- Data-driven business processes are becoming essential for companies as data generation and analytics capabilities grow increasingly important.
- Many companies are looking to outsource their analytics and data science functions to meet demand for faster innovation while overcoming fragmented in-house solutions.
- There are various models for outsourcing analytics, including project-based work, staff augmentation, and creating centers of excellence either onshore, offshore, or in a hybrid model. Key decisions include what capabilities to outsource and who will manage the outsourcing process.
Just as computing and the World-Wide Web progressed through stages of maturity on the way to full acceptance, artificial intelligence is destined to do the same. By understanding the comparable challenges that were overcome and benefits achieved with earlier technologies, organizations can better see today where AI is heading and ensure that they are properly positioned to reap its full value.
The Work Ahead in Manufacturing: Fulfilling the Agility MandateCognizant
According to our research, manufacturers are well ahead of other industries in their IoT deployments but need to marshal the investment required to meet today’s intensified demands for business resilience.
Policy Administration Modernization: Four Paths for InsurersCognizant
The pivot to digital is fraught with numerous obstacles but with proper planning and execution, legacy carriers can update their core systems and keep pace with the competition, while proactively addressing customer needs.
Cognizant is the only company to earn a place in the list of Forbes fastest growing technology companies every year since the list’s inception. Its intriguing growth leaves us inquisitive – is there a framework to excel? Has Cognizant found the same? It is known that the book Built to Last by Jim Collins and Jerry Porras influenced Francisco D’Souza (CEO of Cognizant) the most. He is committed to establish a “cult like” culture focused on core values. But, beyond this, does their dual mandate of run better and run different have any role in their monumental growth? Cognizant is not only preaching about helping their clients to transform in order to run better and run different but also walking the talk by practicing the dual mandate within organization from its early days. This paper digs into Cognizant’s history and current trends to understand what they have done to run better and run different.
Realising Digital’s Full Potential in the Value ChainCognizant
When we spoke with executives across Europe who lead digitising efforts, they described a diverse range of deployments, but digital can, and must, deliver far more than it has so far. In this ebook, we explore how businesses can explore digital's full potential across their value chain.
Business Capital Planning PowerPoint Presentation SlidesSlideTeam
Download our ready to use business capital planning PowerPoint presentation slides to represent the planning process utilized to determine the business long term investments. Our research analysts have researched the content of this presentation, and our group of PowerPoint designers have designed this presentation. This capital improvement plan PPT presentation covers a slide on numerous relevant subjects such as introduction, functional areas overview, ERP system architecture, task categories of ERP systems, ERP project progress by stage, overview of implementation process, planning and selection phase, implementation phase, enterprise resource planning funnel, tuning of concept, situational analysis-basic target concept, software selection process, and software selection criteria. It also includes a template on software selection criteria, realization and implement, v model for implementation of ERP system, tips for selecting ERP system, ERP criteria list-technical requirement, and ERP implementation-selection phase. We aim to provide the presentation slides that help customers win the heart of the audience and achieve end goals. Using this presentation PPT, you will be able to explain the concept of investment appraisal. Finetine the frequency with our Business Capital Planning PowerPoint Presentation Slides. Your views will be recieved loud and clear.
Five Priorities for Quality Engineering When Taking Banking to the CloudCognizant
As banks move to cloud-based banking platforms for lower costs and greater agility, they must seamlessly integrate technologies and workflows while ensuring security, performance and an enhanced user experience. Here are five ways cloud-focused quality assurance helps banks maximize the benefits.
Just as computing and the World-Wide Web progressed through stages of maturity on the way to full acceptance, artificial intelligence is destined to do the same. By understanding the comparable challenges that were overcome and benefits achieved with earlier technologies, organizations can better see today where AI is heading and ensure that they are properly positioned to reap its full value.
The Work Ahead in Manufacturing: Fulfilling the Agility MandateCognizant
According to our research, manufacturers are well ahead of other industries in their IoT deployments but need to marshal the investment required to meet today’s intensified demands for business resilience.
Policy Administration Modernization: Four Paths for InsurersCognizant
The pivot to digital is fraught with numerous obstacles but with proper planning and execution, legacy carriers can update their core systems and keep pace with the competition, while proactively addressing customer needs.
