service” platform strategies—by leveraging a common set of development, production, and support capabilities
  • Analytics Program Staffing — resource augmentation (salary and intellectual arbitrage), project and program management
  • Domain and Function Modeling Knowhow — depends on how standardized the tasks and KPIs are
  • Dashboard Populating and Creating – data collation, cleansing and dashboard creation
  • Legacy BI modernization – a growing problem of enhancing or wrapping the old to produce new
  • Emerging technology areas like Mobile BI…using a “innovation-as-a-service” model
  • Data Quality – With data increasingly critical to business strategy, the costs of poor quality data,  fragmentation, and lack of lineage take center stage.


For each area and business need (transformation vs. strategic vs. tactical) there are different vendors that are a better fit.  In this posting I examine the frequently asked executive questions around Outtasking or Outsourcing Analytics (and Data Sciences) – models of engagement, cost models etc.  Also included is a list of Analytics Outsourcing Providers that I have been tracking.   Most of these firms are evolving their capabilities but are rooted in providing BI and Analytics capabilities on a staffing or project basis.

Outsourced analytic providers serving many industries, including retail, telecommunications, healthcare and others, provide clients with domain expertise in database-driven marketing and customer segmentation.

The following figure from GenPact illustrates how a vendor thinks about Analytics Outsourcing.


What are the models of Analytics Outsourcing engagement?

Regardless of the services—data management, business intelligence and reporting, research, advanced predictive analytics services, and analytics consulting services; you will have to pick a engagement model.

  • Project based model
  • Competency based Staff Augmentation based on salary arbitrage
  • Creating a Analytics center of excellence (CoE) staffed by your team and vendor team
  • Creating a hybrid CoE (partly onshore in the corporation + partly offshore at a captive or third party vendor)
  • SLA or Outcome based is the most complex engagement model.
  • Pay per use “as-a-service” Cloud models – providers are responding to the continuing shortage of data scientists by offering data science know-how as a cloud service.

In some cases you will need mixed models. For instance, it’s important to keep in mind that 80% of the costs for data-related projects get spent on data preparation – mostly on cleanup data quality issues. Unfortunately data related budgets for many companies tend to go into platforms, frameworks which can only be used after you have quality data.

Who makes the Outsourcing Decision?

Who handles the management and implementation of  analytics in the enterprise?  CIO, CFO, CDO, LoB or marketing executives?  

Most enterprises are struggling with the right operating model for analytics and data science.  This was relatively straightforward with BI and data management which was often under a global CIO or CTO’s umbrella.

Data sciences and analytics while seen as potential game changer seems to have a fragmented set of buyers: Line of Business, Function or even IT?  Who is on point to fund the project?  Depends on whether its a departmental initiative or a cross-silo initiative.

Analytics is increasingly business driven. Why? With the right architecture and execution, analytics can have a powerful impact on customer engagement, frontline business units,  and operations. Also as speed-to-market and innovation become critical,  getting the right solutions and implementing them is typically a business initiative done with outside vendors (often outside the purview of a typical CIO or CTO.)

In a recent Deloitte study, “The Analytics Advantage,” highlights how diverse the initiative ownership is. Executives in many different types of roles own the  analytics initiatives within their enterprises, and no clear title emerges as the dominant owner (see below).


Who are the industry leaders in this space?

This is a tough question to answer without more context around problem or use-case.  But in general, our survey of market leaders shows:

  • Broad “super market” services firms with a broad array of capabilities – Accenture, IBM, Deloitte
  • The growing pure-play analytics firms include:  Mu-Sigma, Opera, EXL Analytics
  • Offshore vendors who have built their model around analytics – Genpact (spin out from GE)
  • Domain specific vendors — Dunnhumby (retail analytics); Acxiom (database marketing)

What are the Range of Outsourcing Services Offered?  Increasingly vendors are able to offer horizontal and vertical solutions effectively packaged in a variety of configurations. Vendors are becoming more sophisticated as they gain experience handling large, complex datasets.  The services range from Data Sciences ->  expertise in various techniques -> toolsets -> vertical specific expertise.


What are the range of Technical Skills?

