Data Science & AI Trends 2019 By AIM & AnalytixLabs

10 DATA SCIENCE &
AI TRENDS IN INDIA
TO WATCH OUT FOR
2019
By Analytics India Magazine
& AnalytixLabs

of 18
DATA SCIENCE & AI TRENDS IN INDIA 2019 Analytics India Magazine & AnalytixLabs
EXECUTIVE
SUMMARY
With enterprises putting digital at the core of their transformation, our
annual Data Science & AI Trends Report explores the key strategic shifts
enterprises will make to stay intelligent and agile going into 2019. The year
was marked by a series of technological advances, including advances in
AI, deep learning, machine learning, hybrid cloud architecture, edge com-
puting (with data moving away to edge data centres), robotic process auto-
mation, a spurt of virtual assistants, advancements in autonomous tech and
IoT.
With data championing technological innovation, analytics and AI has prov-
en to be a competitive advantage for businesses across functions, such as
sales, marketing, finance, customer service and IT.
Today, Data Science and AI has seeped into many business functions and
we are seeing increased collaborations between leading companies and
Data Science Service Providers, specializing in providing tailored solutions.
Another key takeaway is how companies are planning to increase their IT
budgets on analytics and data science.
A majority of our respondents believe the growing adoption of AI and an-
alytics has become a game changer across business functions and there
will be an uptick in niche sectors such as sports. A key trend this year was
core business processes being redesigned around AI to deliver advan-
tages beyond cost savings. RPA has been widely deployed to automate
well-defined, rule-based tasks and the spurt of virtual assistants in banking
has spawned a new age of Conversational AI in finance. On the other end
of spectrum, we will see an increased AI-cloud interdependency which in
turn will fuel the growth of public cloud companies. Organisations are also
realising the benefits of cognitive technologies to provide better insights.
This year we collaborated with AnalytixLabs, a leading Data science training
institute to bring out key trends.
Check out the top trends in Analytics and Data Science to dominate in 2019.
CONTENTS
Foreword
More Industries Are Going To Utilise Analytics & AI In A Big Way
Deploying Models For Real-Time Use-Cases
Data Analysis And Informed Customer Space
More Investment In Enterprise Grade Data
Infrastructure, Platforms & Cloud
Analytics & AI Is Becoming Increasingly Pervasive & Transparent
Complexity Necessitates Greater Collaboration
Personalised Products
AI Will Make Analytics More Human, Not Less
Centralised Data Will Be Replaced By A Single View Of All data
Voice & AI Assistants Are The Future
(04)
(06)
(08)
(10)
(12)
(14)
(16)
(18)
(20)
(22)
(24)

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We believe 2019 will be the year when AI
Revolution goes mainstream. Irrespective of
industry, this year with every client we have
witnessed applications of Deep Learning gaining
impetus and deliver effective solutions to some of
the most complex business problems. Certainly,
there is unprecedented potential for value creation
for organisations of all sizes. In fact, the fast-paced
growth of AI makes it more conducive for younger
businesses to leverage the technology earlier than
blue-chip companies.
The advancement in Machine & Deep Learning
techniques has increased exponentially in recent
times, making it possible to solve a much wider
variety of problems. This coupled with inexpensive
connectivity, scalable cloud computing, massive
collaborations, and low-cost devices, has allowed
the proof-of-concepts of the past to become
concrete reality.In the end, creating an intelligent
automation process (IPA) for real-time decision-
making is the catalyst to achieve operational
excellence, increase rate of business, build
competitive advantage.
It is really exciting that trends emerged from this
research report resonates with our perspective and
in a way reassures that investments we have made
in the field of AI Deep Learning will enable us to
deliver significant value to our clients. Hope you
enjoy reading this report and find it insightful!
FOREWORD
Sumeet Bansal
Founder & CEO,
AnalytixLabs

