Year: 2015

JERSEY CITY, New Jersey, December 21, 2015

Fractal Analytics (https://fractal.ai), a global provider of advanced analytics, today announced that Dr. Lata Iyer has joined the firm as head of Artificial Intelligence and Machine Learning (AIML) practice. Lata brings more than 20 years of research and solution innovation in the areas of machine learning, text & speech analytics for insurance, CPG, pharmaceuticals, healthcare and retail industries. She holds a Ph.D. in computational physics from the University of California – San Diego and Masters in Nuclear Physics from Mumbai University.

“AI is transforming how organizations use structured and unstructured data from within and outside the enterprise to make better and more automated decisions. I am delighted to have Lata lead our AI and machine learning (AIML) practice and solutions,” said Srikanth Velamakanni, co-founder and CEO, Fractal Analytics. Dr. Lata Iyer further said, “Fractal represents the kind of visionary company I’ve been looking to be a part of. I look forward to further strengthening Fractal’s team and capabilities and realize my vision for the next generation of AI solutions.”

Dr. Iyer adds to the company’s key executive hires in AI this year. Earlier this year, Fractal Analytics announced the acquisition of Imagna Analytics, appointing its founder Prashant Warier as Chief Data Scientist. Fractal Analytics also acquired Mobius Innovations this year to strengthen the company’s Customer Genomics® solution for hyper-personalized marketing.

About Fractal Analytics

Fractal Analytics is a global analytics firm that helps Fortune 500 companies gain competitive advantage through deep understanding of consumers and better data-driven decisions. Fractal Analytics delivers insight, innovation and impact through predictive analytics and visual storytelling.

Fractal Analytics was founded in 2000 and has 13 offices around the world serving clients in over 100 countries.

The company has earned recognition by industry analysts and has been named one of the top five ‘Cool Vendors in Analytics’ by research advisor Gartner.

Given the way, analytics as a field have grown in recent years, it is but evident the way many successful analytics service providers have mushroomed recently in India. By our estimate, there are more than 200 boutique Analytics firms, small to medium sized service providers with niche focus on analytics and related fields, operating in India currently. They could be as varied in size as 5 employees to 4000 employee teams. These are also the firms that have seen more growth in analytics space than some of the established players. It is not surprising that analytics is gaining popularity as a career option, and especially among the specialty organizations like the one’s listed below.

Analytics India Magazine carried out a survey in the analytics community to find out the most popular analytics boutiques of 2015 and we have listed them in alphabetical order. We did similar list back in 2013, we leave it to you to compare and enjoy reading.

Founded in 2011, Bridgei2i Analytics is one of the fastest growing analytics companies globally. They have a proven track record of building two of the largest analytics set-ups for HP and GE in the past. With offices in US and India, they offer analytics solutions for marketing, sales, supply chain & risk functions to a diverse portfolio of clients across industry verticals, including a number of Fortune 500 companies.

Furthermore, Bridgei2i has a well-balanced and fun work environment where employees get exposure by solving a variety of business problems across functions, industries and geographies using data science, delivered through a perfect blend of technology and services.

“Our focus is to enable sustainable business impact for customers. With the right balance of services and technology accelerators, our managed analytics approach helps customers operationalize analytics and accelerate business impact. With strong domain expertise, technology focus and the right talent mix, we are well poised to among the top global analytics firms over the next couple of years.’’ – Prithvijit Roy, CEO, BRIDGEi2i Analytics Solutions.

Brillio

Brillio is not your typical analytics services firm.  With a deep focus on helping businesses imagine and realize the possibilities around data and digital, Brillio Analytics blends the disciplines of big data, predictive analytics and mobility to enable businesses to more effectively compete and win in the marketplace.

Brillio brings domain and functional expertise in retail banking, utilities, consumer packaged goods, retail, technology, and the media and entertainment industries as organizations work to utilize emerging technologies to create new and unique customer experiences, achieve cost efficiencies, and gain competitive advantage.

The company has fueled growth and brought new capabilities to customers with the 2014 acquisition of Marketelligent, an advanced analytics solutions provider, as well as recent investments in Albeado – a software company delivering predictive analytics and causal modeling solutions, and Arundo – a predictive maintenance platform company for asset intensive industries. Brillio is headquartered in Silicon Valley and has offices in Bangalore and Trivandrum.

Blueocean Market Intelligence

Blueocean Market Intelligence is a global analytics and insights provider that assists corporations realize a 360-degree view of their customers through data integration and a multi-disciplinary approach, leading to more sound and better business decisions.

We believe the most effective business decisions come from a synthesis of data streams. Using our 360 discovery approach, we ensure the comprehensive use of all available structured and unstructured data sources, enabling us to bring the best to bear against each engagement. Strong decision support is implemented by a harmonious amalgamation of analytics, domain expertise, engineering and visualization skills.

Many leading companies have benefited from our partnership in terms of financial growth, 360 views of their markets and competition and  improved customer acquisition, satisfaction as well as retention.

Blueocean Market Intelligence is part of the Cross-Tab group of companies (Cross-Tab Marketing Services, Informate Mobile Intelligence and Borderless Access Panels) that include more than 1,000 professionals serving the world’s largest companies from offices in the United States, United Kingdom, Singapore, Dubai and India.

Cartesian Consulting

Sandeep Mittal, Founder, Cartesian Consulting

Founded in 2009 by Sandeep Mittal, Cartesian Consulting strongly advocates the power of analytical thinking when it comes to marketing. It’s vision is to be the best blend of marketing and analytical thinking.

Cartesian stands out for its proficiency in the Indian market. It has an exceptionally large and stable client portfolio with more than 40 clients across industries such as retail, eCommerce, finance, hospitality, QSR and others through its offices in Mumbai, Bangalore and Gurgaon. Cartesian is now rapidly taking its practice global with a new team and set up in Singapore to service its clients in the APAC region.

At its core, Cartesian works on customer analytics, marketing analytics, channel analytics, pricing and margin optimization and new advances in digital analytics and mining of unstructured data. The last couple of years have seen Cartesian growing at a very rapid clip and it is now gearing up to build solutions aimed at helping clients adopt Segment of One thinking, as well as democratizing analytics through DIY capability building.

Fractal Analytics

Fractal Analytics is a global analytics firm that helps Fortune 500 companies gain competitive advantage through deep understanding of consumers and making better data driven decisions. They deliver insight, innovation and impact through predictive analytics and visual storytelling.

Fractal Analytics has 900 people in 12 global offices around the world serving clients over 100 countries. The company has earned recognition from industry analysts firms, and has been named one of the top five “Cool Vendors in Analytics” by Gartner. The company is also featured in Business Today’s “Great Place to Work” issue.

Recently, Fractal Analytics acquired an artificial intelligence startup to further strengthen its IP in the area of Customer Genomics®. This is its second acquisition this year. Earlier, Fractal acquired Mobius Innovations, a context awareness platform that gathers information such as geolocation and open social media data and sentiments to strengthen customer intelligence to deliver personalized and contextual offers in real-time on mobile devices.

