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Global Delivery Model worked great for IT outsourcing. But is it valid for Analytics?

Global Delivery Model worked great for IT outsourcing. But is it valid for Analytics?

Over the last two decades, Indian IT bellwethers Infosys, Wipro, TCS and HCL have perfected the Global Delivery Model (GDM) that geared to meet the business challenges and addressed key skill gap. In fact, Indian IT consulting giant Infosys famously pioneered and the perfected GDM that changed the way traditional business model worked in the industry. Former Infy CEO Nandan Nilekani famously said, “By mainstreaming GDM we have shifted the battle to our battlefield. It has become a global outsourcing standard and has helped us create and perfect the science of global project management.”

Seems like the salad days are behind for Indian IT majors who are facing an uphill battle with a protectionist regime and regulatory visa norms. However, Indian data and analytics providers who  took cues from GDM, refined these practices to suit the life cycle stages of solving analytics problem and have established best practices.

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Demand for GMD in Data and Analytics

According to Naren Peri, Director & Practice Head – Consulting & Analytics, Brillio, “The demand for analytics is ever increasing; it’s expected to grow at the least by 30 per cent CAGR for next five years. Though data science and analytics space appears crowded, I believe there is enough space for startups with relevant offerings mapped to any particular industry or a function to make room here”.

Peri believes by embracing GDM, start-ups can provide competitive offerings to firms worldwide and build critical mass. Data analytics solutions provider Incedo operates a shared services delivery model knowing that analytics cuts across all business functions. “Our focus is asserting centralization of data science offerings under a single power house. This enables cohesiveness in skills, use cases and speeds up delivery,” said Tejinderpal Singh Miglani, CEO, Incedo Inc.

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Given the need for a shared model, Incedo set up an ‘incubation lab’ last year wherein a dedicated set of experienced data scientists experiment with the latest modelling techniques, test them on a variety of business data sets and create plug and play frameworks for clients. Stressing on the importance of GDM, Miglani shared, “Although, experience shows that a small dedicated on-shore team with adequate data science background helps understand client’s business as well as maintain the follow-the-sun strategy to keep up with pace. We tend to execute this and this has helped Incedo maintain stability and boost revenue.”

Advantages of Global Delivery Model for Data and analytics providers

  • Miglani believes the hybrid nature [onsite/offsite+ offshore] of the model provides a seamless workflow and boosts efficiency
  • Clients are more confident to kick start POCs without a long approval cycle when it comes to a shared services delivery model
  • Peri emphasizes a GDM for data science offerings enables scaling solutions across products/geographies/customer segments and drives high ROI impact
  • Most importantly, GDM allows global companies to have access to large pool of talent that in certain economies, like in US and Europe, are in shortage, Peri explains

Is Cost and skill gap fuelling GDM?

Peri reiterates that GDM is not only useful, but essential for solving problems, design and develop prototypes, enable extreme experimentation and scale chosen prototype by building industrial grade solution. “Given the shortage of talent in data sciences profession, Global Delivery Model is essential to support the business demands. Data driven decision making typically requires one to take an exploratory and experimentation approach. One has to fail fast, learn, iterate and implement those learnings in successive iterations to arrive at an acceptable/implementable solution,” he said.

To carry out these experiments, the cost of experimentation has to be low. Global Delivery Model allows for conducting many experiments at low cost. “That’s why outsourcing data science offerings through a Global Delivery Model is essential for firms to scale and institutionalize data driven decision making,” Peri explained.

Besides the cost factor, the model also leaves room for ‘business’ innovation and ‘analytical’ innovation, believe Miglani.

Hybrid Global Delivery Model vs Shared Service Delivery Model

Indian analytics companies also realize the importance of a hybrid global delivery model comprising of in-house experts, backed by a dedicated team at client side to make sense of business at a deeper level. What the client side team does is essentially articulate the vision, define business needs and set up the roadmap.

Of late, recent surveys suggest that firms are more inclined to build their own internal analytics team rather than outsourcing it to vendors, Miglani revealed. Another emerging trend is outsourcing to niche analytics vendors who understand the data as well as have core analytics frameworks to tackle specific problem.

The other key factor is clients suggesting execution within their premises to facilitate analytics. “That’s why the shared services model makes logical sense while scaling up operations and keeping costs down,” said Miglani. However, the downside to shared services delivery is it can fail when one does not optimize business workflows and the complexity gets out of hand.

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