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MORE INDUSTRIES
ARE GOING TO UTILISE
ANALYTICS & AI IN A
BIG WAY
TREND #1
Enterprises in 2019 and beyond will continue to focus on
advanced solutions and adopt technologies like artificial
intelligence and machine learning to deliver a better
customer experience. The technology landscape will also
transition to an API-based ecosystem with AI and analytics
becoming more pervasive.
Page 6 of 18
In the coming year too, more and more industries and
services including automotive, healthcare, broadcast and
telecommunications, education, finance, consumer
electronics and multimedia will adopt and implement AI
to enhance customer experience, optimise and automate
processes.
Nitin Pai, Senior Vice President, Head of Marketing & Strategy, Tata Elxsi
While we have seen industries like financial services,
consumer product goods, telecom, healthcare etc. embracing
analytics in their decision making, adaptation of data driven
decisions will see increased application in sports in different
areas from performance analysis to fan engagement.
Indranath Mukherjee, AVP, Head, Strategic Analytics, AXA XL
AI in Automobile segment is showcasing Automated Guided
Vehicles (AVGs) to move materials around automotive plants.
Multi-recognition systems monitor and analyse data to derive
driver’s attentiveness or excitement level. AI enabled wearable
IOT sensors such as H-Vex protect the workforce ensuring
safety and increased productivity.
Dr. Muthukumaran Balasubramanian, Head – BigData, HTC Global Services
In 2019, Credit Risk Analytics will deep dive in the Lending
algorithm of New to Credit (NTC) using latest AI/ML tools.
While several fintechs have come up with incremental data
enrichment for this segment, Banks & NBFCs will soon see a
crystallised analytical way of managing NTC customers
through advance analytics.
Anand K Sundaram, GEVP, Data Analytics Center, YES BANK
The acceptance of AI, ML & DL started to happen in the last
few months and now will become the key to solve business
problems making it the way of life at organisations adopting
analytics . The acceptance and application will increase
significantly in global and Indian context solving business
problems, developing new use cases and developing
innovative products & solutions based on that.
Gurpreet Singh, Director & Founder, G-Square Solutions

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DEPLOYING MODELS
FOR REAL-TIME
USE-CASES
TREND #2
The emergence of real-time analytics has become the key
to unlock insights and up to 70 percent of organisations have
increased their spending on real-time analytics. The shift
towards real-time analytics is spurred by the growth in the
number of connected devices, which has driven enterprises
towards real-time edge analytics which is helpful in finding
correlations and hidden patterns, in turn helping business
leaders make better, informed decisions. In a similar vein, the
rapid development of connected devices and IoT applications
has pushed centralised cloud computing to the edge.
Page 8 of 18
In consumer internet companies such as from the domain of
e-commerce, machine learning models are being deployed
into the real-time serving infrastructure, whereby based on the
user’s actions within the session, propensity scores are being
computed to determine best set of product recommendations
along with dynamic pricing. This is being enabled by building
models using batch data, but performing scoring in real-time.
The challenges are how to employ models with enough so-
phistication to make significant impact, yet have a low latency
response to the in-sessions scoring.
Nitin Seth, CEO, Incedo Inc.
Real time analytics will be another disruptor in the coming
years, we haven’t seen much of it yet. This means using run
time data from dashboards to make decisions. One good use
case could be traffic monitoring for designing intelligent street
parking systems.
Mohit Kapoor, CEO, DBS Asia Hub 2 (DAH2)
With the explosion in the number of IoT devices, there is a
need to harness the power of sensor data to produce mean-
ingful real-time insights. In 2019, move from batch to stream
data processing to get real-time actionable insights is going to
be massive. More and more companies will look for real-time
data analysis for events that need to be detected right away
and responded to quickly.
Akhilesh Ayer, Head, Research & Analytics, WNS

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DATA ANALYSIS AND
INFORMED CUSTOMER
SPACE
TREND #3
2019 will be the year when organisations will put “consumer”
at the heart of digital transformation. Going forward,
consumers will become a part of larger strategy around
digital transformation with enterprises building new business
models to engage users better. The study forecasts a trend
where enterprises will put data back into the hands of
users – the real “data owners” to drive better user
experiences.
Page 10 of 18
The business environment for the insurance industry has
been challenging. Data will build the foundation for an
efficient customer engagement strategy of insurance firms.
The two major segments of analytics in the online space -
group dynamics and individual behaviour, will continue to
impact the process of detailed analysis, product develop-
ment, personalised insurance products, strategy and creat-
ing customer-connect campaigns accordingly.
Vijay Sinha, MD & CEO, COCO by DHFL General Insurance
In the past machine learning output, especially deep learning
output was more of a black box as far as providing an expla-
nation to the users of the product/system. However, more and
more machine learning systems are evolving to also provide a
tie in to user’s history or behaviour in addition to providing the
actual product recommendation. With this tie-in, the users get
a context of why something is being recommended to them,
and are not surprised by particular user experiences present-
ed to them by the consumer Internet company/interface. This
is very important to retain the user’s trust in the company.
Nitin Seth, CEO, Incedo Inc.
As opposed to today’s scenario, customer data ownership will
move from businesses and back towards customers. We fore-
see new services that will emerge and empower customers to
monetise their data and rent it to companies, with the capabil-
ity to withdraw its use when they decide to. Since data is the
fuel behind powering AI, customers will begin to realise the
power they have to drive their AI-based experiences with data
control and data management practices.
Suman Reddy Eadunuri, MD & Country Head, Pegasystems India