Gramener

Gramener is a data visualization and analytics company – Founded in 2010 by Ex-IBM and BCG leaders, addresses essential need of making the “consumption” of data – simpler, coherent across organizations and rapid.

Gramex: Data Visualization Platform (patent pending), is a flexible visual product builder with built in data connectors for major data sources (SAP, Teradata, SQL, Hadoop etc.) Gramex platform is the only Automated Analysis, scalable visualisation offering in the market with an ability to process arbitrarily large data real time on multiple servers.

With offices in California, Hyderabad & Bangalore, Gramener today has 70+ clients spread across Pharma, Media, Telecom, BFSI, Retail, Healthcare, Airlines ,Manufacturing, Education and Public Sectors who are served by 100+ Smart Data enthusiasts to solve complex business problems.

They are pioneers of Data Visualization in India and have been ranked 8th in Deloitte 50 Fast Technology Companies of India 2015. Moreover, they have won Lufthansa Pioneering Spirit – 2012, and were recognized as pioneers of tomorrow by Lufthansa, Canaan Partners & TiE-NCR. Additionally, the are recognized as “Top 50 Emerging” Companies in startup category by Nasscom.

Hansa Cequity

Hansa Cequity brings years of customer marketing thought leadership to all their clients. Their team of 400+ strong professionals bring experience in business, customer strategy, marketing, data management, analytics, digital campaigns management and social media to help develop customer-centric marketing strategy and implementation processes.

Over the last 8 years, Hansa Cequity has developed business expertise coupled with a passion for customer focus to provide clients an intelligent platform to reach all their customers individually and optimally.

The company has people with diverse skills. Hansa Cequity’s technology and data specialists have the tools to work with big data, build proprietary data cleansing algorithms, create data transformation methodologies and vertical-specific data models and dashboards. The team of  analysts at Hansa Cequity help derive insights to provide on-demand analytics to help their clients make intelligent decisions.

Exclusive to Hansa Cequity, are its digital marketing and campaign specialists who create and deliver innovative ideas that can be measured. The company has been credited with building some pioneering models in Predictive Analytics and the power of their analytics work has been featured in technology and marketing publications and also presented at the Predictive Analytics World Conference.

MaFoi Analytics

Ma Foi Analytics is a leading-edge innovator in advanced analytics solutions, serving varied clients across India, US, Singapore & the Middle East.

Headquartered in Bangalore and with an office in Chennai, we are powered by a team of 50+ passionate professionals ranging from data scientists and statisticians to marketing strategists and industry experts, who collaborate seamlessly to produce relevant and impactful outcomes for our clients.

We help our clients unlock value everyday with our domain-centric, outcome-oriented approach combined with our cutting-edge data science and proprietary big-data technology to turn business challenges into opportunities.

Ma Foi Analytics has been conceived by renowned entrepreneurs and experienced practitioners across organizations like GE, HSBC, TNS, IBM, Infosys, Capital One, Barclays, Genpact and Oracle. We are driven by a mission to help our clients become intelligent enterprises who not only leverage analytics to make better decisions but who also embrace analytics as a way of life in their organizations.

For our unique vision and pioneering work in the field, Gartner recently named us as a representative vendor in the June 2015 Market Guide for Advanced Analytics Service Providers, featuring 36 of the foremost advanced analytics providers in the world.

Atul Jalan, CEO, Manthan

Manthan is a Bangalore based company operating in the worldwide marketfor analytics software products. Founded in late 2013 by lifelong entrepreneur and CEO Atul Jalan, Manthan’s aim was to transform the role of analytics from decision support to decision making.

Their ability to bring together a unique combination of capabilities – deep understanding of business contexts, statistical sciences and analytics technology – has enabled Manthan to create the most comprehensive range of analytics products for the retail and cpg industry over the span of a decade. Which is recognized worldwide by leading industry analysts and over 200 clients.

The rise of digitalization and consumer-led technologies (social, mobile, analytics, cloud, and internet of things) have created new market opportunities for Manthan in creating disruption and customer value in the cloud computing and big data analytics market. ‘Consumerization of Analytics’is what Manthan believes will revolutionize the analytics market and in the next 3-5 years, Manthan intends to position itself as the global leader in analytics and analytics-driven applications market across industries.

A recent investment round of over $60mn in Manthan underlines their market success and potential of their unique, highly differentiated product innovations.

Tiger Analytics

In the year 2011 in Silicon Valley, California, a strong team of data science researchers and practitioners led by Mahesh Kumar (MIT, IIT-B) and Pradeep Gulipalli (UT Austin, IIT-M) founded Tiger Analytics. Since then it has emerged as a leading provider of data science and data engineering solutions to businesses worldwide and have a grown as a team of highly qualified and experienced professionals working from their offices in India (Chennai) and the US (San Jose, Chicago).

Today, Tiger Analytics is one of those few companies in India who have most of their consulting work in the domain of predictive analytics and machine learning space. They have signed up with many marquee clients across industries.

Their office is full of positivity and they have a well balanced work environment. A heavy focus on learning, an employee-friendly culture, and emphasis on life beyond work, make it a great place to work, which is also reflected in their attrition rates that are well below the industry average.

Data scientist is a rare breed and a well celebrated one. Middle of this year, we took it upon us to identify the top data scientists in the country. The initiative was asked for by many of our avid readers.

I personally believed that, given our inroads in the analytics ecosystem of the country, this initiative would essentially be an easier take. I was wrong, it took us a good 4 months to carve out the below list. The list is a result of our survey and submissions from various organizations, in depth research into the ecosystem and feedback from various leaders and experts.

We believe that a top data scientist should have the following in some combination: – A right pedigree and showcased achievements in applying data science in their respective domain and industry, an Individual contributor, hands-on coder, Hacker of Big Data/ analytics Tools & techniques, good communicator with ability to convince multiple stakeholders through data insights, Patent/ Technical publications author etc.

So, here it is, our cherry-picked, meticulously curated list of top 10 data scientists in India for this year.

Anand S

Anand is the Chief Data Scientist at Gramener.com. He has advised and designed IT systems for organizations such as the Citigroup, Honda, IBM, Target, etc.

Anand and his team explore insights from data and communicates these as visual stories. Anand also builds the Gramener Visualisation Server — Gramener’s flagship product.

Anand has an MBA from IIM Bangalore and a B.Tech from IIT Madras. He has worked at IBM, Lehman Brothers, The Boston Consulting Group and Infosys Consulting.

Chandra Mouli Kotta Kota

Chandra Mouli Kotta Kota is a Analytics Specialist and Data Scientist with a decade long experience after completing his education from IIT-Madras in 2006. He has got rich Marketing Analytics experience which he has attained during his tenure with prestigious companies like McKinsey, Genpact and erstwhile Citigroup Services. With his analytical rigour he has helped various Banking & Financial Services, Media, Telecom and Retail clients across the globe.

