If you’re an executive, manager, or team leader, one of your toughest responsibilities is managing and organizing your analytics initiative.
The days of business as usual are over. The cost of data generation is falling. The cost of collection and storage is also falling. The speed of insight-to-action is increasing. The bottleneck is clearly shifting from transaction processing to Analytics & Insight-driven Action.
Here are just a few examples of analytics at work:
- Target predicts customer pregnancy from shopping behavior, thus identifying prospects to contact with offers related to the needs of a newborn’s parents.
- Tesco (UK) issues 100 million personalized coupons annually at grocery cash registers across 13 countries. Predictive analytics increased redemption rates by a factor of 3.6.
- Netflix (Cinematch, Max) predicts which movies you will like based on what you watched.
- 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).
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.
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 and to what degree the tasks and KPIs are standardized.
- Legacy BI modernization – a growing problem of enhancing or wrapping the old to produce new.
- Emerging technology areas like Mobile BI…using an “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. 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 serve many industries, including retail, telecommunications, healthcare; and others provide clients with domain expertise in database-driven marketing and customer segmentation.
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, a survey conducted by LiquidHub 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).
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.
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? These questions usually dictate 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 is ambiguity 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.
- Emerging technology expertise – can they help innovate around new data sources like mobile or hyper-connected “Internet of Customers”?
- 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).
- Hot geographic areas with a lot of startups like San Francisco or New York – the rates may be much higher….supply vs. demand.
- China, especially Shanghai, is a good place for analytical talent in my experience. India is also a good location with different Indian Statistical Institutes (where sound engineering firm Bose came from) also has good cheap talent. At LiquidHub we built an actuarial center of excellence in New Delhi which worked well.
- Offshore analytics consultants (India will be around the $30-$75 per hour range – pay premium only for IIT and IIM educated personnel; Indian Statistical Institute (ISI) also generates good graduates).
Resource costs depend on domain expertise and analytics niche. Niches include: Predictive analytics; Behavioral analytics; Risk analytics; Sales & Marketing analytics, Social Media analytics, or Web analytics.
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 allows 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. 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.
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?
- 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 ramp-up 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 ramp up around technology platforms like SAP and Oracle more easily than around use cases like marketing analytics.
- For more challenging use cases like recommendation engines, the next best offer which requires more sophisticated modeling (simulation, optimization, time series etc.) – most vendors probably can assemble a small team but may not be able to scale easily beyond 10.
- Domain modeling expertise – architects and skilled project managers tend to be the hardest skills to find.
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.
- 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. This 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.
About the Author: Ravi Kalakota Ph.D, is a Partner at LiquidHub, a digital integrator with operations in North America, Asia, and Europe. He has over 20 years of experience as a global CIO, CTO (for Mercer – the Health, Retirement, Talent and Investment consulting services firm of Marsh & McLennan Companies); Managing Director with Alvarez & Marsal Business Consulting, a premier restructuring and performance improvement firm; and as CEO for several technology research and consulting firms.
Ravi has co-authored 10 books on e-commerce, e-business, mobile, web services, and global outsourcing, including Offshore Outsourcing: Business Models, ROI and Best Practices and Global Outsourcing: Executing an Onshore, Nearshore or Offshore Strategy.
For more detailed descriptions and vendor listings for Data Science and Analytics Outsourcing – Vendors, Models, Steps see Ravi Kalakota’s blog at: http://practicalanalytics.wordpress.com/2013/11/06/data-science-and-analytics-outsourcing-vendors-models-steps/