Srikanth
Currently, there are two ways a business taps into technology to execute business challenges or requisites. Outsourcing is one, where a strategic step to gaining competitive advantage is achieved. Besides, business process outsourcing providers are considered as partners rather than just a contractual business that carries out major functions that aren’t necessarily considered as core business. And why not, a business owner needs to focus on his/her core competencies.
In the late 1980s, specialization, labor arbitrage, risk sharing, and scale efficiencies gave birth to the modern outsourcing. From the late 1990s forward, the era of globalization saw the rising of the second wave of outsourcing with the emergence of offshoring. Then, in the era of social networking, we witnessed the third wave of outsourcing with the emergence of crowdsourcing.
Crowdsourcing is a means of tapping into the global talent pool wherein certain business requirements, a company wants to fulfill, are broadcast to an unknown group of virtual service providers.
Yes Bank, the fourth largest private sector bank in the country sought to involve the data science community outside of their organization and see if their use cases and ingenuity could solve some of the greatest but unforeseen setbacks in the financial system. With this objective, Yes Bank brought in the 4th wave of outsourcing and coined the term ‘OutCrowding’.
The management at Yes bank aimed to engage the internal and external teams in a symbiotic relation, where both sides can equally benefit. The business problems were troubleshot with the combined efforts of the internal data scientists provisioned by Yes Bank and the genius participants that hailed from a plethora of domains.
Carefully picking up the pros of both the models (outsourcing & crowdsourcing), Yes Bank was able to bring in this unique model called ‘OutCrowding’, that saves a lot of cost and time of the parent company. The time and money spent on researching the vendors, shortlisting the same, then specifying and designing a contract etc, is well mitigated when the competitive factor comes in.
The talent pool is benefited by the resources and assistance provided by the host, whereas the host is benefited by the diversity of exceptional and fascinating solutions that the talent puts forth to the banking problems. A win-win situation per se.
Good quality solutions could become a quandary for such an interesting program like Datathon. This could have been the case due to an insufficient number of participants. However, Datathon I received over 6000 applications and was conducted with 1700 teams world over.
Datathon, by opening an avenue to the outside world, was able to facilitate itself with the best teams/talents for the best in class solutions and ideas. Besides, the exposure to a planet size volume of data to the participants allowed the teams to push their projects to near perfection. This out-crowding model fetched Yes Bank, cross-industry collaboration opportunity too.
The 80-20 Data Science Dilemma
It has been found that data scientists spend 60% of their time on cleaning and organizing data. Collecting data sets comes second at 19% of their time, meaning data scientists spend around 80% of their time on preparing and managing data for analysis.
Few more figures: 76% of data scientists view data preparation as the least enjoyable part of their work out of which, 57% of data scientists regard cleaning and organizing data as the least enjoyable part of their work and 19% say this about collecting data sets.
Therefore, Yes Bank as part of their Datathon initiative opened the gates to this large datasets for the student community. The Datathon Campus Editon allowed students to leverage this data and build their use case solutions after sorting the data. Evidently, students’ talent with problem-solving remains untested with insufficient real-time data, which, in this case, was resolved by Yes bank.
The objective was to give the student community their well-deserved opportunity and exposure and at the same time have the banking dilemmas solved by having such fresh minds on board.
“We are currently targeting futuristic models. We intend to throw the projects on some emerging and promising startups or even data science clubs that most engineering colleges have in the curriculum”, the bank mentioned . With data scientists seeking to try everything to upgrade their skills, lack of real-time data and technology can bring them down.
This is where Datathon steps up to provide the ingredients that data scientists require for their skills. This is what Yes Banks intends to do: contribute to the data science society at large by availing all the necessary tools. More importantly, the technology platform and the huge data set.
Yes Bank has successfully built an ecosystem, rather the ‘Datathon community’, comprising of 10000 plus members. The community includes data scientists, professionals, Computer Science engineers, and students etc. A true to its nature India’s first bank led Datathon, Yes Bank has indeed bridged the gap between the academy and industry.