Chandra has worked in various domains, like Marketing Analytics (CSI, CLM & Pricing), Risk Analytics and Operation Analytics and has hands-on expertise in Big data and Multivariate analytical techniques including classical & machine learning algorithms, Acquisition, Response, LTV Models, Attrition and MROI Models, Pricing Models and Credit Risk Models (PD Models for Credit Cards, Consumer Loans and Insurance Portfolios). A master of various statistical and analytics platforms, like SAS, R, Python, SPSS Modeller and Hadoop.

He is now the Chief Data Scientist at AnalytixLabs, where he has trained and coached several client teams and 1000+ professionals on data science and advanced analytics along with on job implementation. He is also heading the Research wing of AnalytixLabs where he is constantly engaged into devising and improvising the integrated Date Science and Big data solutions.

Hindol Basu

Hindol, is the head of the Analytics Consulting practice at Tata Industries (Consumer Analytics Division), he brings in about 13 years of analytics consulting experience spanning across multiple industry verticals and markets. Prior to joining the Tata group Hindol used to lead the Asia Pacific analytics consulting practice for FICO, in India. Hindol had also worked with Citibank and TransUnion for Indian and North American markets.

Hindol had led multiple analytics consulting and customer centricity initiatives in the Indian, Asia Pacific and North American market across financial services, travel and telecommunication verticals. In India, he had been the pioneer in establishing credit bureau analytics for the Indian market; he had developed the first set of credit bureau scores for CIBIL. He had also lead analytical consulting assignments for Indian companies like HDFC Standard Life, MakeMyTrip and Airtel for evangelising the adaptation of data driven decision making.

Hindol holds a bachelor in engineering from IIT Kharagpur and an MBA from IIM Bangalore. He is also the co-author of the book titled “Business Analytics – Applications to Consumer Marketing – Sandhya Kuruganti and Hindol Basu” published by McGraw Hill India in March 2015. Hindol had partnered extensively with ISI Calcutta for developing an application focused approach for training final year students.

Joy Mustafi

Joy has more than twelve years of experience in industrial, research and academic world. Did graduation in Statistics (2000) from Ramakrishna Mission Residential College, Narendrapur [CU] and post-graduation in Computer Application (2003) from Regional Computer Center (RCC), Kolkata [KU]. He then got the Junior Research Fellowship award in Computer and Communication Sciences from Indian Statistical Institute (2004).

Joy joined IBM India (2006) as Analytics Consultant in the Business Analytics and Optimization service area. Engaged with IBM – India Software Lab, Watson Business Group (2013) to continue research and development in (Data Mining / Machine Learning / Natural Language Processing).

Joy invented a system, which can take mathematical question as input, then solve it automatically – in short, the machine needs to learn to think. Right at that time his son was 5 years old and just been introduced to word problems in mathematics. Joy used to teach him, observe his thinking and try to de-cypher his perception and subsequent analysis of the problem. That led to his first patent.

Moved to Global Technology Services – IT Operations Analytics as Data Scientist (2014). Also contributed as Subject Matter Expertise in IBM SPSS Statistics, IBM SPSS Modeler (Text Analytics) and IBM Content Analytics.

Joy has been Involved as visiting faculty member in different academic organizations. Having several patents, research publications on Applied Statistics and Computational Linguistics.

Nilesh Karnik

Nilesh is the Chief Data Scientist at Aureus. In this role, he is responsible for development of algorithms and mathematical models that help large organizations with advanced analytics solutions.

Nilesh has a Doctorate in Electrical Engineering from University of Southern California (1998). His Ph.D dissertation made a substantial contribution to the theory of Type-2 Fuzzy Logic Systems and his work is still widely referenced. Nilesh is passionate about analytics and is conversant with a wide range of qualitative and quantitative techniques.

Prior to joining Aureus, Nilesh was with Morgan Stanley Advantage Services where he held a variety of roles with analytics and technology teams. Other organizations he has worked with in the past include Tata Infotech, GE Capital International Services and MindTree Consulting.

Nilesh lives in Mumbai with his wife and two daughters.

Praphul Chandra

Praphul Chandra is a Principal Data Scientist at Hewlett Packard Enterprise. He focuses on the application of machine learning techniques to streaming and sensor data. This data science domain at the intersection of Big Data and Internet of Things poses some unique challenges: some algorithmic (incremental learning) and some computational (In-database vs. In-memory vs. streaming). His broad experience across machine learning, embedded systems and human computer interaction enables him to lead an inter-disciplinary teams of statisticians, computer scientists and domain experts to deliver data science projects.

Besides contributing to projects, Praphul regularly publishes in international conferences advancing the state of art. To build the data science community in India, he continues to deliver tutorials and invited talks in technical conferences & industry forums. He holds 10 patents and has 12 patent pending filings.

Prior to his current role, Praphul was the principal investigator for the Crowd-Cloud project at HP Labs which sought to combine machine learning & crowdsourcing to create scalable solutions. His research interests are Game Theory, Machine Learning, Complex Networks & Public Policy. He holds a B. Tech in Electronics Engineering (IIT-BHU), M.S. in Electrical Engineering (Columbia University, NY), PG Diploma in Public Policy (Univ. of London) and is currently working on his PhD in Mechanism Design (IISc-Bangalore.)

Prashant Warier

Prashant has over 14 years of experience in data science and artificial intelligence. He is an expert in the fields of context aware digital advertising, hyper-personalized marketing, merchandise planning, and network optimization. He has authored a book and several papers and patents in AI and related fields.

He founded Imagna Analytics in 2012, a personalized targeting platform that decodes customer behavior and delivers contextually relevant digital ads and offers in real-time. This AI startup was recently acquired by Fractal Analytics.

In an earlier stint with Fractal, Prashant was responsible for laying the foundation of their Customer Genomics platform. He also was part of the founding team of the SAP Data Science practice.

Rajeev Rastogi

Rajeev Rastogi is the Director of Machine Learning at Amazon where he directs the development of machine learning platforms and applications such as product classification, product recommendations, customer targeting, and deals ranking. Previously, he was the Vice President of Yahoo! Labs in Bangalore where he was responsible for research programs impacting Yahoo!’s web search and online advertising products. He was named a Bell Labs Fellow in 2003 for his contributions to Lucent’s networking products while he was at Bell Labs Research in Murray Hill, New Jersey (1993-2004). He launched and led two premier research labs in India: Bell Labs (2004-2008) and Yahoo! Labs (2008-2012). Rajeev received his B. Tech degree from IIT Bombay, and a PhD degree in Computer Science from the University of Texas, Austin.

Rajeev was named an ACM Fellow in 2012 for his contributions to large-scale data analysis and management. He has published over 100 papers in top-tier international conferences (such as SIGMOD, VLDB, SIGKDD) and 33 papers in international journals (such as TKDE and VLDB Journal). According to Google Scholar, his research publications have over 12,500 citations and an h-index value of 57. Rajeev has also been a prolific inventor with 57 issued US Patents. He is currently a member of the News editorial board of the CACM, and was previously an Associate editor for TKDE. He has served on over 50 program committees of the leading database and data mining conferences, and was a Program Co-chair for the CIKM conference in 2013 and the ICDM conference in 2005.