Cognizant is the only company to earn a place in the list of Forbes fastest growing technology companies every year since the list’s inception. Its intriguing growth leaves us inquisitive – is there a framework to excel? Has Cognizant found the same? It is known that the book Built to Last by Jim Collins and Jerry Porras influenced Francisco D’Souza (CEO of Cognizant) the most. He is committed to establish a “cult like” culture focused on core values. But, beyond this, does their dual mandate of run better and run different have any role in their monumental growth? Cognizant is not only preaching about helping their clients to transform in order to run better and run different but also walking the talk by practicing the dual mandate within organization from its early days. This paper digs into Cognizant’s history and current trends to understand what they have done to run better and run different.
Realising Digital’s Full Potential in the Value ChainCognizant
When we spoke with executives across Europe who lead digitising efforts, they described a diverse range of deployments, but digital can, and must, deliver far more than it has so far. In this ebook, we explore how businesses can explore digital's full potential across their value chain.
Business Capital Planning PowerPoint Presentation SlidesSlideTeam
Download our ready to use business capital planning PowerPoint presentation slides to represent the planning process utilized to determine the business long term investments. Our research analysts have researched the content of this presentation, and our group of PowerPoint designers have designed this presentation. This capital improvement plan PPT presentation covers a slide on numerous relevant subjects such as introduction, functional areas overview, ERP system architecture, task categories of ERP systems, ERP project progress by stage, overview of implementation process, planning and selection phase, implementation phase, enterprise resource planning funnel, tuning of concept, situational analysis-basic target concept, software selection process, and software selection criteria. It also includes a template on software selection criteria, realization and implement, v model for implementation of ERP system, tips for selecting ERP system, ERP criteria list-technical requirement, and ERP implementation-selection phase. We aim to provide the presentation slides that help customers win the heart of the audience and achieve end goals. Using this presentation PPT, you will be able to explain the concept of investment appraisal. Finetine the frequency with our Business Capital Planning PowerPoint Presentation Slides. Your views will be recieved loud and clear.
Five Priorities for Quality Engineering When Taking Banking to the CloudCognizant
As banks move to cloud-based banking platforms for lower costs and greater agility, they must seamlessly integrate technologies and workflows while ensuring security, performance and an enhanced user experience. Here are five ways cloud-focused quality assurance helps banks maximize the benefits.
Evolving Role of System z in the Application EconomyCA Technologies
Join Michael Madden, CA Technologies Mainframe Business General Manager, for a preview of the amazing innovations that are transforming the role of System z in the application economy. From accelerating application delivery and managing complex big data environments, to preventing loss of critical data, discover how you can combine new technologies with your existing software investments to drive business value.
For more information on Mainframe solutions from CA Technologies, please visit: http://bit.ly/1wbiPkl
The Work Ahead: Transportation and Logistics Delivering on the Digital-Physic...Cognizant
The T&L industry appears poised to accelerate its long-overdue modernization drive, as the pandemic spurs an increased need for agility and resilience, according to our study.
Industry 4.0 is the name of the next industrial revolution which is fueled by the advancement of digital technologies. It
is dramatically changing how companies engage in business activities. As a result, the disruptive nature of Industry 4.0
demands a reassessment of the requirements for IT. On the one hand, there is the possibility that the responsibilities of Chief Information Officers (CIOs) could be taken over by other executives such as the Chief Digital Officer (CDO) or the Chief Technology Officer (CTO). On the other hand, this
recent development creates entirely new perspectives for positioning themselves and their IT departments
within the business.
The impact of digital technologies is reaching a magnitude at which IT is considered a substantial
business driver, potentially placing CIOs in the driver’s seat.
Getting Ahead With AI: How APAC Companies Replicate Success by Remaining FocusedCognizant
Changing market dynamics are propelling Asia-Pacific businesses to take a highly disciplined and focused approach to ensuring that their AI initiatives rapidly scale and quickly generate heightened business impact.
Operations Workforce Management: A Data-Informed, Digital-First ApproachCognizant
As #WorkFromAnywhere becomes the rule rather than the exception, organizations face an important question: How can they increase their digital quotient to engage and enable a remote operations workforce to work collaboratively to deliver onclient requirements and contractual commitments?
