Ranjan has been recruiting top talent for a decade now, and while he attends various conferences on Talent Acquisition in India or abroad, he has seen major changes of how HR is getting into the digital space. Every expert that he talks to or listens to at various events, talks about getting good data-teams on-board. His organization is also looking to expand the data team. Now, Ranjan seems to be in a fix. He realises that he needs to understand who data scientists are and what are they suppose to do in the organization, and also what is the procedure to get them on board, because if he understands the crux of the data logic, only then he can get the right talent for his company.

So we asked a few organizations about how they build their big data dream teams. And here’s what they had to share.  

The Basic Requirement

The basic requirements to build a dream team would start from highly skilled engineers in big data technologies. To build a dream Big Data team, an organization needs to start by clearly identifying its goals and key indicators of achievement. Anil Warrier, Director & Head of Talent Acquisition at SAP India said that primarily organizations should look for three things while hiring data geeks – 

• Data Scientists: Who can mine and analyse large sets of structured and unstructured data on a day to day basis. 
  
  
• Business analysts: Analysts who have in-depth knowledge of the industry and market. They use the data to build a business strategy and decide the organization’s course of action. 
  
  
• Data Visualizer: Graphic and UX designers to help present data in visually appealing infographics which in turn help management understand the data and derive perspectives.

Agrees Rajiv Burman, Senior Director and Head Human Resources APAC at Kronos. He points out that companies should look for engineers/scientists with rich analytical skills, passion to explore the data and dig out valuable use cases out of the data as the first priority in addition to the skillset of big data technologies and programming.

Required Skillsets

Although different organizations have multipronged functional approach, it is believed that knowledge of algorithms, and various programming skills should be of prime focus. “The core skill set required to become a data scientist includes mastery over data analysis techniques, deep knowledge of algorithms and proficiency with tools and programming skills like R, python, big data technologies like Hadoop and spark and basic visualization and reporting tools like Tableau / Microstrategy,” said Rajiv. 

While Manish Mittal, Managing Principal and Head of Global Delivery at Axtria thinks that data scientists are expected to be able to leverage their domain knowledge and technical abilities to be able to tease out meaningful and actionable insights from data. And also, a data scientist should be equipped to deal with the huge volume, high velocity, and changing variety of data from multiple sources.

How Will You Go About Finding The Right Talent

As demand for big data professionals grows at a staggering pace (studies peg the demand is 32% higher over demand for IT professionals) and experts predict a shortfall of 200,000 analysts by 2018, there is a yawning gap of employability among prospective candidates. Organizations keep no stones unturned to get the right talent. “Adopting a non-conventional and cross-channel approach is the best way to look for the rare talent.  The approach involves an integration of traditional recruitment practices, such as campus talent identification and job listing portals/forums, with the new-age techniques involving social media, professional networks, peer-to-peer recommendations, and seminar/conference interactions. This helps us in advance scouting of data professionals, and allows us to bring the best talent available in the sector on board our team. We also constantly monitor our own employees to identify in-house talent which can be groomed and trained as data professionals,” shares Ruchika Sawhney, VP- HR & Admin at Udio.

Challenges

The challenges in building the dream team in this sector especially in India are basically to hire the right candidate, groom them for success and keep them motivated for retention. At the time of hiring, the challenge is to showcase the opportunity as an exciting avenue for professional growth and convincing the candidate of the organization’s big data vision and roadmap and hence the long term relevance for the candidate. 

Manish believes that “in this competitive industry there are large employees who rank high on technology expertise, but struggle with domain and analytical expertise. On the other hand, there are niche consulting players who rank high on business domain and analytical expertise, but low on technology. Then there are core analytics players who rank high on analytical expertise, but low on domain and technology knowledge. One of the key challenges is being able to get the right talent with a combination of these core skills. Secondly, structuring the organization in a way that projects can be staffed with multi-disciplinary skills can also be challenging…The dream team set up requires a well-crafted matrix organization.”

How To Retain And Engage The Talent

Axtria, which is a Data analytics company have invested in creating an ecosystem that fosters learning and development among their employees – The Axtria Institute, an internal learning and development body helps associates build long lasting careers in the analytics industry. The company provides both a full training platform combined with an advanced and effective certification program to become a data scientist and business/ technical analyst. SAP India also believes in investing in the learning and growth of these employees, so says Warrier. “Given how data science and big data are constantly evolving, highly dynamic fields, the best way to go about engaging and retaining data professionals is to give them the flexibility and operational freedom that the role requires,” shared Ruchika. 

People like Ranjan will now have a structure by which they can look for the right data scientists to come on-board. But being agile and looking out for the best should always be top priority.