When various researches on analytics predict an acute shortage of data scientist around the world, what they don’t realize is that most of this deficiency is going to be filled in India. In the last few years, the number of US based companies that had setup analytics competency centres in India have grown in numbers.

Last year alone, companies like Limited Brands, Lowe’s and Cargill have opened centres in India that primarily caters to the analytics and data science needs of parent company. It is a well-known fact today that a number of well-known Fortune 500 companies outsource analytics functions to India. According to a research done by Analytics India Magazine 2 years back, India accounts for 9% of analytics headcount worldwide. I actually see this pie growing.

Why outsource Analytics?

Analytics is at the same stage of adoption as what IT was exactly 15 years back. Higher demand for a relatively new field has led for a dearth of data scientists, leading to increasing wage levels in this industry. Moreover, expertise is analytics is slightly a different ball game than IT or other streams – it requires a combination of deep skills like statistics and mathematics, data, computer science, visualization, design bend and business knowledge.

While there are still some enterprises who consider analytics as an in-house activity, the arrival of major vendors in India with specialized processes has led to analytics outsourcing becoming a fastest growing verticals of the outsourcing industry.

Building a data science team is an expensive and time consuming process – experts need to recruited, tools acquired, use cases identified etc. The result of analytics in terms of value is usually not as evident as say creating an IT application with known results. It is difficult to convince business management to convince on analytics when the value is not quick and evident, and most in house analytics initiative fall short of funding. Outsourcing is a speedy, economical and effective mode to showcase this value.

Why outsource to India?

The analytics industry in India has gone through a “concept selling” phase in the last few years with a majority of clients who have so far utilized outsourced to India for primarily IT or process services.

India, as a geography, is an unmatched analytics hub because of its pool of talent in quantitative disciplines like mathematics and economics. While, the depth of analytics skillsets is contested by many, the good news is that India’s analytics talent base is expanding rapidly. Analytics education market is flourishing in India with dozens of master’s level course being curated by universities, technical and business schools and private players; some with world class quality.

Another useful, and often missed, feature is India’s adjacency to IT services that provides useful context and the technology background. As compared to other geographies, analytics service providers in India finds the IT play within data science, the easiest.

The Challenge in analytics outsourcing

All good things come with their set of challenges. Unlike IT/ITES, the conversation around analytics is primarily consulting driven. Analytics partners look to tackle problems in various areas of business for their clients. In choosing an analytics partner, focus should be on analytical and technical competence, and applying those competencies to solve real world issues.

The success of BPO/ IT outsourcing is well documented, though data science outsourcing is still finding its ground and a right business model. The unique nature of data sciences require a very different contract and pricing model.

Various outsourcing models have emerged recently –

• Project based model that execute clearly scoped out projects for a fixed price and a well-defined set of deliverables
• Competency based Staff Augmentation – Extension of your in-house team with a mix of experienced consultants and data scientists that help you scale and operationalize analytics.
• Build Operate and Transfer – variably recruit data scientists and analytics professionals, train and operate towards your business needs before absorbing them as ‘resources on own payroll’
• Creating a Analytics center of excellence (CoE) staffed by your team and vendor team
• Outcome based model

Conclusion

The “should we or shouldn’t we” argument to outsource data science is heating up among business managers as analytics becomes essential for competitive advantage.

Undoubtedly, the biggest argument in favour of outsourcing is the speed to adoption of analytics. It brings scale and specialization in a cost effective manner within short timelines.

Another big benefit overall is the forced standardization that outsourcing would bring to the whole industry. This is good for the whole industry, as processes, techniques and skillsets would be shared and adopted across domains.

This standardization would lead to increased automation of key processes and techniques as is evident in many industries. Most outsourcing outfits tend to create unique IP’s and products that 1) are results of specialized knowledge that they have gained over time and 2) are mostly plug and play, catering to a wide range of companies and business problems.

What’s needed now is the right set of governance and engagement models that are crafted specifically for the analytics industry. This is bound to happen as analytics outsourcing evolve and matures.