Shared Service Centers: Risks & Rewards in the Time of CoronavirusCognizant
Our recent research reveals that organizations are reassessing the pros and cons of captive services. Companies are twice as likely to reduce than increase their use of shared service centers.
Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Pr...Cognizant
Organizations rely on analytics to make intelligent decisions and improve business performance, which sometimes requires reproducing business processes from a legacy application to a digital-native state to reduce the functional, technical and operational debts. Adaptive Scrum can reduce the complexity of the reproduction process iteratively as well as provide transparency in data analytics porojects.
Engineering the Next-Gen Digital Claims Organisation for Australian General I...Cognizant
In recent years, insurers have invested in technology platforms and process improvements to improve
claims outcomes. Leaders will build on this foundation across the claims landscape, spanning experience,
operations, customer service and the overall supply chain with market-differentiating capabilities to
achieve sustainable results.
Selecting a Software Solution: 13 Best Practices for Media and Entertainment ...Cognizant
When selecting commercial off-the-shelf software (COTS), companies in the increasingly digitally-based media and entertainment industry need to develop a detailed advance plan, obtain support from all stakeholders and continuously monitor vendor performance against critical expectations, best practices and business requirements.
Don't let the common issues catch you out. M&A IT projects are difficult however the issues tend to be common ones. In this whitepaper we help guide you through them so come Day One you have a smile on your face and not a frown.
Learning & Development: A Prescriptive Vision for Accelerating Business SuccessCognizant
Corporate learning is increasingly critical to business, but traditional approaches are inefficient, overly rigid, fragmented and unconnected from employees' daily work - and thus ripe for transformation - as recent research reveals.
IoT: Powering the Future of Business and Improving Everyday LifeCognizant
New survey shows IoT at scale is a critical path, but many companies struggle to realize value. See how 10 companies are overcoming these challenges and succeeding in the new normal.
AI in Media & Entertainment: Starting the Journey to ValueCognizant
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
Profitability in the Direct-to-Consumer Marketplace: A Playbook for Media and...Cognizant
Amid constant change, industry leaders need an upgraded IT infrastructure capable of adapting to audience expectations while proactively anticipating ever-evolving business requirements.
Evolving Role of System z in the Application EconomyCA Technologies
Join Michael Madden, CA Technologies Mainframe Business General Manager, for a preview of the amazing innovations that are transforming the role of System z in the application economy. From accelerating application delivery and managing complex big data environments, to preventing loss of critical data, discover how you can combine new technologies with your existing software investments to drive business value.
For more information on Mainframe solutions from CA Technologies, please visit: http://bit.ly/1wbiPkl
The Work Ahead: Transportation and Logistics Delivering on the Digital-Physic...Cognizant
The T&L industry appears poised to accelerate its long-overdue modernization drive, as the pandemic spurs an increased need for agility and resilience, according to our study.
Industry 4.0 is the name of the next industrial revolution which is fueled by the advancement of digital technologies. It
is dramatically changing how companies engage in business activities. As a result, the disruptive nature of Industry 4.0
demands a reassessment of the requirements for IT. On the one hand, there is the possibility that the responsibilities of Chief Information Officers (CIOs) could be taken over by other executives such as the Chief Digital Officer (CDO) or the Chief Technology Officer (CTO). On the other hand, this
recent development creates entirely new perspectives for positioning themselves and their IT departments
within the business.
The impact of digital technologies is reaching a magnitude at which IT is considered a substantial
business driver, potentially placing CIOs in the driver’s seat.
Getting Ahead With AI: How APAC Companies Replicate Success by Remaining FocusedCognizant
Changing market dynamics are propelling Asia-Pacific businesses to take a highly disciplined and focused approach to ensuring that their AI initiatives rapidly scale and quickly generate heightened business impact.
Operations Workforce Management: A Data-Informed, Digital-First ApproachCognizant
As #WorkFromAnywhere becomes the rule rather than the exception, organizations face an important question: How can they increase their digital quotient to engage and enable a remote operations workforce to work collaboratively to deliver onclient requirements and contractual commitments?
Shared Service Centers: Risks & Rewards in the Time of CoronavirusCognizant
Our recent research reveals that organizations are reassessing the pros and cons of captive services. Companies are twice as likely to reduce than increase their use of shared service centers.
Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Pr...Cognizant
Organizations rely on analytics to make intelligent decisions and improve business performance, which sometimes requires reproducing business processes from a legacy application to a digital-native state to reduce the functional, technical and operational debts. Adaptive Scrum can reduce the complexity of the reproduction process iteratively as well as provide transparency in data analytics porojects.
Engineering the Next-Gen Digital Claims Organisation for Australian General I...Cognizant
In recent years, insurers have invested in technology platforms and process improvements to improve
claims outcomes. Leaders will build on this foundation across the claims landscape, spanning experience,
operations, customer service and the overall supply chain with market-differentiating capabilities to
achieve sustainable results.
Selecting a Software Solution: 13 Best Practices for Media and Entertainment ...Cognizant
When selecting commercial off-the-shelf software (COTS), companies in the increasingly digitally-based media and entertainment industry need to develop a detailed advance plan, obtain support from all stakeholders and continuously monitor vendor performance against critical expectations, best practices and business requirements.
Don't let the common issues catch you out. M&A IT projects are difficult however the issues tend to be common ones. In this whitepaper we help guide you through them so come Day One you have a smile on your face and not a frown.
Learning & Development: A Prescriptive Vision for Accelerating Business SuccessCognizant
Corporate learning is increasingly critical to business, but traditional approaches are inefficient, overly rigid, fragmented and unconnected from employees' daily work - and thus ripe for transformation - as recent research reveals.
IoT: Powering the Future of Business and Improving Everyday LifeCognizant
New survey shows IoT at scale is a critical path, but many companies struggle to realize value. See how 10 companies are overcoming these challenges and succeeding in the new normal.
AI in Media & Entertainment: Starting the Journey to ValueCognizant
Up to now, the global media & entertainment industry (M&E) has been lagging most other sectors in its adoption of artificial intelligence (AI). But our research shows that M&E companies are set to close the gap over the coming three years, as they ramp up their investments in AI and reap rising returns. The first steps? Getting a firm grip on data – the foundation of any successful AI strategy – and balancing technology spend with investments in AI skills.
Profitability in the Direct-to-Consumer Marketplace: A Playbook for Media and...Cognizant
Amid constant change, industry leaders need an upgraded IT infrastructure capable of adapting to audience expectations while proactively anticipating ever-evolving business requirements.
A report providing an overview of the Financial Technology startup landscape, graphical trends and insights, and recent funding and exit events. Contact info@venturescanner.com to learn more!
Visual Analytics combines human intuition and data science to derive knowledge from the data in a very efficient, effective and easy way. Visual Analytics empowers your people to interact with the data and generate new insights.
I have been drinking from a virtual fire hose since joining my most recent technology company, Anametrix, a cloud-based digital analytics innovator. A whole new book opened for me on how digital analytics can both increase top line revenue and reduce spend by shining a very bright flashlight into marketing efforts.
We are all painfully aware of the data explosion problem. In 2011, the Gartner Group stated that information volume collected by businesses today is growing at a minimum 59% annually. The rapid adoption of social media has also caused customer data to explode in the last few years, creating entirely new challenges for marketers. It is now imperative for organizations to think differently to accommodate the variety, volume, and velocity of their growing customer-related data.
This is where my recent experiences come in: I have personally seen how digital analytics can harness the power of massive amounts customer-related data. It can literally simplify the accelerating complexity by providing deep visibility – as well as clarity – into the effectiveness of various marketing efforts, across both online and offline channels.
I will now outline the role of IT and CFO in adopting cloud-based digital analytics solutions, discuss the benefits as well as challenges of moving to this emerging category, and provide some illustrative examples on how digital analytics can transform your marketing organization.
Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
The evolution of the Architecture of Enterprises (AKA Enterprise Architecture) Leo Barella
We are in the era of competitive advantage through smart information and analytics. Process automation and leveraging transactional systems is a "thing of the past". To advance organizations need to start designing their architecture leveraging microservices and focus on data management / analytics efficiency.
Booz Allen Hamilton uses its Cloud Analytics Reference Architecture to build technology infrastructures that can withstand the weight of massive datasets – and deliver the deep insights organizations need to drive innovation.
While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate.
Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees.