Satnam Singh

A data geek, researcher, business and team builder, Satnam has a decade of work experience in successfully building data products from concept to production. As a data scientist and team leader in Samsung Research India Bangalore, he built data science team and developed several analytics features in smartphone.

Prior to Samsung, he spent nearly five years at General Motors Research as a senior researcher, with responsibility for conceptualizing and developing data products from scratch for several verticals in GM. After working in CA Technologies for about 1.5 years, Satnam is currently working in a startup as a Principal Data Scientist where he is building a data science product.

Apart from holding a PhD degree in ECE from University of Connecticut, Satnam also holds a Masters in ECE from University of Wyoming and BE from IIT Roorkee. To his credit, he has 7 granted patents, 13 patent applications and 32 journal and conference publications. Satnam is a senior IEEE member and a regular speaker in various Big Data and Data Science conferences.

Besides work, Satnam is an avid cyclist, marathon runner and rock climber. He also participates in several adventure events in a year.

Shailesh Kumar

Dr. Shailesh Kumar is currently Chief Scientist and Co-Founder of a Stealth mode AI startup. He also serves as a faculty of Machine Learning at statistics.com, IIIT-Hyderabad, and ISB-Hyderabad.

Dr. Kumar’s research interests are in fundamental problems in AI and ML especially in natural language understanding, computer vision, and reasoning and bridging the gap between the reality and the dream of AI of building truly intelligent “Thinking Machines”!

Prior to his own startup, Dr. Kumar was part of the Machine Intelligence group at Google, Inc. for five years where he worked on a wide variety of problems including Phrase Discovery, Word Sense Disambiguation, Conversation Modelling, E-Discovery, Knowledge Extraction, Enterprise Search, Machine Learning, and Computer Vision. Dr. Kumar holds 6 US patents with Google, Inc. and 5 publications while at Google.

Prior to Google, Dr. Kumar worked as Sr. Scientist at Yahoo! Labs for about 2 years primarily on Image Search and Query understanding problems. His team received the Yahoo! Superstar award for improving Yahoo! image search quality by more than 10%.

Prior to Yahoo!, Dr. Kumar worked as a Principal Scientist at Fair Isaac Research for about 8 years where he worked on credit card fraud detection, credit modelling, text analytics, computer vision, and retail data mining problems. Dr. Kumar holds 8 US patents with Fair Isaac.

Dr. Kumar received his Masters in Computer Science (Artificial Intelligence and Machine Learning) and PhD in Computer Engineering (Statistical Pattern Recognition) both from University of Texas at Austin, USA. He received his B.Tech in Computer Science and Engineering from IIT-Varanasi in 1995.

Appoints Founder of Imagna, Prashant Warier as Chief Data Scientist

San Mateo, CA., (October 29, 2015)

Fractal Analytics (www.fractal.ai), a global provider of advanced analytics, today announced that it has acquired Imagna Analytics, an artificial intelligence start up founded by Prashant Warier. This is Fractal’s second acquisition this year and will further strengthen Fractal’s IP in the area of Customer Genomics.

Prashant Warier started Imagna Analytics in 2012, a personalized targeting platform that decodes customer behavior and delivers contextually relevant offers in real-time. With this acquisition, Prashant joins Fractal as Chief Data Scientist.

Prashant is an accomplished AI researcher and has authored several papers and patents in AI and related fields. Prashant holds a doctorate in operations research from Georgia Institute of Technology and a bachelor’s degree from IIT Delhi.

“AI is fundamentally changing the way analytics is produced and consumed and I am delighted to have Prashant lead our business and clients in this emerging area,” said Srikanth Velamakanni, co-founder and CEO, Fractal Analytics.

“I am excited to rejoin Fractal to lead the development of next generation AI solutions at scale, a field I am deeply passionate about,” said Prashant Warier.

Earlier this year, Fractal Analytics acquired Mobius Innovations to strengthen the company’s Customer Genomics® solution for hyper-personalized marketing. Customer Genomics integrates enterprise, geo-location and open social media data to deliver personalized and contextual offers in real-time on mobile devices.

Fractal’s products also include TrialRun for systematic marketing experimentation at scale and Concordia® to harmonize disparate data sources.

About Fractal Analytics

Fractal Analytics is a global analytics firm that helps Fortune 500 companies gain competitive advantage through deep understanding of consumers and better data driven decisions. Fractal Analytics delivers insight, innovation and impact through predictive analytics and visual storytelling.

Fractal Analytics was founded in 2000 and has 850 people in 13 offices around the world serving clients in over 100 countries.

The company has earned recognition by industry analysts and has been named one of the top five “Cool Vendors in Analytics” by research advisor Gartner.

About Imagna Analytics

Imagna is a data science company with a focus on personalization solutions. Imagna’s AdPersonix is a digital data coalition and personalized targeting platform. Imagna utilizes proprietary AI techniques to decode customer behavior and target them with relevant ads and offers in real-time.

Though global marketers haven’t yet perfected the art of turning data into insights, many of them are well on their way, according to the CEO of leading global data firm Fractal Analytics.

Founded in 2000 in India, Fractal has grown to become one of the world leaders in customer analytics and business intelligence, serving clients like Procter & Gamble, Kimberley Clark and CapitalOne across global markets where they operate.

In the past year, Fractal has expanded its presence in the Canadian market through a major global partnership with Aimia, which now offers Fractal’s customer behaviour analysis and big data marketing services to its loyalty clients, as well as using Fractal’s tech to provide insight on its Aeroplan program.

On a recent visit to Canada, Fractal’s CEO and co-founder Srikanth Velamakanni sat down with Marketing and talked about how close the multinational brands it works with are to achieving the dream of fully data-driven marketing.

He said when it comes to transaction data — the most abundant and powerful form of customer data that most companies have access to — the world’s top marketers have pretty much “cracked that problem.” The P&Gs of the world have reached a stage of maturity where customer data is properly captured at a large scale, converted into reliable, actionable insights in near-real time, and applied to most major strategic decisions.

Some of Fractal’s most progressive clients won’t commit to any major business move until their chief analytics officer gives the go-ahead. “Traditionally, strategic decision-making has been relegated to gut feeling. Today you’re seeing analytics playing a bigger and bigger role in strategy,” he said. “Should I do this today or not? That never used to be an analytics question.”

Where the frontrunners are focusing now is on learning to apply analytics not just at the executive level, but as a cultural practice across the organization. “They are looking to democratize the data, give people access to actionable insights in real-time — on their phones, on their ipads, whatever,” he said. All the top analytics and automation platforms — Fractal, SAP, SAS, Salesforce — have developed mobile dashboards for marketers and executives to check in on real-time revenue or spend projections whenever they need to make a decision, whether they’re in the boardroom or on the road.