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MORE INVESTMENT IN
ENTERPRISE GRADE DATA
INFRASTRUCTURE,
PLATFORMS & CLOUD
TREND #4
With an increasing investment in cloud and quantum
computing, business leaders emphasise an increasing AI
interdependency on the cloud. In fact, we believe AI services
have fuelled the growth of public cloud providers, especially
AWS, Microsoft, Google and Alibaba. Going into 2019, we will
see an increased AI-cloud interdependency and as we
observed in 2018, leading cloud giants are pursuing an
AI-lock in approach by providing open source AI-related
services. The next year will see AI platforms dominating the
public cloud market and cloud providers, especially Google,
AWS and Microsoft will further expand their AI cloud
portfolio. A key reason why enterprises are leaning towards
public cloud is that it is impossible to build in-house, scalable
AI systems and the general sentiment is that AWS leads in
cloud and AI deployments.
Page 12 of 18
2019 will witness increased growth of Big data platforms into
medium & small size organisations. Big data prominence over
the last few years has led to the generation of enormous
amount of data. This data has colossal potential for analysing
existing and new consumer patterns, research, identifying
new trends etc. Converting data potential to reality, will need
exponentially improved speed of computing, hence the rise
of quantum computing. Companies have started investing in
this space, however results are yet to be seen.
Sudhanshu Singh, Senior Vice President, Analytics and Research, Genpact
Public clouds will increasingly be the host platform for AI, will
evolve to reduce complexity, and will have reduced reliance
on IT departments. Powerful GPU instances, flexible stor-
age options, and production-grade container technology are
just a few of the reasons that AI applications are increasingly
cloud-based. For engineers and scientists, cloud-based devel-
opment eases collaboration and enables on-demand use of
computing resources rather than buying expensive hardware
with limited lifespan. Cloud, hardware, and software vendors
are recognising, however, that this technology stack is often
difficult for engineers and scientists to setup and use in their
development workflow.
Paul Pilotte & Bruce Tannenbaum, Heads, Data Analytics & AI marketing, MathWorks

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ANALYTICS & AI IS
BECOMING INCREASINGLY
PERVASIVE &
TRANSPARENT
TREND #5
2018 was a year when analytics and AI showed serious
commercial applications and usability. We have observed a
shift how technology companies are using AI and analytics
to significantly improve their marketplace opportunities. As
we move forward in 2019, we see an emergence of cognitive
technologies, which go beyond automation to computer
vision, deep learning and language technologies which
include speech recognition, sentiment and text analytics,
natural language processing and generation. As we press
forward, we will see a rich ecosystem of platform companies
offering AI applications with high business value.
Page 14 of 18
AI is gaining a nearly ubiquitous presence, and machine learn-
ing and deep learning are getting more sophisticated. As we
enter 2019, we will see a rise of AI and cognitive platforms
aimed to help businesses mine and analyse massive amounts
of unstructured data (in form of text, images, and videos) to
derive actionable insights, a thing we’ve not been able to un-
lock in the past. With this greater depth, we could see more
commercial use cases of image and speech analytics in the
nearer term
Akhilesh Ayer, Head, Research & Analytics, WNS
As AI Augmented analytics (predictive & prescriptive) be-
comes more mainstream, there’s going to be a much wider
audience than the data scientists that it was previously limit-
ed to. How do you get every employee, who may have never
directly accessed data, or has years of “experience” under
their belt, begin to trust a prediction or recommendation that
analytics software gives them? AI will need to be transpar-
ent. Narrative storytelling & explanations on how the software
came to a prediction will replace static charts and numbers. AI
will need to be measurable . Automated feedback loops that
self-report differences between actuals and predicted metrics
and drive retraining of the models will be standard features in
any AI augmented analytics product. AI will need to be held
accountable. Concepts such as “protected variables” (zip
code, gender) to prevent bias entering models will be the top
5 requirements in any RFP. Analytics products without these
features will not be able to gain the trust of the end users and
thus ROI and adoption will suffer.
Ketan Karkhanis, GM of Einstein Analytics, Salesforce
Businesses are realising that analytics competency can not be
built within a silo in the organisation. Everyone has to talk the
language of data. Everyone has to be data savvy. 2019 will be
the year that everyone becomes ‘Data Smart’. Data literacy for
everyone in the organisation is going to be the mantra for 2019.
Gaurav Vohra, Co-founder & CEO, Jigsaw Academy