Discovering real business opportunities and achieving desired outcomes can be elusive.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
Enterprise Architecture - An Introduction Daljit Banger
The Slides are from my session at "An Evening of Enterprise Architecture Awareness" held at theUniversity of Sussex Hosted by the BCS Local Chapter and facilitated by the BCS EA Specialist Group.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
Similar to Data Science And Analytics Outsourcing – Vendors, Models, Steps by Ravi Kalakota - May 28, 2015 (20)
Snap Inc's Q1 2017 Earnings Report released to the media showing the results of operations for the three months ending March 31, 2017 including Consolidated Statement of Operations, Consolidated Balance Sheet and Consolidated Cash Flow Statement.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Data Science And Analytics Outsourcing – Vendors, Models, Steps by Ravi Kalakota - May 28, 2015
1.
Data Science and Analytics Outsourcing
– Vendors, Models, Steps
By Ravi Kalakota
May 28, 2015
Data-driven business processes are not anice-to-havebut a need-to-havecapability 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.
2. Click Image To Enlarge
Better/Faster/Cheaper Analytics Execution
Industrialization of analyticsis 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?
3. 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.
Click Image To Enlarge
Why Outsource or Outtask 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).
4. 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.
Click Image To Enlarge
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
5. ● 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.
Click Image To Enlarge
For each area and business need (transformation vs. strategic vs. tactical) there are different vendors
that are a better fit. In this posting we 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.
6. The following figure from GenPact illustrates how a vendor thinks about Analytics Outsourcing.
Click Image To Enlarge
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.
7. 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).
Click Image To Enlarge
8. 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.
Click Image To Enlarge
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?
9. Click Image To Enlarge
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…
11. 24. Idiro Technologies(Predictive modelling specialising in Social Network Analysis, Big
Data) – www.idiro.com.
25. WNS Analytics(acquired Marketics) (Marketing, Consumer Behavior Analytics)
–http://www.wns.com/Services/Cross-Industry-Solutions/Research-and-Anal
ytics.aspx
26. Opera Solutions (General – serves broad areas) –
http://www.operasolutions.com
27. Data Monitor(General – serves broad areas) – http://www.datamonitor.com/
28. Ipsos(Marketing Analytics) – http://www.ipsosasiapacific.com/
29. EXL Services(acquired Inductis) (General – focuses on broad areas)
–http://www.exlservice.com/
30. Meritus(Marketing, Customer Analytics) – http://www.meritusglobal.com/
31. Modelytics(Financial, Lending, Collections, Recovery, Retail Banking)
–http://www.modelytics.com/integrated/index.htm
32. Bridge i2i Analytics(Behavioral Modeling & Resource Planning)
–http://www.bridgei2i.com/
33. Cytel(Clinical & Pharma Analytics) – http://www.cytel.co.in/index.shtml
34. Neural Techsoft(Financial & Risk Analytics) – http://www.neuraltechsoft.com/
35. Vehere Interactive(Telecom, Financial) – http://www.vehereinteractive.com/
36. Aegis Global(General – focuses on broad areas)
–http://www.aegisglobal.com/section_level3.aspx?cont_id=eLxpcOMCwVY=
37. Datamatics(Financial, Insurance)
–http://www.datamatics.com/services/information-management/business-in
telligence-analytics
38. Marketelligent(CPG, Finance, Telecom Analytics)
–http://www.marketelligent.com/home/
39. TNS Global(Marketing Analytics) – http://www.tnsglobal.com
40. NettPositive Analytics(Marketing, Credit Risk Analytics)
–http://www.nettpositive.com/
41. Affine Analytics(Marketing Analytics) – http://www.affineanalytics.com/
42. EVALUESERVE(Financial, Life Sciences Analytics) – http://www.evalueserve.com
43. ZS Associates (Life Sciences, Pharma, Sales and Marketing) –
http://www.zsassociates.com/
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
12. 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
● Emerging 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.
13. ● 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)
14. 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
Communications
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 Ecommerce
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.
Retail
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.
15. 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 forKPIs for Financial Services Industry.
Government
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 forInformatics 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
16. 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, Sparkthat 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.
Original source:
http://practicalanalytics.co/2015/05/28/datascienceandanalyticsoutsourcingvendorsmodels
steps/