“How do you make everyone more analytically driven rather than relying on intuition or gut feeling? I think that’s the biggest change that companies are trying to make,” Velamakanni said. “In the next three to five years businesses are going to get a lot better at that.”

THERE’S NO SHORTAGE OF VALUABLE DATA

Compared to the U.S. market, Canada has less reliable data about consumers. There are no retail data wholesalers like BlueKai or Datalogix, and organizations like telcos and credit card providers are much further behind in packaging and monetizing their data. The data available can often be outdated.

But, Velamakanni said no matter where you look, businesses never feel like they have enough data.

“With data it’s always a question of relative poverty. You talk to the credit card companies, they’ll say look, we don’t know what the consumer is really buying because we only see how much they paid,” he said. “If you go to the insurance company they say look, the credit card companies have so much more data, but our data is so sparse because we see much fewer transactions. Everybody is poor at some level.”

But, the reality that Fractal sees is everyone already has access to a mountain of data they aren’t leveraging. For some companies that means they haven’t fully exploited their own transaction data; for others it means they haven’t explored publicly accessible data sources like social media, or even census data, which when combined with internal data sources can provide powerful metrics including your share of the customer’s wallet.

“Most companies have not looked at their own data sources very well. They have not combined their email data or their unstructured data sources, they’ve not looked at those data sources at all,” Velamakanni said. “If you bring those types of data sources in I think you will get a lot of value — and then you can start looking at external sources of data.”

TAKE IT A DAY AT A TIME

For companies that are just beginning to take advantage of their internal data, it can be tempting to set out an ambitious plan to comprehensively capture and analyze every contact point they have with the consumer. That’s a mistake, Velamakanni said, and it’s part of the reason many CMOs feel they have more data than they know what to do with.

“We see two kinds of approaches to adopting analytics. One is companies who say, ‘Let’s get the data together, and then once we’re done that let’s start solving some problems,’” he explained. “They are generally much less successful. You can continue to boil the ocean and you’ll never be done with that.

“The other set of people are much smarter. They’re essentially saying, ‘Let me solve a problem — let me get the data together just for that problem, and then let me build the infrastructure over time to enable that and more.’” The incremental approach, he said, leads to more demonstrable, short-term wins, which help build up support at the senior level, and at the same time slowly build towards the comprehensive infrastructure and expertise that businesses are ultimately looking for. It also helps instill the idea that building an analytics program is an ongoing progress — one that will never be finished, as there will always be more data to exploit.

As far as how reliable the data is, Velamakanni said it’s important not to get too caught up in whether every database entry is pristine and infallible. Analytics won’t ever be able to predict whether any one person will buy an iPad or an Android. What it’s intended to do is give you a better than 50/50 chance of finding someone who will.

“The idea is, can you be better than random? Can you be better than before?” he said. “That’s what you’re always looking for — you’re not looking for perfection. This is not an audit, it’s not a forensic investigation. … All information is not 100% accurate, but there is enough signal in that noise to make us better at what we’re doing today.”

A manufacturer of tractors sends alerts to the farmers, who own the tractors manufactured by the company, on when a particular part in the tractor is most likely to fail. For accomplishing this task, the company deploys many different technologies, including Big Data platform, through which information can be extracted from the data related to weather, soil, agriculture patterns, etc.

In banking industry, Big Data is making significant contributions. Many Indian banks have developed robust risk-analysis models for analysing the quality of credit—this has led to record decline in the credit losses that the banks were suffering earlier. There are many such stories of Big Data technology adoption in India. Yet, the truth is that India has barely scratched the surface, when it comes to Big Data.

The Big Talent Crunch

The overall experience of the few enterprises in India that have actually deployed Big Data is positive. A recent Accenture report says that 94% of the Indian organisations, which have made one or more Big Data implementations, are satisfied with the business outcomes of the technology.

The survey also informs that 78% of the enterprises in India see Big Data as something that is necessary for the development of better customer relationships. About 53% of the enterprises surveyed have cited the lack of talent to be one of the key challenges in the deployment of Big Data. Mckinsey estimates that India will need 200,000 data scientists in the coming years.
There is consensus in the Industry on the issue of talent crunch, and the enterprises are trying to address it by re-skilling the existing workforce and by undertaking a different kind of employment model, which is based on selecting professionals with multiple skills.

“The shortage of Big Data professionals is a major issue. We have a lot of young statisticians and computer scientists in the country, but the skills of majority of these young professionals are limited to their own niche areas,” says Bhimasankaram Pochiraju, Clinical Professor and ED, Applied Statistics and Computing, ISB.

The E-Commerce Industry

The e-commerce vertical is an early adopter of Big Data. As large number of consumers have warmed up to online shopping, enterprises such as Flipkart, Snapdeal, etc., are becoming repositories of consumer data. These organisations are also sitting on massive amounts of historical data. They see huge potential in deploying Big Data for achieving meaningful insights from the data that they hold.

Snapdeal has undertaken a Big Data project, which is on verge of completion. The company is also on the lookout for the right kind of talent to take its Big Data initiatives forward. “There are two major challenges that we face. The first is that there aren’t many people who have the experience of working with Big Data technologies. The second is that in order to derive meaningful insights from Big Data, it takes lot of time,” says Ankit Khanna, Senior VP, Product Management, Snapdeal. Khanna is of the view that in the fast moving world of e-commerce, the enterprises need access to necessary information.

“The talent that we are currently having in the organisation is good for problem-solving, but we are looking for more experience. Big Data is a highly innovative area, and it takes time for anyone to grasp the fine details of the technology,” adds Khanna.

As mind boggling amount of data is being created on a regular basis, the companies are also looking for the best possible ways of deriving information out of this data. There is need for professionals who can use Big Data analytics to make effective decisions. They have to be capable of integrating the findings from Big Data with the knowledge derived from other techniques. Bhavish Sood of Gartner says, “A good data scientist will use his knowledge in the context of the business problem.”

Big Data Skills

An ideal Big Data professional would be one who understands the big picture of the business. If he is good in mathematics, statistics and working in the retail industry, but is not well-grounded in the retail business, the constraints of the supply chain operation, then he will not be able to solve the supply chain issues by using Big Data. Data science is the combination of advanced computational science, mathematics, statistics and advance analytics. While there are experts available who are good at one of these disciplines, the industry needs a steady supply of workforce that is equipped with the knowledge of a combination of all these disciplines.

Fractal Analytics provides expertise in the areas of Big Data analytics. The company has about 800 analytics professionals, some of whom are currently deputed at Fortune 500 enterprises. Srikanth Velamakanni, Co-founder & CEO, Fractal Analytics says, “The expertise in Big Data, Analytics and BI is not available in India. Very few people are well-trained in the Big Data stack of technologies.”

Velamakanni says that the Indian companies are not in a position to offer stable and interesting career option to the data scientists. “The enterprises don’t have the ecosystem in place for hiring and grooming talent,” he says.

