According to IDC, the Big Data analytics and related technology market will be worth USD 203 billion by 2020. For data science leaders, gaining competitive advantage is no longer about relying on traditional decision-making models but about integrating data science with engineering capabilities, leveraging AI programs to analyse patterns in large data sets, and taking advantage of edge computing to monetise insights. In fact, ‘data monetisation’ is already emerging as a major source of revenue growth for a third of Fortune 500 companies who say their information-based products contribute double the revenue compared to rest of the product/service portfolio.

How do Indian companies match up in this space? Sadly, not too well. According to a PWC report, only 37% of Indian organisations are data-driven – an indicator that more than a third of Indian companies are risking competitive advantage.

As data creation and consumption continues to soar, focusing attention on these three aspects can help data science leaders better position their organisations for growth:

#1 Strengthen the data science function: Maximising investments in data science means enhancing the utilisation of data science models. This can be done by leveraging data science platforms that enable scientists to place API code wherever required to significantly accelerate model implementation time. In addition, bridging the gap between data science and software engineering by integrating the two workflows, is critical. Software platforms such as GitHub or BitBucket help achieve this goal by enabling easy tracking of changes, updation of codes for faster product deployment, and overall maximisation of ROI from data science investments.

#2 Deploy sophisticated AI programs to leverage machine intelligence: According to Gartner, 59% of organisations are currently building their enterprise AI strategies while the remaining 41% have already made the plunge. A robust AI strategy helps businesses analyse patterns of large data sets for more informed and accurate decision making. In addition, using biological neural networks such as Numenta’s technology that leverages Hierarchical Temporal Memory (HTM) – an intelligence framework based on neocortex activities; can help organisations understand the structure of streaming data. This can be particularly helpful in areas such as monitoring IT infrastructure or stock performance, detecting patterns in geospatial data, making accurate predictions, and detecting anomalies/unusual human behaviour.

#3 Engage edge computing: An estimated 5.6 billion IoT devices are expected to be harnessed by governments and enterprises by 2020. With IoT becoming increasingly mainstream, remote hosted datacentres are losing their sheen as businesses prefer edge computing. Conducting analysis at the edge - closer to the point of data origin, helps reduce latencies, ensures high availability, and significantly compresses the cost of data management and operations. For instance, Harley-Davidson embedded location awareness devices and sensors in its Pennsylvania facility to reduce the time taken to build a customised motorbike from 21 days to 6 hours.

AI and ML paired with skilled data scientists is the future
In an increasingly data-driven environment, it is ironical that most data scientists find it challenging to convince stakeholders to utilise their models and innovations. Recent advances in AI and ML promise to change that equation by giving data scientists the tools they need to demonstrate tangible value from their work.

For data science leaders, interesting times lie ahead, particularly as revolutionary applications like the Apple ARKit enable scientists to blend digital objects and data with real world environments, to power entirely new use cases on the go. The future will belong to data scientists who can intelligently combine relevant domain knowledge with advanced data analytical skills to help organizations leapfrog the competition and gain market share.