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Source: Digital Journal
Sudip Saha, COO from the Future Market Insights, is developing actionable business intelligence to help businesses navigate challenges and take critical decisions with confidence and clarity amidst breakneck competition. Saha explains to Digital Journal how businesses can utilize a range of new technologies and ways of working in order to drive 'data-driven decision-making'.
Sudip Saha: There is no denying that data has assumed far greater significance for businesses in 2020, and investments to procure data will only grow manifold in the near future. The general consensus is that every 7 in 10 organizations around the globe are affirmative on increasing their investment in data in the next three years. However, it is also pertinent to understand that procuring data is only half the job done – using this data to derive insights that actually help businesses create value is the real gargantuan task. To put this in perspective, while every 7 in 10 organizations will invest in procuring data, only 3 out of 10 are able to use the colossal data to drive action.
Considering the brouhaha over the terms, Big Data, Artificial Intelligence, and Machine Learning, it's difficult not to mistake one for the other. However, it is important to understand that data and insights are not synonyms – they are aspects of a methodology whose broader aim is to drive business growth. A firm can have millions of data points at its disposal, but inferring this colossal data and putting it in perspective with respect to the business pain points is the real task.
In my opinion, we will see the current narrative, which is heavily tilted in favor of data, to evolve into how efficient processes can be built around harnessing real insights from the data. So, to summarize, data is immensely important for modern businesses, but significantly more important are the real actionable insights.
Saha: Since times immemorial, humans have shown an insatiable obsession to know 'what will happen in the future'. And, this innate human trait has led to the development of many successful (and unsuccessful) technologies that can analyze historical patterns to chart out what 'may' happen in the future. Predictive analytics has evolved through decades to reach a stage where it is being successfully implemented across organizations to meet a range of business objectives. The applications of predictive analytics are multi-pronged – from diligent risk assessment and churn prevention to sales forecasting, market analysis, and financial modelling, predictive analytics has been proven to be successful in anticipating the likely future behavior. For example, the use of predictive analytics in the healthcare sector can have far-reaching impact. The use of predictive analytics can improve the accuracy of diagnosis and treatment, while significantly aiding in managing operations effectively.
It is important to mention here that the potential of predictive analytics is yet to be fully realized, as investment and skepticism about its actual ROI have kept many businesses on the fence. However, as technology becomes affordable, and predictive analytics becomes more mainstream, we will see it become a standard (along with other processes) in many organizations.
Saha: What are they thinking? What will appeal to them? What should I do to nudge them? As long as there has been an open marketplace, sellers have been using everything they have at their disposal to know what's going in the consumers' head. Thanks to new technologies, this curiosity has been alleviated, albeit to a certain level. Through a combination of AI's machine learning, deep learning, consumer analytics, and traditional behavior science, we are at an advanced position of understanding consumer behavior.
Saha: Organizations are now concentrating on different ways to enter a new domain of consumer understanding. Today, limiting the analytics to a highly structured format excludes about 80% of unstructured information available in call logs or on social platforms. This is where Natural Language Processing (NLP) steps in. Amidst the race to provide a personalized touch, the only way by which a machine can speak to a consumer is NLP. Integrated with Machine Learning and Deep Learning, NLP moves beyond conventional rule-based algorithms to handle different areas than consumer interaction.
Evolving from human-computer interaction to conversation, NLP is a must-have for businesses to discover in-depth customer insights and for machine translation. This is not the only use case where NLP emerges as a game-changer. Sentiment analysis, cognitive analytics, email filters, and voice recognition are deeply penetrating businesses to help them sustain and stay at the top. However, it is imperative to note that human emotions and experience continue to be significant in 'real' interaction.
Saha: Business process automation is everywhere – transforming inefficient and error-prone tasks in the workplace. Speed is another area in which automation steals the limelight. Robotic Process Automation (RPA) accelerates consumer interaction, brings products to the market faster, and fulfills contract requirements sooner. Chatbots – a vigorous new medium to increase sales and assist a wide range of customers – is one particularly conspicuous example, and one where NLP thrives.
Today, more than 5 out of 10 consumers are showing preference for messaging over phone calls to resolve their service issues. Call centers are thus targeted as the next phase of the Chatbot roll-out revolution. Automation will become an integral part of businesses to maximize effectiveness with limited personnel. Cognitive Automation or Intelligent Automation that employs RPA is also heralding as the new era of innovation, changing the way businesses operate.
Saha: DX usually strikes me as an effective marketing tool, which is foremost in all of our minds. Disruptive technologies are taking today's business environment by storm, but their usefulness is dubious. Companies implement technology when needed; the idea of a digital 'transformation' and transformation initiatives usually turn out to be an advertising hype.
It doesn't surprise me, unfortunately, that 70% of digital transformations fail. Digital transformation requires changing both infrastructure and culture at organizations, which is likely to result in having less control rather than more profit margins. Tech-driven products and services developed with playbooks from the past can also make experiences disjointed. Executives and top managers need to be on the same page for better returns on their digital yet strategic investments.