According to Udo Sglavo, Senior Director, Advanced Analytics, SAS R&D, a person who wants to have a career in Big Data must have three skills—firstly, he must have interest in statistical modelling, maths, engineering; secondly, he should have good experience with at least one programming language such as Java, SAS or any open source language; thirdly he should have the ability to present his findings in a business-like language so that the business leaders in the organisation can easily digest the information. “The third skill is usually the most difficult to develop,” says Sglavo.

“A seasoned analytics professional should have a combination of skills from the fields of statistics, data management including Big Data analytics, and programming. A sound business acumen is also a must,” says Bhimasankaram Pochiraju of ISB.

Khanna of Snapdeal is of the same view. He says, “You have to understand the business from all angles and use Big Data to develop the information that will lead to better outcomes.” The problems that the enterprises face need speedy redressal; if too much time is spent in analysing the data, then the information gleaned may become obsolete by the time it reaches the business heads.
Some enterprises are trying to tackle the resource crunch by having specific training programmes. There are also those, like Snapdeal, which have now started scouting for talent outside the country. “There are lot of Indians working in the Silicon Valley and other technology centres of the world. We are trying to give them an option to relocate back to India,” says Khanna.

The Way Forward

“The evolution of the Big Data delivery models can only happen when we have adequate industry specific Big Data talent. The companies can outsource their Big Data needs, but for that we need to have the Big Data professional services players,” says Rajamani Srinivasan, Vice President & Head of Platform & Technology Business, SAP India.

The data science community can learn from the Indian IT industry the ideas for rapidly developing skilled resources. The idea is to break down the Big Data value chain into “specialised” roles and deliver “industrialised” training processes. “In the area of Big Data, I see the need for three kinds of professionals—the Big Data programmers, the Data Analytics professionals and the domain experts,” says Rajiv Gupta- Head Technology Advantage Practice, BCG India.

It is clear that to address the talent gap in the Big Data space, the HR and the talent management professionals must begin by educating themselves about the technology. They must learn how Big Data will be the strategic driver for bringing competitive advantage to their organisations. The managers and the senior managers must also have the knowledge of the real potential of
Big Data.

By Anuj Kumar – Vice President, Fractal Analytics

In their book “Freakonomics,” Levitt and Dubner note that the average adult in a global sample has one breast and one testicle – an extreme example of being precisely inaccurate. Unfortunately, not all sampling produces insights that are precisely inaccurate and as stark and easy to catch. Hence, they go unnoticed or even worse, they are noticed and serve as the basis for decisions that lead to losses and heartache.

Analytics is a combination of science and art (the left and the right brain) where the science may paint the picture of a one-testicle, single-breasted customer, but the art should ensure that the image that is identified is incorrect and therefore should be modified.

Read more

Experts from Fractal Analytics, Manthan, Market6 and SPI offer thought leadership for consumer goods (CG) manufacturers that are wading through the big data hype to find the best sources for true business opportunities.

Read more

John LaRocca – Practice Head, CPG & Retail represented Fractal Analytics in the 2015 Data & Analytics Solutions Round Table Discussion.

Due to the lack of a large, modern trade presence, agencies often collect data through retail audits instead of scantrack, which is typically used in developed markets. In addition, sales channels are long and unorganized. Due to these manual processes, data collected is not as accurate, does not cover all aspects of sales (such as promotions), and is not granular enough to enable a deep understanding. This leads to fewer data points for modeling analysis and insufficient variables that are typically required to make a model robust.

Coverage

Market Changes

Change the Rules

Scale

List includes the man who (along with Jeff Hammerbacher) coined the term ‘data scientist’, and is currently the Chief Data Scientist in the White House.

Read full article

With the explosion of business analytics (the business analytics software market is poised to reach over $59 billion by 2018) and a widely publicized lack of analytics talent (a McKinsey report estimates that by 2018, about 140,000 to 190,000 big data jobs will be unfilled due to a lack of applicants with expertise and experience), many enterprise organizations, from IT to marketing, are turning to analytics service providers for help.

Leaders seeking to build a competitive advantage, as well as drive their personal career development, are now asking how to evaluate and select the right analytics partner. This decision can’t be made lightly, as the consequences can easily make or break a critical initiative, or a career.

Analytics service providers range from tiny niche boutiques to midsized established consulting firms to massive organizations embedded inside larger consulting or platform providers. Offerings range from single-point solutions or projects to specialty industry expertise to retained teams with broad and deep expertise.

Which provider you select depends on the following factors:

• The complexity of the business problem(s)
• How you are currently addressing the issue
• The nature of the desired solution
• The depth and level of service desired.

These are selected criteria you might want to use to evaluate analytics providers.

Complexity of the Problem

Is your problem well defined or ambiguous? Your answer will determine the solution approach and depth of problem-solving experience you will need when working with your analytics provider.

Considerations when evaluating your analytics provider include the following:

• Degree of complexity – simple or massively complex
• Degree of experience – historical issue or new
• Organizational scope – single department or enterprise-wide
• Timing – transient or recurring issue

A sample question or proof you should ask of candidate providers: “What approach do you use to collaborate on problem definition?”

Current Approach

How extensive are your current analytics and big data capabilities? Your answer will determine the gaps that your analytics partner needs to fill to augment your current approach.

Considerations when evaluating your analytics provider include the following:

• Current analytics sophistication – internal and other provider skills, infrastructure, tools
• Types of data – structured, unstructured, streaming
• Data sources – internal, public, third party
• Industry and problem expertise – broad, deep, relevant

A sample question or proof you should ask of candidate providers: “Can you demonstrate expertise in the range and depth of my internal and external data?”

Solution Requirements

The solution you need will depend of the type of analytics you need to build, how they will be operationalized, and whether you need ongoing support.

Considerations when evaluating your analytics provider include the following:

• Desired analytics sophistication – machine learning, artificial intelligence, automation
• Innovation – proven approach , push the envelope, bleeding edge
• Scale tradeoffs – speed, scope, accuracy, consistency
• Customization – out of the box, tailored, completely customized
• Data security – regulatory compliance, on-site versus off-site protocols, certifications
• Implementation – integration across platforms, data connections, and decision processes
• Operationalizing – business rules, visual storytelling, optimization flows

A sample question or proof you should ask of candidate providers: “Can you demonstrate expertise in building and operationalizing machine learning?”

Provider Requirements

Once you have defined the problems you want solved and identified gaps in your current approach and the parameters of your desired solution, you are prepared to define the type of partnership that will best fit your needs. Are you seeking a short-term project solution or a long-term partnership that will scale and position you and your enterprise-wide initiatives for highly visible success?

Considerations when evaluating your analytics provider include the following:

• Problem-solving capabilities – data management, insight development, predictive modeling, machine learning, automation, visual storytelling, systems integration
• Industry experience – same or relevant, cross-pollinated ideas
• Team structure – analysts, consultants, data scientists, big data engineers
• Engagement approach – strategic partnership, project, ongoing program
• Process – solution requirements, milestone management, problem resolution, satisfaction measurement
• Skills and training – technical skills, advanced analytics, consulting skills
• Culture – flexible, reliable, supportive, client service orientation
• Results – impact, insight, and innovation delivered

A sample question or proof you should ask of candidate providers: “What can you tell me about your employee engagement and how you attract and retain the best talent?”

