SAS partners with state govts to implement epidemiological models to predict timing and magnitude of Covid-19 infection

India is known for its abysmal healthcare infrastructure especially in the rural area where citizens struggle to get medical services. Coronavirus has further deepened the healthcare crisis in India. What sort of analytical solutions does SAS offer so that the situation can be mitigated? Has the company entered into an agreement with the Indian government to help the latter fight against the pandemic?

We have not entered into any specific country wide agreement but are very actively working with states governments across the country basis their specific requirements to help fight the pandemic. Some of the key priorities we are broadly focused on are contact tracing for public health, medical resource optimisation and situational awareness/critical response analysis amongst others.

We are working with Maharashtra, Rajasthan, Odisha, Assam and UP to provide Covid-19 dashboards based on various analytical modelling to equip them to understand the current situation and combat the pandemic. The actionable insights obtained from the analysis helps to address the current needs and mitigate the risk of the spread of disease in near to mid-term horizon. SAS’ analytics solution is also helping states to estimate resources like beds, ICU infrastructure and ventilators by simulating the disease spread for various levels of R0 and lockdown. 

In case of situational awareness, SAS is working with state governments to empower them to understand where the Covid-19 cases are emerging from, travelling population from outside the country, interstate travellers and possibilities of individuals returning to the state after the lock down. Additionally, SAS is working with these states, providing simulation analysis to help them understand the gravity of the current issues and challenges and accordingly provide actionable insights to counter the current scenario. 

Through its analytical modelling, SAS is predicting the expected surge in number of infections, deaths and is delivering insights considering the lockdown percentages in the worst and best scenarios. The insights emerging from the various mapped scenarios are enabling the states to be equipped if the lockdown is being partially lifted in some areas and completely in future. The insights derived from the analysis is also supporting these states to manage their infrastructure and further mitigate the risk of the spread of the virus.

What is your new business strategy for India post coronavirus? What are some of the new measures to bolster SAS’s presence in the Indian market?

At the onset of the pandemic, we setup a special team internally (i.e. COVID Incident Command System (ICS)) to co-ordinate SAS efforts to support our customers through this crisis. In addition, we partnered with health care providers and state public health agencies to implement epidemiological models that could help predict the timing and magnitude of the Covid-19 infection in the community and peak demand on health care resources.

We built our approach around the 3 phases as we see it: Respond, Recover and Reimagine to help mitigate the disruptions caused by the pandemic. The initial stage – Respond was to help governments and healthcare agencies to focus and prioritise on tackling immediate concerns. As economic activity resumes and we move to the Recover and Reimagine phase, the use of AI and analytics technologies becomes even more significant, to help identify which impacted communities need the most assistance, boost productivity while optimising labor, and helping detect fraudulent activities. Saying so we are working with banks and financial institutions maximise their operational foresight with the help of analytics to help in their business continuity planning and managing their liquidity position. We are working across industries to help manufacturing, retail and CPG organisations to stabilise end-to-end supply chains as well as improve demand sensing decisions across the supply chain. Analytics plays a huge role in such situation of changing demand patterns and constant market disruptions. The Reimagine phase is all about future-proofing organisation by building resilience.

Beyond just the Covid situation, analytics will continue to play a crucial role as the importance of data continues to rise in businesses across sectors. Banks continue to use SAS’s advanced analytics in the areas of risk management specially to meet regulatory demands as well as to fight the high number of incidents of fraud. For governments, investment in advanced analytics can vary from helping them automatically detect evasion in tax to initiatives to help ensure protection of endangered species. With technology advancements, there will be an ever-increasing demand in the areas such as AI, Machine Learning and Deep Learning in order to efficiently and effectively accomplish business goals.

Can you provide an overview of how analytics has evolved since the early days of the company?

Data Analytics is based on statistics, which can be traced as far back as ancient Egypt for building pyramids. As an organisation, we have seen the evolution of what was merely called ‘statistics’ back then to the popular AI and ML age now. The advent of business analytics began with tools that could produce and capture a larger quantity of information and discern patterns in it far more quickly than the unassisted human mind ever could. And many of us would have seen what we call the three generations of analytics. 

Analytics 1.0: This was the era where analytics provided deep understanding of important business phenomena and gave managers the fact-based knowledge to go beyond intuition when making decisions. 

They called it, “Business Intelligence” and was probably the first time that data about production processes, sales, customer interactions, and more were recorded, aggregated, and analysed. But this type of analytics functioned only on small datasets and very often the analysis would take weeks or months to perform. Moreover, the reporting processes addressed only what had happened in the past; they offered no explanations or predictions. With the aim to improve operational efficiency to make better decisions was born Analytics 2.0.

