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How to Build a Team to Deliver on Big Data’s Promises

By February 23, 2016Article

Big data, which has caught the fancy of people worldwide, across disciplines, seems to be maturing from the ”next big thing” to providing business value for enterprises. As a key trend shaping the market, it continues to hold sway over all stakeholders in this ecosystem, whether it is the millions looking to make a career out of it, thousands of enterprises wanting to leverage data for business gains or the rapidly mushrooming set of new and established players who intend to provide solutions in this space. However, one question that baffles the big data world is “How does one build and scale data science teams to deliver consistent business value from data?” 

Relying on the adage “experience is the best teacher,” I draw upon the experiences of building a delivery organization to provide customer value from the big data promises. Need for multi-disciplinary skills, dearth of talent, intense competition to hire talent, limited hiring dollars and a fledgling brand all made the task tougher. Hence, this article discusses the realistic action in the trenches rather than a few concepts. 

Setting up a data science team 

For enterprises aspiring to put big data to use, the broad approach to a sound solution is a three-step process:

  1. Consultative solutioning to identify the business problems, define and scope out the right perspectives
  2. Pertinent analytics to derive insights and hidden patterns from data
  3. Data visualization to bridge the last-mile disconnect by converting numbers into visuals, to present the information and insights from data. 

Let’s now look at the key challenges that an organization would face in building and scaling such a big data solution and how to address these challenges. 

1. What mix of skills can deliver value? 

Delivering a robust data science solution calls for a multi-disciplinary skillset across four broad areas:

  1. Domain skills to identify the right business challenges and come up with solutions
  2. Quantitative skills needed to apply math and statistics for extracting insights from data
  3. Design skills to present information in a creative, aesthetic and usable manner
  4. Technology skills to leverage deep programming and data technologies for scripting this end-to-end analytics and visualization solution. 

2. How to hire the right skills 

With companies struggling to hire talent with good skills in most of the above areas, it is next to impossible to get a combination of all skillsets in one person. One solution is to carve out new roles in data science along functional and technical lines by bundling a set of related skills that is closest to the solution offered. 

The intent is to hire people with complementary skills, who would come together as a multi-functional team to deliver an engagement. For a bootstrapped startup, it’s a sound strategy to start with talent in known circles and through direct referrals, wherein the initial hires can be trained on the job and supported on existing skill gaps by the senior members or founding team pitching in. 

3. How to attract the right people 

The war for talent is a perpetual problem for most companies, and new-age startups take a different approach to address this issue of hiring great talent. These companies consciously invest a good amount of time speaking and participating at relevant big data conferences, public forums and partnering with educational institutions offering data science courses. While this aids with branding and creating a buzz in the industry, it also helps get closer to qualified talent and attract them from relevant circles. 

As a side benefit, this could also have an indirect fallout on sales lead generation by helping add qualified client leads to the sales pipeline. 

Additionally, targeting the key movers in online technology forums like Github, Stackoverflow and the like can be very beneficial for companies in identifying lead and senior profiles. The focus at this stage should be to make the hiring process efficient by pre-qualifying candidates and helping preclude the inordinately long cycle times and low success rates associated with the traditional hiring process. 

4. How to train the team across multi-disciplinary skills to deliver sound solutions 

With a growing team, it’s imperative to address the problem of repeatable delivery early on by putting together a sound delivery framework with clearly defined processes, roles and responsibilities, and deliverable templates and artifacts. One must keep a continuous focus on training and upskilling the team across roles by creating or sourcing content to run internal training programs. This can be supplemented by online courses and guest lectures by experts from the industry. 

All through this stage of growth, one must retain a laser sharp focus on clients and ensure that there is consistent and considerable value-add to the business stakeholders by leveraging data to smartly solve the business challenges. 

Adjusting sails for the next wave of growth 

5. How to correct chinks in the armor that impede scale and decentralization 

As organizations scale, it is imperative to reexamine the systems and processes to identify the need to adapt to changed market needs and internal dynamics. Often when companies breach the golden team size mark of 100 employees, they run into typical scaling issues around employees, processes and the quality of client-facing solutions. 

This can be tackled through a critical review and reorganization of existing processes in order to overhaul all those practices that don’t fit well with the theme of rapid upscaling. At this stage, organizations need to be open about decentralization and empower the second line of leaders who can carry the organization forward. 

Also, the convenient and comfortable practices would have to be given up in favor of more objective and standardized processes that can be rolled out across a larger team. 

6. How to improve the skills-mix by growing breadth while also achieving depth in focus areas 

Organizations at this stage need to focus on deepening the skills and knowledge areas to improve the quality of their solutions. A relook and expansion of roles by unbundling responsibility areas to allow for deepening of skills and knowledge areas can prove beneficial. At the same time, one needs to look at broadening the portfolio with complementary offerings to provide a well-rounded solution. 

Towards this effect, Centers of Excellence (CoE) or “horizontals” can be carved out within the organization in the core areas of data science, information design and technology to help achieve the needed depth in big data skills. These horizontals can take on the mantle of expanding and providing specialized training to the teams for all up-skilling needs in the organization while also taking a lead role in the complex, specialized implementations for clients. 

7. How to adopt practices in hiring for scale 

With requirements calling for greater numbers in hiring at this phase, the channels can be expanded by signing up with selected strategic hiring partners, apart from leveraging innovative techniques in data analytics through online channels for hiring qualified talent. It is also a sound practice to run hiring hackathons and data science contests on sites like Kaggle to get the right level of attention from prospective candidates while also opening up the possibility of hiring in bigger numbers. Employee-driven referrals can start yielding fruitful results at this stage. 

8. How to move up on process and solutions maturity 

At this next stage of evolution, it is critical to deepen the relationship with the client by taking on an advisory role and hand-hold the enterprise in chalking out a comprehensive data analytics road map. The maturity levels in solutions offered moves upstream, from delivering business value in chosen areas to looking at the enterprise end to end and advising on the set of strategic initiatives and moving clients up the data leadership hierarchy. 

In conjunction with this, the organization and delivery process must be re-bolstered by focusing on scalable processes and delivery excellence to support the growth needs. By weaving in the expanded roles and responsibilities of all individuals in the organization, a robust performance review process can be established to enable continuous career focus and growth for the team. 


Building and scaling a big data organization is a continuous and challenging process. One has to continue work methodically on the above-mentioned spectrum of areas, coupled with reviewing and reorganizing at the right intervals throughout the growth stages of the organization. 

In spite of a constantly shifting base along with the many moving parts within and outside the organization, it is critical to retain an open mind-set and nurture the core ethos that is the lifeblood of an open, startup organization: innovation, technology-focus, client-centricity combined with an open culture and fun at work. By retaining the focus on these critical growth factors and addressing the scaling challenges, this cycle of conceiving, scaling and maturing of an analytics delivery organization can indeed be made a reality. 

We continue to unlearn, relearn and scale to the next level in our journey towards becoming a mature big data product and solution provider. As more and more customers commence their nascent big data journeys or look at moving up the maturity value chain, organizations like us that offer solutions in this space have a critical responsibility to not just merely aid them, but transform constantly to deliver concrete and lasting return on investment throughout the journey. 

Ganes Kesari is the VP of products and consulting at Gramener, a data visualization and analytics company. He tweets from @kesaritweets and can be reached at












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