And of course, you will want references and a check for ethical business practices. Some companies seek business at any cost, and the sooner you know you can trust your provider, the better. Test your provider with hard questions that challenge its integrity and run for the hills if the provider outright lies or promises the moon. This wrong choice can cost you and your company dearly.

Careen Foster is the Chief Marketing Officer at Fractal Analytics. Throughout her career, she has been on the forefront of Big Data analytics space as the head of product management and partner marketing at FICO’s  Scores division (Fair Isaac), and product management and operations for risk management solutions at TRW (now Experian).

Careen holds an MBA from the University of California, Irvine, Paul Merage School of Business and a Bachelor’s in Psychology, from the University of California, Irvine.

Who is leading the charge for Big Data in Singapore and India? We’ve compiled a list of the top ten you should keep on your radar

Big Data analytics has become one of the most vital tools for companies in the Internet, e-commerce, retail, banking, and insurance spaces that helps them target the right audience with customised and personalised solutions. Companies using analytics tools have seen significant improvements in their customer conversion, retention and sales.

A large chunk of Big Data companies born in Asia have scripted success stories outside their home markets. However, Asia is now becoming a crucial market for them, as 60 per cent of the total world population reside in the region and who are increasingly going online.

So here are the top ten names in the Asian Big Data analytics space:

Aureus Analytics: Singapore-headquartered Big Data analytics startup Aureus Analytics offers an integrated Big Data analytics platform for enterprises to eliminate the need to have multiple tools for understanding the customers, risks to business and operational inefficiencies. Its platform ASAP (Aureus Statistical and Analytical Platform) is designed to be an integrated analytics platform that can work across various types of data stores and structures — structured and unstructured; internal and external; and big and small. The firm recently raised around US$850,000) in funding via online deal-making platform LetsVenture.

Mu Sigma: Founded in 2004, Bangalore-based Mu Sigma helps companies institutionalise data-driven decision making and harness Big Data. It solves business problems in key areas such as marketing, risk and supply chain. It provides enterprise clients with an ecosystem of technology platforms, processes and people. The firm claims that it has more than 3,500 decision science professionals and 75 Fortune 500 clients.

To date, Mu Sigma has raised more than US$200 million in funding from the likes of Sequoia Capital and General Atlantic. It is currently in talks with investors to raise over US$200 million in fresh funding.

Mu Sigma has offices across the globe.

Fractal Analytics: Incorporated in 2000, Fractal Analytics offers enterprises (consumer companies, retailers and financial institutions) the tools to understand, predict and influence consumer behaviour and improve marketing, pricing, supply chain, risk and claims management.

Its flagship product ‘Customer Genomics’ helps marketers learn complex customer behaviour at an individual level. Customer Genomics learns from every transaction and customer interaction including from social media to help marketers build a complete view of individual customers. Its solutions can also be used to forecast business performance.

Fractal is backed by private equity investor TA Associates, which has invested US$25 million in the firm in 2013. Last year, Fractal received funding from Toronto Stock Exchange-listed company Aimia.

Fractal has clients in over 100 countries and currently employs 800 people. Headquartered in the US, Fractal has 13 offices across cities such as London, Mumbai, New Delhi, Singapore and Dubai.

Manthan Software Services: Bangalore-based Manthan provides Business Intelligence and Big Data analytics solutions to enterprises. Incorporated in 2004, Manthan combines advanced predictive analytics, actionable insights and customer knowledge to help retailers identify and drive growth opportunities.

The firm is backed by Fidelity Growth Partners India and Norwest Venture Partners. Headquartered in Bangalore, the firm also has offices in the US, the UK, the Philippines, Singapore and Brazil.

Crayon Data: Chennai and Singapore-based Big Data analytics startup Crayon Data was founded in 2012. Unlike the conventional people-led model of analytics, Crayon builds tools that deliver real business solutions by bringing together enterprise, public, external internet and social data to a single platform.

Crayon Data’s flagship product ‘Simpler Choices’ brings the power of Big Data and analytics to enterprises that enable clients increase their sales conversions and improve returns from existing accounts. The firm’s key focus verticals are hospitality, finance, retail and technology. To date, the firm has raised US$5.5 million in angel funding from Jungle Ventures and Spring Seed Capital.

Heckyl Technologies: Started in 2010, Heckyl provides a real-time news and data analytic platform to brokerage firms, short-term traders, investors and fund managers. Its integrated solutions provide information, visuals, heat maps of sentiments and market data to help traders find the trading opportunity in the market.

Unlike the traditional solutions, Heckyl brings in a social media aspect to its platform. It is also planning to launch a Big Data mining platform for financial institutions and the hedge funds. In December 2013, Heckyl secured $3.5 million in a Series B round led by IDG Ventures and Seedfund Advisors. The company has offices in Mumbai and the UK.

Spire Technologies: Bangalore-based Spire Technologies offers a contextual search engine for enterprises to manage their talent requirements. Founded in 2008, Spire offers various solutions for supply chain management, customer relationship management, fraud intelligence, talent growth management and predictive talent intelligence. Spire also offers fraud detection tools by pulling in data from documents, e-mails, SMSes and reviews. In December 2013, the firm raised US$8 million in Series A funding from an unnamed investor.

Altizon Systems: Altizon is a Big Data-based IoT startup focusing on the industrial Internet. It offers a platform to manufacturers to build intelligent connected devices and manage them from the cloud. The startup has a set of sensor data appliances and software development kits (SDKs) that drive data from sensors in industrial equipment to its Datonis platform, which provides a device and performs large-scale data ingestion and aggregation.

In September last year, Altizon nabbed an undisclosed amount in seed funding led by The Hive India, a Big Data-focused early-stage fund in India, with participation from Infuse Ventures and Persistent Ventures.

TookiTaki: Tookitaki is a marketing intelligence startup focused on audience discovery and prediction. It is building a SaaS platform for enterprises and media agencies to provide actionable insights on audience behaviour. Its analytics are based on predictive models which combines public digital data with ROI-focused feedback loops.

TookiTaki has offices in Singapore and India. In January this year, the company raised US$1 million in seed funding from Jungle Ventures, Rebright Partners, and Blume Ventures.

Corporate360: Set up in 2012, Corporate360 offers IT sales intelligence data services to enterprises. It offers four products: Tech SalesCloud (data solution designed for technology industry offering company profile, tech install intelligence, contact data and  predictive analytics), DataFactory (B2B marketing data software delivered as Data-as-a-Service model, offering company profile and contact intelligence), Peep (social media visualisation application that aggregates, normalise and presents all social media profiles/data of contacts in a unified window), and SataStudio (data service offering curated data services, data mining, predictive analytics and social sentiments).

Corporate360 has offices in California, Liverpool, Manila, Singapore and India. Recently, the company received US$200,000 in angel funding.

