This blog is authored by Dinanath Kholkar, Vice President and Global Head of Analytics and Insights at Tata Consultancy Services. Listen to him at NASSCOM BPM Strategy Summit 2017
With digital becoming all pervasive, enterprises have to reimagine their business models to stay agile, compete, and relate to their customers. However, achieving this will require the intelligent and insightful use of data, leveraging technologies like the Internet of Things (IoT), analytics, cloud, automation, and artificial intelligence (AI), among others. Companies are already aware of the many benefits that digital transformation brings to the table. The payoffs speak for themselves – increased compliance, lower costs, and above all, data-driven insights that can deliver powerful business outcomes.
These emerging technologies, however, are building blocks for establishing a real-time digital enterprise (RTDE). Organizations need to transform into forward-looking, real-time digital enterprises: a real-time digital enterprise establishes a digital core, becomes intelligent and social, and enhances its end-customers’ customer experience. However, in isolation the capabilities of digital levers are limited. In the absence of analytics, Big Data can only be a simple repository of highly structured information, limiting the enterprise’s ability to gain relevant insights. Similarly, automation in isolation would largely function as an unintelligent tool for handling tedious, rule-based tasks.
I have observed that some organizations in their effort to be a real-time digital enterprise, choose to take the plug-and-play approach – deploying tools and adopting selective digital levers to cover urgent business needs or simply to become early adopters. Without a clearly defined implementation roadmap in place, technology stacks will keep piling up as the business continues to expand till a point where there are countless services, databases, third party systems, and applications operating out of sync. Such a strategy could prove to be very costly and should be avoided at best.
A judicious combination of all the digital levers is hence critical to the success of an organization’s journey of digital re-imagination. This approach could help an organization simplify its systems, improve data analysis, and reduce hardware capacity. As a result, organizations are bound to drive business growth and achieve a sustainable margin improvement.
One thing is for certain—data is the fuel for driving digital transformation. From the myriad data streams, analytics can glean contextual insights from historical records to forecast the future. By visualizing relevant data patterns, businesses can better understand not just their customers but also themselves—how they operate, make decisions, and interact with the market. Supported by the requisite investments and emerging standards like Cross-Industry Standard Process for Data Mining (CRISP-DM), Predictive Markup Model Language (PMML), Portable Format for Analytics (PFA), enterprises can leverage platforms on the cloud for harnessing faster, code-free, and out-of-the-box Big Data analytics.
Businesses are already deploying analytics combined with AI to upend traditional manual processes. The US Department of Health and Human Services for instance, has been investigating AI’s potential to process public feedback regarding their regulations. Their findings reveal significant potential savings in employee efforts. With AI and machine learning in the mix, enterprise systems will become capable of not just performing its designated duties, but also learning in the process, as well as preparing to handle increasingly complex business problems. In short, these components need to be tied together meaningfully with the company’s myriad processes, infrastructure, and application ecosystem by a service integration and orchestration layer that will eventually create a unified digital operations architecture.
With time, enterprises will be capable of building predictive models that enable faster decisions and automate far more complex processes. This would also entail transitioning from lower level descriptive and diagnostics analytics to higher forms of intelligence like predictive, prescriptive, and even cognitive. Advances in natural language processing (NLP) will rewrite the rules of communication and the enterprise’s ability to process data across multiple sources and channels. This creates the scope for driving large scale optimization to deliver richer, more personalized experiences.
Enterprises across industries are already embarking on this analytics-driven transformation journey. Retailers, for example, are harnessing the power of analytics to deliver compelling customer experiences—from the store to the door. By factoring in weather data, they are able to anticipate seasonal demand and even forecast adverse conditions that might disrupt logistics. Considering that a large part of a service’s value is derived from its supplier and supply chain, business success is inescapably linked to supply chain performance. Going forward, one of the core qualities of the next generation supply chain will be agility – the ability to proactively acknowledge demand signals before they arise and constantly confront changing conditions. Predictive and prescriptive analytics have made this possible to an extent and functions within the supply chain that depend on response velocity will benefit immensely by removing its reliance on a human decision maker.
On the manufacturing side of things, predictive analytics and machine learning are helping drive up production capacity while reducing material utilization. This is achieved by anticipating parts requirements, automating supplier assessment, and rationalizing spares inventory, thereby improving product design yield. In turn, manufacturers today are empowered to scale up operations exponentially. As a response, utilities are also applying analytics to predict energy demands, assess reliability of power generation assets, and automate responses accordingly. In that respect, smart grids are quickly morphing from a buzzword to ground reality with the European Commission expecting over 200 million smart electricity meters to be installed by 2020 — making the case for analytics even stronger.
India as a country has already embarked on a larger scale Digital transformation journey. Given the scale, diversity and a wide range of opportunities we have in front of us, it is safe to assume that data and analytics would be the primary drivers. Data would be the bedrock of transparency and good governance, whereas analytics would play a key role in making informed decisions and formulating the right policies that would benefit the population. Knowing this, NASSCOM has already taken the lead to invest on data and analytics to foster an eco-system for all stakeholders to benefit.
Given that this pace of change is unrelenting, analytics will continue to be a critical tool for enterprises on the path to becoming agile, intelligent, and efficient. Thriving within this digital paradigm will require companies to become early adopters with clearly defined goals. It is analytics that will be the center piece of a ‘sense, think, execute, learn, and enrich’ framework—creating a closed loop of continuous innovation.