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COMPLEXITY
NECESSITATES
GREATER
COLLABORATION
TREND #6
The transforming nature of technology and the changing
dynamics will necessitate increased collaborations between
engineering teams and data scientists. As we inch towards
2019, business leaders lay down the friction points in prod-
uct lifecycle management, complexity of projects and lack of
expertise which will have to be resolved to simplify workflows
and improve results. What we observed in 2018 was a high
adoption of new tools which helped in setting up templates
and simplifying workflows and coding.
Page 16 of 18
The increased use of machine learning and deep learning in
complex systems will necessitate many more participants and
greater collaboration. Data collection, synthesis and labelling
are increasing the scope and complexity of deep learning proj-
ects, requiring larger and decentralised teams. Systems and
embedded engineers will require flexibility to deploy inference
models to data centers, cloud platforms, and embedded ar-
chitectures such as FPGAs, ASICs, and microcontrollers. These
teams will also need expertise in optimisation, power manage-
ment and component reuse. Engineers at the center of collab-
oration, developing deep learning models, will need tools to
experiment and manage the ever-growing volumes of training
data and lifecycle management of the inference models they
handoff to system engineers.
Paul Pilotte & Bruce Tannenbaum, Heads, Data Analytics & AI marketing, MathWorks
As data science teams grow, the need for collaboration and
version control will become paramount. General purpose engi-
neering tools like Git, JIRA, Trello etc. would become more and
more important for data scientists. Also, new tools specific to
data science collaboration would get adopted - some of them
are already popular - such as mode studio or dbt.
Ankur Sharma, VP, Analytics, Instamojo

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PERSONALISED
PRODUCTS
TREND #7
AI-driven personalisation has become the answer to
marketers’ woes and the growing use of analytics and AI is
helping businesses to deliver customised communications
and services. There is a growing usage of customer
segmentation and data to better understand user behaviour
and deliver a curated set of services in real-time to serve
consumers. Especially in the financial sector, there is a
growing uptick in the adoption of analytics to rethink
customer engagement and the segmentation approaches.
Page 18 of 18
Customers prefer personalised insurance covers instead
of the one-size-fits-all products. Flexible coverage options,
micro insurance and peer-to-peer insurance are becoming
viable options. Lifestyle apps are re-imagining the insur-
er-insured relationships. APIs are enabling the creation of
insights-driven offerings as they integrate data from mul-
tiple sources. Premiums will become highly personalised,
enabled by new sources of tech-enabled data such as
Internet of Things, mobile-enabled InsurTech apps and
wearables.
Anand Pejawar, President-Operations, IT & International Business, SBI Life Insurance
Some lenders are already personalising the targeting for
sourcing loan applications. However, we are yet to see person-
alised interest rates beyond broad segments. We are headed
towards a world of ultra-personalisation where a loan’s ap-
proval, the amount, and the interest rate would depend on the
applicant characteristics, the purpose of the loan, the tenure
requested, and the external environmental factors of that time.
o Pre-loaded/Dynamically-loaded EMI cards with person-
alised interest rates will become more ubiquitous.
o Personalised restructuring of loans/add-on loans will be-
come a norm.
o P2P lending and newer NBFCs in the form of start-ups and
big-tech will increasingly compete with traditional banks and
NBFCs, driving reduction in interest rates and increasing the
pace and personalisation.
Dr. Satya Gautam V, Head of Data Science and Artificial Intelligence, CreditVidya

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AI WILL MAKE
ANALYTICS MORE
HUMAN, NOT LESS
TREND #8
Amidst the ongoing debate about AI siphoning off jobs, there
is an increased emphasis on developing human-level AI.
Leading tech giants such as Microsoft and Google are setting
up ethical committees to monitor how technology impacts
human lives and eliminate bias in data. On the other
spectrum, we have seen an think tanks across the globe
which are working alongside Governments and enterprises
to help companies create new economic opportunities for
citizens and also set up new ways to benefit the society as a
whole.
Page 20 of 18
There is rightful concern about the rise of AI and its
potential to eliminate jobs. But in the near future, AI will
likely create more jobs than it eliminates. Gartner predicts
that in 2020, AI becomes a positive net job motivator,
creating 2.3 million jobs while only eliminating 1.8 million
jobs. In 2019 and beyond, AI designed around people will
have a higher impact than AI that takes people out of the
process. By 2020, augmented analytics will be a dominant
driver of new purchases of analytics and BI as well as data
science and machine learning platforms, and of embedded
analytics.
Arun Balasubramanian, Managing Director of Qlik in India
We’re getting past a point where basic intelligence is sufficient
for consumer-facing AI. Customers are realising the need for
brands to view them as individuals and not of the many cus-
tomer data records. In 2019, vendors are expected to focus
on increasingly humanising AI along with empathy. This could
mean picking up clues on customer motivation, their current
mood perceptions, or how they could act in certain situations.
Suman Reddy Eadunuri, MD & Country Head, Pegasystems India

