Over the last decade there has been a tremendous increase in understanding and cognizance of utilising data science capabilities to optimise business outcomes, achieve competitive advantage and increase profitability. However, developing in-house employees to be stellar data-scientists through skills training is often seen as time consuming, inefficient resource allocation and a burden on the costs by organisations. Hence many companies are leaning towards outsourcing these competencies to analytics vendors.

Though traditional models of data-science outsourcing were popular, their inability to maximise value and mitigate risks for the buyer has led to businesses desiring for mature or alternate models. With enterprises being subjected to greater global economic uncertainty, fierce competition, constant disruption in technology, the desire to have more dynamic liaisons with a guarantee of predictable return on investment (ROI) is now more than ever. Outcome based delivery model is a result of these expectations, which enables buyers and providers to collaborate, achieve optimum and measurable returns.

Certain advantages of outcome-based delivery models in comparison with traditional models are:

Minimising the delivery risk:

In outcome-based models, since the analytics service provider takes responsibility for delivering measurable outcomes or pre-defined outputs, they are more aligned to take ownership of the end results.

For e.g. If a vendor is approached to optimise store check-out times, in traditional model vendors would be inclined to analyse the workloads, timelines, etc. However, in outcome-based models the vendor would use analytics to trouble-shoot around processes and technology to reduce the check-out time.

Better value for money:

In many cases outcome-based models work on the principle of revenues generated or cost-savings achieved. This helps vendors optimising resources to get best returns.

A good example of this arrangement could be the Air-Audit solution designed by Happiest Minds. It is an outcome-based pricing model where the airlines pay a percentage of revenue leakage identified by the solution. This solution is designed to prevent unnecessary Global Distribution System (GDS) related costs and inventory spoilage caused by non-compliant booking practices.

Customisation based on individual needs:

Unlike traditional models that follow regular practices (like staff augmentation, product-based services) to achieve linear growth, outcome-based models help optimise results.

A note-worthy example in this regard is how big-data has enabled farmers achieve better results based on their individual needs, thus converting the agri-business into a specialised serviced based industry, rather than a product based one. A collaborative effort in this regard from all allied industries will help the overall crop-output. For example: the fertiliser industry can provide fertilisers based on historical analyses of the soil sample, thereby optimising the cost, and accentuating soil quality.

Lean approach to maximise profits:

As all the stakeholders share a common goal of achieving the pre-decided outcome, there is further focus on the exponential growth to be achieved. The outcome-based delivery model typically works with vendors deploying their most skilled resources to the project, who encourage innovation in processes, thereby creating an agile work environment.

Transparency:

As the outcome-based delivery model works as a win- win situation for the vendor and the client, there is an absolute transparency, which further helps build long term relations between both the parties. This in a way ensures client’s data is protected from being shared beyond the scope of work or contract between the stakeholders.

In summary, outcome-based delivery model is a “Value driven paradigm” that can enable businesses to capitalize on the unstoppable digital transformation the future holds. Unearthing the processes involved in an outcome-based approach will help businesses uncover unexpected sources of growth, and once successfully implemented these can be scaled to greater business outcomes even though requirements may keep changing.

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