Interview

UPTRENDING BIG DATA ANALYTICS

Rupesh Khare, Global Head – Advanced Analytics and Artificial Intelligence, ABB

A big strategic decision, taking to the analytics route calls for change management within existing decision-making process and flexibility in adapting new ways of Data Driven Decisions

The pandemic-driven digital revolution across industries such as healthcare, manufacturing, wholesale and retail, etc. has resulted in enhanced big data implementation. Industrial internet of things (IIoT), and artificial intelligence (AI) too have propelled computerization in the production industry. Big data technology is increasingly being employed across portfolio management, asset administration and analytical maintenance, network manufacturing, real-time alerts, and many more to magnify business growth.

[quote font=”tahoma” font_size=”13″ font_style=”italic” color=”#262626″ bgcolor=”#f9f9f9″]

“Data-as-a-service (DaaS) will become a more widespread solution for data integration, quality management, storage, and its usage in analytics.”

Rupesh Khare,
Global Head – Advanced Analytics and Artificial Intelligence,
ABB

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Riding on the uptick in market demand, the global big data analytics market size is surging ahead and is expected to breach a USD 549.73 billion mark by 2028. From an overall market size of USD 206.95 billion in 2020, it has reached USD 231.43 billion in 2021. Continuing its growth streak, the market is estimated to grow at a CAGR of 13.2% during the 2021-2028 period, according to “Big Data Analytics Market Forecast, 2021-2028” report from Fortune Business Insights.

The Big Data Analytics market in India is currently valued at $2 Billion. This is expected to grow at a CAGR of 26 percent reaching approximately $16 Billion by 2025, making India’s share approximately 32 percent in the overall global market. The nearly eight fold leap of the Indian Big Data market is a lucrative opportunity waiting to be tapped.

As the India market remains in the eye of a major digital transformation buoyed by a tech-centred Union Budget, SME Channels embarks upon recording the voices of the who’s who of the Big Data Analytics landscape.

In a special interaction with SME Channels, Rupesh Khare,Global Head – Advanced Analytics and Artificial Intelligence, ABB, reveals his organization’s approach to leverage this highly promising technology, opportunities created by the Analytics market for channel growth, his forecasts for the Big Data Analytics industry, the major growth drivers and his vision and plans for the channel community. Edited excerpts…

What key mega trends do you foresee in the Data Analytics market? 

Data Analytics has been a dynamic sector for more than two decades and is still witnessing continuous changes. Analysts anticipated that the speed of the change would slow down. However, on the contrary, these changes are accelerating in a pattern.  Earlier, the industry addressed challenges pertaining to Data such as types, size, format, etc. followed by the intervention of Technology such as Big Data, Cloud, Internet of Things (IoT), Blockchain etc. While technology continues to be a powerful enabler, the next focus is expected to be on the Users of Analytics and Artificial Intelligence (AI) solutions.

The Users are setting the norms and the direction for analytics. This is a propelling force for a clear shift towards analytics-based products. The trend is to transfer more degree of freedom in the hands of the users of analytics-based products. These products are aimed to provide self-servicing analytics, more user autonomy and real time analytics through maximum automation. Given that the static dashboards and visualization tools limit the depth of insights, the next generation products will aim for interactive interfaces and more independence leading to an improved user experience.

Data-as-a-service (DaaS) will become a more widespread solution for data integration, quality management, storage, and its usage in analytics. This makes an obvious choice when organizations are evaluating cloud-based infrastructure to drive efficiency.

What are the major drivers of growth in the Big Data Analytics market? 

Uptrend in Big Data Analytics is manifested by the social, technological, and operational drivers. Digitization and the use of social media are impacting all the Vs – Volume, Variety, Velocity, Veracity and Value of data. The business sees an opportunity in using this new oil to their advantages. The meaningful insights from the social media data can be useful in many business decisions including knowing the customers’ profiles, purchasing preferences, new customer acquisition.

Today, technology has been evolving to complement collecting and processing the massive quantities of variety of data. Technological advancement has been reducing the storage and processing costs significantly. Open-source Big Data platforms have further made it affordable for the organizations to start the Big Data Projects. Additionally, the advent of Internet of Things (IoT) which enables physical objects to connect and exchange data also propels the growth in Big Data Analytics market. These technological drivers are getting good operational support as well. Advanced Data Science and Cloud Computing knowledge are supplementing Big Data Analytics on skills required to execute the project.

How do you ensure partner profitability in this emerging technology vertical? 

Analytics assists partners in their effective and efficient data driven decision making. This process of analytics critically depends on the accuracy of identifying factors contributing to the profitability. However, quite often it is observed that an analytics solution addressing these factors fails to increase the profitability. And hence, it becomes very imperative to identify the main reasons behind this failure, and list of these reasons could be long.

A successful analytics solution must be simple, interpretable and easy to implement. A simple solution helps partners to understand it better while they lose interest in complicated ones. Another critical success factor in increasing the profitability is an ability to convert the analysis to insight using domain knowledge. An easy interpretation of the solution carries higher chances of the acceptance. Finally, the most practical aspect is the implementation to improve the profitability. An easy to implement solution will have more chances in ensuring profitability.

How is the response from the partner community towards this niche technology? 

Typically, the response from the partners depends on the industry they operate in, readiness to accept a niche technology, their future vision and the data culture of the organization. Different industries have different response time. Normally, industries such as Banking, Financial Services, Insurance, E-commerce, Retail are quick in their response while other capital-intensive industry are relative slow in responding. Taking the analytics route is a big strategic decision and it requires change management in existing decision-making process and flexibility in adapting new ways of Data Driven Decisions. This change must be in line with the overall future roadmap of the organization and analytics should add synergy in attaining the business objectives. Quite often organizations take ‘impulsive’ decision to embrace analytics without doing enough analysis on their readiness in introducing a novel approach, impact on business goals, required infrastructure and skill sets. Such initiatives with little homework may lead to a misadventure. A practical strategy to adopt analytics is to conduct a few Proof of Concepts and analyze their outcomes before implementing them widely in the organization.

Organizational data culture accelerates the outcome of analytics, amplify its power, and steer the business away from failure probabilities. Building a Data Culture in an organization is a key step in driving the data and analytics strategy effectively. However, this invites high commitment from the top leadership of the company. This commitment must be manifested by more than occasional high-level pronouncements; there must be an ongoing, informed conversation with top decision makers and those who lead data initiatives throughout the organization.

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