Analytics 2.0: This was the era of big data- where data was not generated purely by a firm’s internal transaction systems. It was externally sourced as well, coming from the internet, sensors of various types, public data initiatives such as the human genome project, and captures of audio and video recordings. As analytics entered the 2.0 phase, the need for powerful new tools—and the opportunity to profit by providing them—quickly became apparent. Innovative technologies of many kinds had to be created, acquired, and mastered. It became difficult to analyse Big data fast enough on a single server, so it needed to be processed with Hadoop, an open source software framework for fast batch data processing across parallel servers. As cloud became popular, more information was stored and analysed in public or private cloud-computing environments. Other technologies introduced during this period include “in memory” and “in database” analytics for fast number crunching. Machine-learning methods (semi-automated model development and testing) were used to rapidly generate models from the fast-moving data. Black-and-white reports gave way to colorful, complex visuals. This paved the way for Analytics 3.0

Analytics 3.0 -the era of data-enriched offerings.  This was when analytics was enhanced to support customer-facing products, services, and features. It caught the attention of organisations with its better search algorithms, recommendations engine, and highly targeted ads, all driven by analytics rooted in enormous amounts of data. This provides organisations with the ability to analyse complex data sets for the benefit of customers and markets. It also provides the ability to embed analytics and optimisation into every business decision made at the front lines of operations. For companies that use analytics mainly for internal decision processes, Analytics 3.0 provides an opportunity to scale those processes to industrial strength. Creating many more models through machine learning, enables organisations to become much more granular and precise in its predictions.

To sum up, data analytics involves the research, discovery, and interpretation of patterns within data that stretches from hindsight focused descriptive business intelligence to forward looking predictive and prescriptive analytics. The future is clearly going to revolve around operationalising AI, ML, automation and cloud analytics to drive business value. Over the last few years, analytics has moved from being a backroom conversation to a boardroom one and that, in my opinion, is here to stay.  

What kind of collaborations have you planned with the Indian government to help the latter, especially in the use emerging technologies to benefit its citizens?

Over the last few years governments have been one of the fastest adopters of technology. With the vision of Digital India programme to transform India into a digitally empowered society and knowledge economy, as one of their key focus areas – governments have only doubled down their investments in technology. The current pandemic has led governments to further realise the need for taking technology to the next level to maximise the benefits from its use. We implemented the Odisha State Covid-19 Dashboard a few weeks ago, which enables the government to estimate susceptible population and required resources such as hospital beds and ventilators. In Mumbai, SAS is closely working with MCGM (Municipal Corporation of Greater Mumbai) on the Covid-19 relief work by offering analytical dashboards on the food distribution across 26 wards. The analysis empowers MCGM to distribute food and monitor its progress. Dashboards like these help the governments keep a close eye on the situation enabling them to take timely decisions.

On the healthcare side, we are working closely with National Health Authority (Ayushman Bharat), the world’s largest healthcare scheme to enable the department in detecting and minimising health care fraud using advanced analytics. The scheme is aimed to provide a health cover of INR five lakh per family per year to cover secondary and tertiary care hospitalisation to over 10.74 crore poor and vulnerable families across India.

We continue to work very closely with CBDT (Central Board of Direct Taxes), CBIC (Central Board of Indirect Taxes & Customs) and as well as commercial tax departments across states in widening the tax net and helping detection & prevention of Fraud and Tax avoidance.

In today’s day and age, the use of social media has increased exponentially. Most of these tech users share their opinions, thoughts through blogs, social media sites, e-commerce site etc. This becomes a wealth of information for governments to analyse or summarise in order to take decisions. We are working with governments including Law enforcement agencies across India for this Sentiment analysis and opinion mining.  SAS provides powerful Natural Language processing techniques which help in categorisations of comments across topics, to calculate the numbers of positive, negative and neutral tweets from events, policies, geography, and provide visualisations and graphical representations of the social media landscape. This is helpful for evaluation of government performance monitoring from people’s perspective instead of conducting people’s surveys which is more time consuming. SAS has also been working with some state governments to analyse malnourishment across the state so as to enable timely food distribution to particular villages or districts.

Many companies have taken analytical capabilities to the next level in the last few years, but many are just getting started. What is your advice to them in terms of establishing a roadmap?

For those who are just starting off, embark on small scale experiments in a defined manner to taste success. As investments increase, early successes are likely to be followed up with operationalisation and mainstream adoption in the time to come.  

Some quick tips that may be useful for establishing your roadmap:

• Identify the right use case for your organisation: Plan, start and if you fail, fail fast and start again with your new learnings.
• Understand the business strategy and strategic focal areas: Your analytics strategy must be rooted in the business strategy to yield business success
• Develop your analytics vision and set target analytics maturity levels for your core processes: The use of a maturity model allows your organisation to have its methods and processes assessed according to management best practice, against a clear set of external benchmarks
• Develop Business Ideas for Analytics: Form sets of concrete initiatives that will help you reach your strategic ambition
• Prioritise and develop the roadmap: This highlights the critical decisions, or trade-offs, your company must make and define the initiatives it must prioritise. It also includes building a robust analytical architecture will be key to realising the business outcomes set forth in the roadmap.
• Making analytics projects a success This is not just about how you plan and execute a programme. It is also about people (i.e right skillset) and processes and most importantly executive/ leadership buy-in.
• Lastly, develop an enterprise-wide view of compelling opportunities, potentially transforming parts of the current business processes to embrace algorithms.