Is Data Science Hub the next generation COE solution in addressing emergent business challenges?

Relevance of Data Science

Numerous evolving strategies and technologies that address business challenges in this era are mainly related to big data, data sciences and data economy. We hear about new technologies, platforms and products every other day so I will skip listing them. We see these pervasive elements catering to the demands of businesses across the globe and it is imminent that data sciences based applications will be the norm of the day in the near future if not already. Consider an example which all of us can relate to; humongous amount of data being generated with the most common IOT devices/wearables that we use like fitbits, mobile devices, cars, smart home appliances etc., which are being used to provide solutions for individuals and businesses simultaneously. We are already witnessing the rise of these technologies touching our lives on a continuous basis where it is evident that data science is central to dealing with such data explosions in creating meaningful interpretations that can be put to use by businesses. We are very familiar with those prominent players who have been doing a phenomenal job in this space.

We see the success of big corporates like Google, Facebook, Amazon, LinkedIn etc., and also various startups that specialize in niche solutions like Airbnb, Netflix, Zynga, Uber etc., by leveraging the big data and democratizing it across the organization and using data sciences to drive business outcomes. Data sciences driven decision making is becoming the standard for organizations that are analytically competent. This means having complete data sciences capabilities across all functions and embedding the intelligence into each process within the functions in the enterprise.

Who is a data scientist?

While there are numerous definitions floating around what makes a data scientist, my view is a simplistic one and based on common understanding from collective experiences of my friends, colleagues and mine. Experience in Big Data Technologies with a blend of quant expertise and a good domain understanding should make the cut. Of course these skills can be in varying degrees which can be further understood from Gartner’s report on “staffing a data sciences team” that describes different roles with clarity. Hold on, that’s only the science part it, and for the art part we will need the story-telling skills sandwiched with curiosity & creativity.

Data Science Hub (DSH)

It is evident that the demand in the form of business challenges, data explosions and newer technologies exist and the supply with huge resource potential exists as well. It is time to think how do we bridge them to build solutions that matter to delivering value. I have always believed Centers of Excellence (COE) have had the capabilities to service the needs of a business in an efficient way and is a proven model to bringing specialized skills to a common pool to deliver services and solutions. This will work for data science as well. The key to understanding what clients look for in a data sciences environment and how a third party can partner with the client will provide a holistic solution involving Data Science as a service. I would call the optimal solution to be the Data Sciences Hub (DSH) which is the next generation advanced analytics COE solution towering over existing solutions in this space.

Traditionally analytics COEs are equipped with the standard components that focus only on delivery but, with the data sciences infusion the dynamics are changing. The way a DSH operates is fundamentally different from the one dimensional approach of an analytics COE which is delivery excellence. Apart from delivery excellence a DSH operates in additional dimensions of continuous innovation, adopting newer technologies and building new competencies.

 A DSH effectively needs to have these key differentiating components to succeed: 

• Rapid prototyping technology environment
• Specialized functional squads
• Business value measurement process
• Strong talent enhancement system

DSH facilitates value generation over existing delivery capability. As a discipline, data sciences focuses on solving a problem, standardizing/assetizing the ongoing delivery and moving on to the next. Clients will look forward to creating and engaging with DSH because it will generate better value in terms of data monetization, creating newer solutions, newer assets/accelerators, intellectual property etc., and will contribute to achieving business targets.

Delivering through DSH

Businesses building DSHs need to partner with different service providers to create and sustain data sciences based solutions. Let us be realistic, it is not going to be any one service provider’s game anymore. The key is going to be how quick and efficient a service provider will enable a skill, asset or service towards solving the problem at hand in a rapidly changing environment. It will require a lot of ground work internally in terms of infrastructure set up, investments in both human and technical resources, newer business processes, better employee engagement, ability and experience in working in a multi-partner environment to name a few, to facilitate a full fledged support environment for a DSH. 

The future is calling us to be collaborative than being competitive. It will be interesting to see how this discipline will evolve in business environments. Kindly share your thoughts, I am keen to understand different perspectives on this topic.

*Please note that the views expressed here are strictly personal and does not endorse/reflect any from my employer