There has been explosion of innovation in the Hadoop Ecosystem.  Companies are racing to adopt new open source tools to gain a competitive advantage.  Does your vendor have a deep enough bench in these projects?  Do they have architecture skills to be put together effective solutions around target use-cases?


Technical toolkits around Big Data and Analytics include: RDBMS, Open source Hadoop distribution (e.g., Apache Hadoop), Commercial Hadoop distribution (Cloudera, Microsoft, MapR, IBM, …),  Cloud-based Big Data platform (AWS, Rackspace, …), Cassandra,  MongoDB, Hbase,  Hive, Kafka, Pig, Search (ElasticSearch, Solr, Lucene, …),  Spark,  Storm, and  Zookeeper.

What is Data science?  Data Science is an umbrella term that encapsulates the extraction of timely, actionable  information from diverse data sources. It covers data collection, data modeling and analysis, and problem solving and decision making. It incorporates and builds on techniques and theories from many fields, including mathematics, statistics,  pattern recognition and learning, advanced computing, visualization, and uncertainty modeling with the goal of extracting meaning from data and creating data products.

Data science is  often used interchangeably with business analytics, although it is becoming more common. Data science seeks to use all available and relevant data to effectively tell a story that can be easily understood by non-practitioners.

Data science is nothing new. But digital has increasingly created new opportunities where scientific methods can be applied to massive, real world data sets.

See below for a partial list of Data Science and Analytics Services Providers…

List of Data Science Services Providers 

  1. IBM Analytics (General – serves broad areas) –
  2. Mu-Sigma (Sales, Marketing, Supply Chain, and Risk Analytics) –
  3. LatentView (Marketing, Risk, Customer Management) –
  4. HCL Technologies (General – serves broad areas) –
  5. Accenture (General – serves broad areas) –
  6. Genpact Analytics (acquired Symphony Technology Group) (General – serves broad areas) –
  7. Cognizant Analytics (General – serves broad areas) –
  8. TCS Analytics (General – serves broad areas) –
  9. Wipro Analytics (General – serves broad areas) –
  10. Amberoon Inc. (Solutions for business use cases ) –
  11. McKinsey Analytics Knowledge Centre (General – serves broad areas) –
  12. Deloitte Analytics (General – serves broad areas) –
  13. PwC Analytics (General – serves broad areas) –
  14. AbsolutData (Consumer Behavior Analytics) –
  15. Fractal Analytics (Customer Loyalty, Operations) –
  16. iCreate (Banking Analytics) –
  17. Dunhumby (Retail Analytics) –
  18. Global Analytics (Credit Risk, Financial, Lending) –
  19. Manhattan Systems (Retail Analytics)
  20. Capillary Technologies (Retail Analytics) –
  21. Nabler (Online Retail Analytics) –
  22. Activecubes (Sales, Marketing, Supply Chain, Operations) –
  23. ICRA Technology Services (General – serves broad areas) –
  24. Idiro Technologies (Predictive modelling specialising in Social Network Analysis, Big Data) –
  25. WNS Analytics (acquired Marketics) (Marketing, Consumer Behavior Analytics) –
  26. Opera Solutions (General – serves broad areas) –
  27. Data Monitor (General – serves broad areas) –
  28. Ipsos (Marketing Analytics) –
  29. EXL Services (acquired Inductis) (General – focuses on broad areas) –
  30. Meritus (Marketing, Customer Analytics) –
  31. Modelytics (Financial, Lending, Collections, Recovery, Retail Banking) –
  32. Bridge i2i Analytics (Behavioral Modeling & Resource Planning) –
  33. Cytel (Clinical & Pharma Analytics) –
  34. Neural Techsoft (Financial & Risk Analytics) –
  35. Vehere Interactive (Telecom, Financial) –
  36. Aegis Global (General – focuses on broad areas) –
  37. Datamatics (Financial, Insurance) –
  38. Marketelligent (CPG, Finance, Telecom Analytics) –
  39. TNS Global (Marketing Analytics) –
  40. NettPositive Analytics (Marketing, Credit Risk Analytics) –
  41. Affine Analytics (Marketing Analytics) –
  42. EVALUESERVE (Financial, Life Sciences Analytics) –
  43. ZS Associates (Life Sciences, Pharma, Sales and Marketing)  –

Issues to Consider in Picking an Analytics Service Provider?

