The scope and use of artificial intelligence (AI) and data science have grown leaps and bounds over the last few years, more so, in the time of the pandemic, as companies are moving towards change management to make businesses flourish in these tough times.
In a recent interaction with CXOToday, Gaurav Shinh, Founder and CEO DAAS Labs (Data Science and Analytics Services Labs), explains the new opportunities and the frontiers which Data and AI have created in the present times.
CXOToday: Do you see an increase in the dependence on AI and data analytics across industry during the time of crisis?
Gaurav Shinh: Today, the world is changing and is changing very fast, behavioral segments are getting defined and redefined on a daily basis, consumption patterns are changing and industry landscapes are shifting, and at the same time our relationship and our interaction with the things are being renewed constantly – under such circumstances can we even imagine a world where adoption of data and AI will no longer be a choice. Data brings transparency across business processes whereas AI models provide early signs and help businesses stay relevant in the changing world. The new opportunities and the frontiers which Data and AI have created in the present times were beyond imagination a decade ago. It has challenged the existing business models and led to the folding up of the businesses which were complacent and failed to align themselves to the newer world of opportunities created by Data and Analytics.
CXOToday: How has the pandemic helped more and more industries to embrace data science as the way forward?
Gaurav Shinh: The pandemic has questioned the relevance and existence of the firm. Run rate, Cost, and contactless delivery became the new norm. Now, Enterprises want to evaluate and understand their cost position in terms of stock at hand, fulfilment, and how current and future demands are shaping and eroding. The management style of “ASK the Employee” doesn’t work anymore as everything is virtual – this created a big adoption towards building data-driven organizations and using data sciences to make business decisions. The supply chain resilience is a hot topic. Imagine, can this be possible without integrating data across the value chain, without looking at holistic demand and supply or planning for adverse scenarios and shock conditions?
Capturing data from multiple sources and making sense of the data was a very painful task couple of years ago. With the adoption of AI into the mainstream, the discovery, integration, and exploration of data have become very easy. AI models hook up to data stores, identify the patterns, performs metadata discovery, and can automate the build of the data lake or build of Golden Source of Information for the company.
CXOToday: What are the new trends you are observing in the area of analytics during the COVID-19 era that can help speed up decision making for businesses?
Gaurav Shinh: Many companies are moving from descriptive analytics to prescriptive or predictive analytics to build resilience in their business practices in order to be better prepared for the uncertainty and shock. Whether it is a case of supply chain resilience, demand forecasting, and inventory optimization – the focus is on achieving operational efficiencies and making decisions based on data and facts rather than guts. The trend, I believe, will continue post-Covid era as companies increasingly understand the importance of data today, and contactless will be the new norm. In fact, I foresee a portion of the budget allocated to these models and to build business resilience.
CXOToday: How does NLP allow for smoother functioning businesses?
Gaurav Shinh: NLP has enabled businesses to interact with the computer in a language that is native to the businesses rather than businesses learning the language in which computers interact. In the pre-NLP era, business teams were supposed to understand SQL so that they can make sense out of that data. NLP now has turned the equation, where machines need to do the hard job of understanding the context and intent of the conversation. This has led to completely change the way by which we interact with machines. Enterprises now can ask questions in plain English and the computer will comprehend and articulate the answer back in plain English.
Also, NLP is one of the key technologies which can help companies understand the dark side of the data. Worldwide, there are many companies that are working to build knowledge graphs and information classification models by sniffing data stores such as share points, shared drives, emails, etc. There is a lot of information captured and codified in these sources which NLP is now enabling to discover.
CXOToday: How is AI helping in reducing security concerns?
Gaurav Shinh: Earlier, the vulnerability scan, and security patches were done through the rules-based system. These systems rely on human knowledge to define and configure the rules for both the detection and defining the defense mechanism in case of a security breach. Now, AI models have come up which identifies the pattern of attack and generate rules on the demand. This enabled companies to move to an era of ‘Proactive’ defense rather than a reactive response-based system.
CXOToday: While companies are leveraging new technologies to thrive in this uncertain world, skills gap challenges continue to exist. What are companies doing about it?
Gaurav Shinh: There is definitely a mismatch in demand and supply. Many companies have already acknowledged it and have defined a roadmap and a strategy to reduce the gap. The companies have augmented these resources both internally and externally. Some of the companies have taken an academy approach where they are nurturing the junior talent so as to create a talent pipeline. Many companies have now matured their CoE models where these CoEs act as capability centers to supply these skillsets internally. The companies are transforming themselves and have undertaken a transition journey as a data-driven organization. Such organizations have also added the capability build-out as part of the strategy and invested in senior roles like chief analytics officer, chief digital or data officers.