In 2019, the Global Data Analytics Outsourcing Market was valued at $3.04 billion, and it is expected to reach $9.46 billion by 2025 at CAGR of 21.5% (2020-2025). The increasing volume and the variety of data that is being generated is the primary driving force for outsourcing in the analytics industry, and India is probably the best at it. According to a Gartner report in India, analytics outsourcing takes the lead, generating more than $27 billion in revenue for vendors.
Organisations often struggle to manage and analyse the massive amount of data systematically. Consequently, they seek help from vendors to speed up the process with industry-specific best practices and also get technical expertise for handling dependencies.
There are definitely many advantages of performing analytics in-house as it allows greater control and lesser risk related to data breaches. Besides, engaging in-house data scientists in the analytics project leads to a better understanding of how the business operates, thereby enabling firms to harness the potential of data by unearthing in-depth insights. However, due to the absence of required talent in the market, firms fail to deliver on their intended promise. According to reports, 85% of AI projects fail due to a lack of understanding within organisations about the best practices for analytics projects. Therefore, companies of all sizes decide to outsource analytics projects to obtain the best out of the data they collect.
Vendors offer teams of experienced data scientists who are experts in advanced data science tools and techniques. Thus, organisations prefer teaming up with vendors’ data science experts as it is more accessible and less time consuming than building one’s own team from scratch.
Why is India the hub for outsourcing?
For various companies, India has been their preferred destination for outsourcing IT requirements. Over the years, India has been nurturing a pool of talent in quantitative streams like economics and mathematics along with others for its analytics base, and it has been expanding rapidly. Consequently, analytics in India also has a certain adjacency to IT services that provides useful context and the technical background.
Huge number of International corporations especially Fortune 500 companies prefer India as their prime destination for outsourcing for its enormous talent when it comes to analytics, its workforce and most importantly, for the added benefit of low cost.
With the number, $27 billion in outsourcing analytics revenue this past year, it indicates that upcoming years might see more outsourcing, especially with advanced analytics and cognitive automation. Companies like TCS, Genpact, Wipro have been investing in data and analytics, and they are also working towards expanding their analytics services offered, with their large workforce and expansion, India will remain at the forefront when it comes to outsourcing, especially analytics.
Some Popular Engagement Models
An engagement model is a framework, which defines collaboration between a client and analytics outsourcing partner in terms of the level of control, responsibility and a base for further development.
If implemented correctly, the strategic partnership is a beneficial and happy collaboration between the client and the outsourcing partner. By bringing together, the expertise from outside an organisation can innovate and go beyond what their current analytics team can offer.
Below are mentioned some of the models; however, one should keep in mind that there is no ‘best model’ anywhere, an organisation has to study, research and first decide on a vendor. The next is determining the model that matches the company the best.
This is a simple model that allows the company to extend the existing in-house staff with workers from an outsourcing partner. This model works fine on a short-term basis and demands high client involvement to oversee the augmented staff. Although a simple model, staff augmentation model’s level of innovation will be low for many cases. A typical client goes for the model for cost reduction.
Project-based model is a time-bound engagement model. It reduces cost and time by taking the required technical and domain expertise from the outsourcing vendor. The project-based model is effective where there are only little changes during the development process, and however, if the requirements frequently occur, then there are other models for it. Such as offshore/nearshore development.
Offshore/Nearshore Development Center:
This is a flexible engagement model where the client drives the requirements, and the analytics outsourcing vendor manages the offshore/nearshore staff. The model can undertake a wide variety of projects and activities.
This is a generic term for value-added services where the client’s driver is to access the expertise, which it lacks in-house. These data science expertise that the client lacks are provided by the outsourcing vendor on a fixed price time-bound activity.
Build Operate and Transfer:
In this model, the vendor builds for the client, operates with the client and transfers to the client. What this means is that one can variably recruit data scientists’ professionals, train and operate towards their business before deciding to absorb them as a resource on their own payroll.
Analytical Insight and Excellence
Creating analytics centre of excellence (CoE) staffed by the client’s team and the vendor’s team. This model allows better collaboration between business and IT, increased adoption and use of BI and analytics, better data management, quality and reporting as well as cost savings.
Global Delivery Model:
Global Delivery Model has worked well with IT services so far, in fact, Indian IT consulting giant Infosys pioneered and perfected the GDM. GDMs are a form of client-specific investment promoting services integration with clients by combining client proximity with time-zone for 24/7 service operations.
Challenges with Analytics Outsourcing
The analytics outsourcing market is growing and as good as this option is for the companies, it comes with its own set of challenges despite having good models.
• There is always the risk of exposing sensitive company data and confidentiality. The organisations must ensure security in any contract/agreement that is undertaken.
• There should be constant communication between the business owner or the companies and vendors.
• The companies should get the right legal advice on their side to avoid any legal issues in the future. Care must be taken that the companies have the right contract in place.
• The focus of vendors must be on choosing the right partner. The focus should be on analytical and technical competence and applying these to complete projects effectively and on a broader level, solving real-world problems.
• Choosing the right outsourcing engagement model.
As big tech giants opt for more outsourcing, the real advantage of analytics outsourcing can be taken by the start-ups. The cost that it saves will have a significant impact on Indian start-ups as India sees more and more start-ups every day. Another key aspect will be that these start-ups will be getting high expertise and will save time on projects rather than spending a lot of time on training the in-house data scientists. Outsourcing also lets the management and executive team to concentrate more on the core operations of the business.
With India being the most preferred for outsourcing, the time has passed for small and mid-sized businesses to think of big data and analytics as something only the giants will be able to do.