  • Who handles the data;  How sensitive is the data; how unusual (and competitive advantage based) are the analytics usually dictates the engagement model
  • Capability of the team:  Most firms and vendors are capable of report generation, descriptive statistics or dashboard generation
  • Ability to Analyze and interpret results: Moving to more complex predictive models requires domain expertise and use case knowhow….most vendors claim to have this but very rarely do.
  • How easy are they to work with?  Do you have to spoon feed them or ambiguity is ok. Since clients are looking for faster turn-arounds for more sophisticated insights  on continuously increasing amounts of data, vendors need to deliver solutions that will scale better with lower cost of ownership to meet their clients’ internal service-level agreements.
  • Experience with large complex data sets or ability need to mix and match different types of data
  • MobileFirstEmerging Technology Expertise… can they help innovate around new data sources like Mobile or hyper-connected “Internet of Customers”.

What are Different Resource Cost models?

  • Onshore consultants (Data scientists will be in the $250-350 per hour range);  Specialized domains (Risk Analytics) will carry a 30% premium ($300-$600 per hour fees).
  • Also hot geographic areas with lot of startups like San Francisco or New York…the rates will be much higher…. supply vs. demand.
  • China, especially Shanghai, is a good place for analytical talent in my experience.  India also with different Indian Statistical Institutes (where sound engineering firm Bose came from) also has good cheap talent.  We built an actuarial center of excellence in New Delhi which worked well.
  • Offshore consultants (India will be around the $30-$75 per hour range  — good for dashboard creation and other commodity work… many people i spoke to are not sure about about offshore talent for generating complex analytical models and insights).

Resource costs depend on domain expertise and analytics niche:  Predictive analytics (Industry specific);  Behavioral analytics;  Risk analytics; Sales & Marketing analytics, Social media analytics,  Web analytics.

What are the different Pricing Models in Analytics Outsourcing?

The structure of the pricing for the outsourcing contract can be one of the following:

  • Cost Plus. This approach pays the supplier for its actual costs, plus a predetermined profit percentage. This plan allow little or no flexibility when business objectives and technology change during the life of the contract, nor does it give any incentive for the supplier to perform more effectively.
  • Unit Pricing. This is a set rate determined by the supplier for a particular level of service, and the client pays based on its usage. Paying for desktop maintenance based on the number of users is an example of this approach.
  • Fixed Price. Some buyers think this is the best approach, because they know exactly what the supplier’s price will be, even in the future. But the problem with this approach is that if the buyer does not adequately define the scope of the process and design effective metrics before signing the contract, too often the result will be that the supplier claims a particular service or service level is beyond the scope of the contract and then charges a premium for it.
  • Variable Pricing. This plan involves use of a fixed price at the low end of the supplier’s service, with variances based on higher service levels. Its effectiveness, again, depends on adequately defining scope of process and metrics.
  • Incentive-based (or performance-based) pricing. Here, the buyer provides incentives to encourage the supplier to perform at peak level (or complete a one-time project ahead of time, for example) by offering a bonus reward if the supplier performs well. This same plan works in ensuring that the supplier must pay a penalty if it does not perform to at least the “satisfactory” service level designated in the agreement. This plan is the one to use to ensure the supplier’s excellence in performance.
  • Risk/reward sharing. Here, the buyer and supplier each have an amount of money at risk and each stand to gain a percentage of the profits if the supplier’s performance is optimum and achieves the buyer’s objectives.

The buyer will select a supplier using a pricing model that best fits the business objectives the buyer is trying to accomplish by outsourcing.

What are the Measures of success ?

  • Effort based vs. Outcome based
  • For repeated analytics like Dashboard generation – one can have SLA, Quality and Errors as a measure of success.

How effective are vendors in scaling (upwards – more and downwards – less)?

