Influence of emerging technologies on analytics

Fast-paced changes in digitization, data and analytics have presented enterprises with a tremendous opportunity to bring about concrete changes to their businesses. Enterprises now can rejig business models, improve performance, enhance experience and reduce risk to meet market demands and ensure survival.

At the same time, the technologies themselves keep advancing offering more possibilities to companies. A few of the technologies that are at the forefront of data analytics initiatives are Artificial Intelligence (AI), Internet of Things (IoT), Block chain, 5G, Cloud, Edge Computing, Big Data, Machine Learning, and Automation. As AI, IOT and Cloud becomes more mainstream, we will see organizations approach analytics in a more industrialized manner unlike the past where it was done in pockets.

AI is currently being consumed to process large pools of unstructured data including text, speech and video/images. While these are primarily being embedded as part of process automation in enterprises, we see an increasing adoption of core AI techniques in both operational and strategic decision making.

This is being aided by availability of reliable data at scale, and we foresee large digital transformation exercises which will be the biggest consumer of AI technology and associated services.

AI is also getting commoditized and democratized resulting in adoption of software frameworks to leverage AI-at-scale in most business operations and decision making process.

Breakthroughs in AI particularly advances in machine and deep learning and exponential increase in computing capacity, have made it an important member of the analytics technology portfolio. IoT is an essential member owing to its ability to pull in large amounts of data and cloud technologies are making storing of core assets and analytical applications at the edge possible. Independently, these technologies are powerhouses in themselves and combined they can be the ammunition that companies need to place themselves outside the reach of competition.

Infosys’ recent study of over 1000 enterprises with over USD 1 billion in revenues across 12 industries in the United States, Europe, Australia, and New Zealand offers interesting insights into the technologies at play.

37% of the respondents across all regions surveyed selected AI as the digital technology with the most impact on data analytics outcomes. This was followed by 19% who voted for IOT, a view echoed by respondents from Europe, Australia, and New Zealand.

A significant 45% of manufacturing respondents elected for AI likely recognizing the impact that AI-driven insights can have on their supply chains. 23% of healthcare and life sciences respondents chose IoT, again a not surprising choice given the growing importance of remote monitoring, telemedicine, and connected healthcare.

A blend of AI and automation can deliver a massive uplift in productivity, something that enterprises will find invaluable and this was resonated by our survey findings too. On the role of automation, 57% said that it could help deploy and scale analytics initiatives, 53% said it could standardize data and analysis techniques, and 46% said it could deliver higher efficiencies.

Automation in tandem with analytics can be used to enhance the experience and efficiency of customer service calls and is well in use in the financial services industry. It can decipher the intent behind a call and use that insight to boost the call experience and hence efficiency. Another example in the financial services industry is straight-through processing that can raise transaction volumes while reducing errors and usage of chatbots in support scenarios. In the hi-tech sector, chatbots are much in vogue in product search and to answer frequently asked questions.

The use of AI in analytics had different implications for enterprise respondents. 52% felt that it would help create new business cases/models, a view shared by 60% of respondents from the financial services and healthcare sectors. 51% felt it would drive prescriptive and predictive modeling and the consumer, retail and logistics sector respondents strongly echoed this view. Finally, 31% thought it could help with risk detection and mitigation. In telecom for example, analytics is being used to reduce risk like service disruptions, call drops etc. Similarly, in retail – supply chain disruptions, ethical risks and strategic risks are addressed using analytics. Today, with the possibility of analytics processing on the edge we can actually respond to a lot of events at near real time.

When soliciting participants’ views on the convergence of cloud, big data, and IoT – a significant 58% agreed that it would help with data management, followed by 50% voting for predictive and prescriptive analytics, 49% selecting new business models and another 49% choosing scalable analytics frameworks.

Most of the industries surveyed agreed that harnessing these technologies will bring about synergies and unlock multiple benefits for companies. Enterprises should be ready to undergo a revamp of their existing processes and perhaps even their business models to capitalize on the benefits offered by these digital technologies.

Authored by Satish HC, EVP – Head Global Services – Data and Analytics from Infosys