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CENTRALISED DATA
WILL BE REPLACED BY
A SINGLE VIEW OF ALL
DATA
TREND #9
As data volumes grow high, organisations are moving towards
enterprise data management platforms that helps
businesses gain insights faster by breaking down the data
silos. To resolve integration challenges, we will see
enterprises bringing data from various sources into one,
central repository. As forecasted, a central data store will be
the key to fulfil analytical requirements of organisations.
Page 22 of 18
Two massive trends are changing the landscape. First, differ-
ent vendors are coming together to standardise data models.
Cloud-based data sources in particular will have more stan-
dard formats. Second, and more important, is the emergence
of enterprise data catalogs. These catalogs are accessible in
a hub, with one view of the entire federated data estate, and
deliver a shop-for-data marketplace experience. The more
you share, collaborate, and use the hub, the more valuable it
becomes to the business. Furthermore, it links your analytics
strategy with your enterprise data management strategy, as
the data becomes analysis-ready. In 2019, focus will shift from
bringing all data together into one place to getting a single
view of all data. By 2020, most D&A use cases will require
connecting to distributed data sources, leading enterprises to
double their investments in metadata management.
Arun Balasubramanian, Managing Director of Qlik in India
In 2019, we need to watch out for the convergence of various
data sets onto single AI platforms. Up until 2018, corporations
have been looking for AI solutions in silos - Chatbots, RPA
tools, etc. But we missed out the real fodder for AI, DATA! The
solutions for 2019 will unite structured and unstructured data
to automate entire processes. With hiring for instance, bots will
read the JD/KRA and the resumes, rank them in order of rel-
evance, reach out to candidates automatically, engage them
with chatbots for a first-level screening along with addressing
their queries. If the candidate and the corporate are interested,
the interview will be scheduled. Following that, if the candidate
gets selected the bots can further engage them as an assis-
tant. If all this data is on the same platform the results would
be phenomenal.
Animesh Samuel, Co-Founder & Chief Evangelist, Light Information Systems

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VOICE & AI
ASSISTANTS ARE
THE FUTURE
TREND #10
If 2018 belonged to AI, 2019 will be the year when
conversational AI and digital voice assistants will dominate
sectors such as finance and retail. In India, the year was
dominated with voice-based digital banking chatbots that
are able to provide accurate banking solutions. The trend has
been fuelled by tech giants releasing their APIs for
voice-based conversational AI, which helps businesses to
build intelligent services. Going forward, we will see more
industries diving into voice-enabled AI services.
Page 24 of 18
In 2018, nearly 20% of American adults, or 47.3 million people,
had access to a smart speaker; nearly anyone who owns a
smartphone can use a voice assistant like Siri, Cortana, Alexa,
or Google Assistant. As a group, we’re used to incorporating
these assistants in our daily lives; look for that trend to migrate
further into our working lives as 2019 progresses. How will we
use these virtual assistants? In much the same way as we do
outside of work: managing calendars and schedules, setting
appointments, finding information, and scheduling reminders.
Suhale Kapoor, EVP & Co-founder, Absolutdata
The usage of AI has picked up in the last couple of years.
Chatbots and voice enabled digital assistants are gaining in
popularity. This trend will continue and the focus will be on
making the responses more contextual and back and forth
conversation enabled.
Gunjan Gupta, Senior VP & Head of Analytics at Bajaj Allianz Life Insurance
Conversational platforms will evolve rapidly in 2019, catering to
larger set of Indian languages and will enable more complex
conversations with users by leveraging advanced deep learn-
ing techniques for NLP. This will also enable analytics applica-
tions to percolate deeper towards the base of the pyramid.
Ankur Narang, Head, Data Science & AI practice at Yatra Online

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DATA SCIENCE & AI TRENDS IN INDIA 2019
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