  • Depends on whether the vendor is an IT vendor like TCS, Big 5 like Deloitte or pure-play analytics vendor like Mu-Sigma.   These vendors can rampup from a standing start to 200 people in a few months.
  • For simple use cases and simple analytics – most vendors can ramp up to 30-50 people easily (made up of data management, cleansing/quality, BI report generation and Dashboards)
  • Vendors can also rampup around technology platforms like SAP, Oracle more easily than around use-cases like marketing analytics.
  • For more challenging use cases like recommendation engines, next best offer which require more sophisticated modeling (simulation, optimization, time series etc.) – most vendors probably can assemble a small team but not be able to scale easily beyond 10.
  • Domain modeling expertise, Architects and skilled project managers tend to be the hardest skills to find.

What are the Expected Benefits of Analytics Outsourcing?

  • Specialization, Focus, Speed-to-market  and Scale – tend to be the expected benefits.
  • Vendors may have proprietary IP and tools (see below for landscape view of different techniques)
  • Lower  cost by leveraging economies of scale  (often the sales pitch but seldom works in execution)
  • Better process quality through forced standardization (vendors force clients to standardize which requires re-engineering the way things are done)

Firms must not expect to outsource analytics and then just assume that the specifics will take care of themselves is a recipe for disaster. Managers must retain enough program management capability to enforce processes, communicate with all parties, and keep track of critical details.

Vertical Industry Specific Domain Expertise

See this blog posting for Use Cases for Big Data and Analytics


The communications industry is characterized by intense competition and customer attrition, or “churn.” Targeted marketing opportunities and the rapid response to behavior trends are paramount to the success of communications service providers in retaining existing customers and attracting new customers. Customer relationship management, or CRM, analyses need to be constantly and quickly performed, to enable service providers to market to at-risk customers before they churn, offer new products and services to those most likely to buy, and identify and manage key customer relationships. Other key analytical needs of communications service providers include call data record analysis for revenue assurance, billing and least-cost routing, fraud detection and network management.

Digital Media and E-commerce

For online businesses, the process of collecting, analyzing and reporting data about page visits, otherwise known as click stream analysis, is required for constant monitoring of website performance and customer pattern changes. In addition to needing to address the operational and customer relationship challenges faced by traditional retailers, digital media businesses must also analyze hundreds of millions or even billions of click stream data records to track and respond to customer behavior patterns in real time. Additionally, with online advertising becoming a major revenue generator, many digital media businesses and their advertisers need to understand who is looking at the advertisements and their actions as a result of viewing the advertisements. Fast analysis of online activity can enable better cross-selling of products, prevent customers from abandoning shopping carts or leaving the web site, and mitigate click stream fraud.


With thousands of products and millions of customers, many retailers need sophisticated systems to track, manage and optimize customer and supplier relationships. Targeted marketing programs often require the analysis of millions of customer transactions. To prevent supply shortages large retailers must integrate and analyze customer transaction data, vendor delivery schedules and radio frequency identification supply chain data. Other useful analyses for retail companies include “market basket” analysis of the items customers buy in a given shopping session, customer loyalty programs for frequent buyers, overstock/understock and supply chain optimization.

See this blog posting for KPIs for Retail Industry.

Financial Services

Financial services institutions generate terabytes of data related to millions of client purchases, banking transactions and contacts with marketing, sales and customer service across multiple channels. This data contains crucial business information on client preferences and buying behavior, and can reveal insights that enable stronger customer relationship management and increase the lifetime value of the customer. In addition, risk management and portfolio management applications require analysis of vast amounts of rapidly changing data for fraud prevention and loan analysis. With extensive compliance and regulatory requirements, financial institutions are required to retain an ever-increasing amount of data and need to make this data available for detailed reporting on a periodic basis.

See this blog posting for KPIs for Financial Services Industry.


As some of the largest creators and consumers of data, government agencies around the world need to access, analyze and share vast amounts ofup-to-date data quickly and efficiently. These agencies face a broad range of challenges, including identifying terrorist threats and reducing fraud, waste and abuse. Iterative analysis on many terabytes of data with high performance is crucial for achieving these missions.

Health and Life Sciences

Healthcare providers seek to analyze terabytes of operational and patient care data to measure drug effectiveness and interactions, improve quality of care and streamline operations through more cost-effective services. Pharmaceutical companies rely on data analysis to speed new drug development and increase marketing effectiveness. In the future, these companies plan to incorporate large amounts of genomic data into their analyses in order to tailor drugs for more personalized medicine.

See this blog post for Digital Health and Data.

See this blog post for Informatics in Healthcare

Analytics Infrastructure

The significant growth of enterprise data is fueling a need for additional storage and other information technology infrastructure to maintain and manage it. These technology needs are being further driven by a steady decline in data storage prices, which makes storing large data sets more economical.

As the volume of data continues to grow, enterprises have recognized the value in analyzing such data to significantly improve their operations and competitive position. They have also realized that frequent analysis of data at a more detailed level is more meaningful than periodic analysis of sampled data. In addition, companies are making analytic capabilities more widely available to a broad range of users across the enterprise for both strategic and tactical decision-making.

These factors have driven the demand for next generation data warehouses infrastructure like Hadoop, NoSQL, Spark that provide the critical framework for data-driven enterprise decision-making by way of business intelligence.

In Summary

Fortune recently reported, “Online help-wanted ads for data analysis mavens have shot up 46% since April 2011, and 246% since April 2009, to over 31,000 openings now, according to job-market trackers.”

The shortage of analysts  is driving companies to consider outsourcing their segments of this value chain.. “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions -> Operational Impact -> Financial Outcomes -> Value creation.”.

Clearly, choosing the right analytics providers (onshore or offshore) and structuring effective business relationships that deliver continuous value require managers to have a clear understanding of what they’re looking for and the potential risks involved.



 1) The Typical Transformation Journey…. applies to Analytics Outsourcing  (source – Charlie Feld)
2) Analytics competencies firms are racing to build

3) Shared Services is an alternate service delivery model that enables organizations to apply economic principles of demand and supply to internal business processes.  Shared services help consolidate efforts for delivering the same services to different groups within an organization.

Shared Services enables an organization to:

  1. Consolidate efforts for delivering the same services to different groups within an organization – economies of scale & competence;
  2. Be more commercial orientated; measure SSC-customer satisfaction and competitive offering based on service products;
  3. Utilize multiple delivery channels for services depending on the nature of the service, the recipient and the circumstances

4) Best Practices in Implementing  Analytics Outsourcing

  • Baseline performance of processes relevant for outsourcing (or centralization)
  • Harmonize and standardize business processes
  • Automate business processes via technology investments
  • Enable employees and business partners via self services and single-point- of-contact
  • Manage services effectively through a globally consistent service and operating model
  • Implement governance to internal client-provider relationship

5. A. Fisher, “Wanted: Data Scientists. No Math Chops? No Problem,” Fortune, May 10, 2013,

  • Life insurance companies calculate the likelihood an elderly insurance policy holder will die within 18 months in order to trigger end-of-life counseling.
  • Con Edison predicts energy distribution cable failure, updating risk levels that are displayed on operators’ screens three times an hour in New York City.
  • Linkedin (People you may know, Jobs you may like, Groups you may be interested in)

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9 Comments Post a comment
  1. Suresh Srinivasan
    Dec 30 2013

    Well written article and I read your blogs regularly. Would like to know your thoughts on Unified analytics platforms where data driven cognitive applications can be built much faster. Do you have any data on the market size, potential etc. Would be great if you share the same


  2. May 12 2014

    Ravi: You may want to add my firm’s name to your list. Knowledge Foundry (knowledge specializes in analytics services for retail and has a Customer Engagement product solution called Knolseed ( thanks


    • Ravi Kalakota
      May 12 2014

      Will do Raj.


  3. Viswanathan
    May 5 2015

    Do you have latest data on following 3rd para of yours? I wonder what it would be for now or as of Apr 2015:

    There are 3 billion people connected to the Internet. And it is not just about people. Think about 200 billion devices from sensors to machines all connected to the Internet. And think about the pace. Every 60 seconds, there will be 1 million Facebook updates, 265,000 snapchats, 2.5 million Google searches and 204 million emails sent.


  4. Nov 6 2015

    Great Article! Offshore Business Intelligence services are a great and cost-effective source for qualified BI professionals. During this month, we are offering $1,000 vouchers to new customers who need avaiable and experienced BI consultants. Read more!landing-page/z0uao


  5. Apr 21 2018

    Great list and article! You may want to add which is into data science driven digital data analytics specializing in Clickstream analytics solutions with a tag all approach for data collection.



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