Enterprises have lots of solid choices for big data predictive analytics.

That’s the key takeaway from Forrester’s just released Wave for Big Data Predictive Analytics Solutions for the second quarter of 2015.

That being said, the products Forrester analysts Mike Gualtieri and Rowan Curran evaluated are quite different.

Data scientists are more likely to appreciated some, while business analysts will like others. Some were built for the cloud, others weren’t.

They all can be used to prepare data sets, develop models using both statistical and machine learning algorithms, deploy and manage predictive analytics lifecycles, and tools for data scientists, business analysts and application developers.

General Purpose

It’s important to note that there are plenty of strong predictive analytics solution providers that weren’t included in this Wave, and it’s not because their offerings aren’t any good.

Instead Forrester focused specifically on “general purpose” solutions rather than those geared toward more specific purposes like customer analytics, cross-selling, smarter logistics, e-commerce and so on. BloomReach, Qubit, Certona, Apigee and FusionOps, among others, are examples of vendors in the aforementioned categories.

The authors also noted that open source software community is driving predictive analytics into the mainstream. Developers have an abundant selection of API’s within reach that they can leverage via popular programming languages like Java, Python and Scala to prepare data and predictive models.

Not only that but, according to report, many BI platforms also offer “some predictive analytics capabilities.” Information Builders, MicroStrategy and Tibco, for example, integrate with R easily.

The “open source nature” of BI solutions like Birt, OpenText and Tibco Jaspersoft make R integration simpler.

Fractal Analytics, Opera Solutions, Teradata’s Think Big and Beyond The Art and the like also provide worthwhile solutions and were singled out as alternatives to buying software. The authors also noted that larger consulting companies like Accenture, Deloitte, Infosys and Virtuasa all have predictive analytics and/or big data practices.

In total, Forrester looked at 13 vendors: Alpine Data Labs, Alteryx, Agnoss, Dell, FICO, IBM, KNIME, Microsoft, Oracle, Predixion Software, RapidMiner, SAP and SAS.

Forrester’s selection criteria in the most general sense rates solution providers according to their Current Offering (components include: architecture, security, data, analysis, model management, usability and tooling, business applications) and Strategy (components include acquisition and pricing, ability to execute, implementation support, solution road map, and go-to-market growth rate.) Each main category carries 50 percent weight.

Leading the Wave

IBM, SAS and SAP — three tried and trusted providers — lead this Forrester Wave:.

IBM achieved perfect scores in the seven of the twelve criteria: Data, Usability and Tooling, Model Management, Ability to Execute, Implementation Support, Solution Road Map and Go-to Market Growth Rate. “With customers deriving insights from data sets with scores of thousands of features, IBM’s predictive analytics has the power to take on truly big data and emerge with critical insights,” note the report’s authors. Where does IBM fall short? Mostly in the Acquisition and Pricing category.

SAS is the granddaddy of predictive analytics and, like IBM, it achieved a perfect score many times over. It’s interesting to note that it scored highest among all vendors in Analysis. It was weighed down, however, by its strategy in areas like Go-to-Market Growth Rate and Acquisition and Pricing. This may not be as a big problem by next year, at least if Gartner was right in its most recent MQ on BI and Analytics Platforms Leaders, where it noted that SAS was aware of the drawback and was addressing the issue.

“SAP’s relentless investment in analytics pays off,” Forrester notes in its report. And as we’ve reiterated many times, the vendor’s predictive offerings include some snazzy differentiating features like analytics tools that you don’t have to be a data scientist to use, a visual tool that lets users analyze several databases at once, and for SAP Hana customers SAP’s Predictive Analytics Library (PAL) to analyze big data.

The Strong Performers

Not only does RapidMiner’s predictive analytics platform include more than 1,500 methods across all stages of the predictive analytics life cycle, but with a single click they can also be integrated into the cloud. There’s also a nifty “wisdom of the crowds” feature that Forrester singles out; it helps users sidestep mistakes made, by others, in the past and get to insights quicker. What’s the downside? Implementation support and security.

Auhtored by: Kishore Bharatula

Americans just love driving. They clocked 35,795,000,000,000 miles in 2014 and have far outstripped every other nation in driving mileage.

Executive Summary

Opinion: American driving behavior has altered over the last decade, with per capita miles driven trending downward since 2007. Kishore Bharatula of Fractal Analytics notes that the change has resulted in a reduction in accidents and associated claim expenses, but wonders why it has not impacted insurer premiums.

Insurance companies historically have placed a great weight on mileage driven along with other rating factors based on driver and vehicle characteristics to decide on premiums. But the behavior of American drivers has been changing over the last decade, which will likely have an impact on insurance accidents, claims and premiums.

The price of gas is the single largest variable expense in driving. Crude oil prices have been trending negatively over the last few months and thus should encourage Americans to drive even more. More miles driven would mean higher risk of an accident and therefore a claim. However, from the graph below, we can clearly see that crude oil prices didn’t have a significant impact on miles driven by Americans.

A closer look at the above chart indicates two significant trends:

·         Until 2007, per capita mileage has shown an increasing trend in spite of increasing crude oil prices.

·         Post 2007, per capita miles traveled have been trending down even with a drop in crude prices.

A variety of factors have influenced the downward trend in mileage over the last few years.

Change in Demographics: Per capita miles consistently increased since World War II to the start of the 21st century. Baby boomers (born between 1946 and 1964) took advantage of low gas prices, improved transportation infrastructure and increased speed limits to drive a lot more. However, with most of this generation retiring, their driving habits have also come down.

The millennial generation does not share the same desire for driving, which has contributed to the decline in per capita driving. The decrease is also reflected by the drop in cars per household, which has slipped from 2.1 to 1.9 in the last decade.

Unemployment: Working members of the population drive more than nonworkers. The declining labor force—which went from 67 percent during the 1980s to about 63 percent in the last decade—has also contributed to the drop in mileage.

Increase in usage of public transportation: Americans have increasingly taken more trips via public transportation. There has been a 10 percent increase in usage of public transportation over the last 10 years along with an increase in usage of bikes and travel by foot.

People have also started living in walkable neighborhoods, thus making it easier to take a bike or reach the office by foot, further reducing their dependence of cars.

Technological Developments: Internet-enabled mobile technologies have allowed millennials to avoid regular travels to the office and conferences.

Companies have placed a lot of emphasis on work-life balance and have permitted employees to regularly work from home, use video conferencing, etc. This has led to a gradual decrease in mileage over time.

This change in driving mileage has a wide set of repercussions on auto insurance as well. When people drive less, they spend less time on the road, implying that the risk of getting into accidents is also less. As can be seen from the chart below, the number of accidents across all categories has consistently trended downward over the last few years.

Other factors, apart from mileage, have also contributed to the decrease in accidents overall. Technological developments have aided the safety of cars and drivers. A slew of inventions to prevent collisions and thefts have meant that not just the rate of accidents but also the severity of resultant claims have been consistently coming down.

All this would mean that insurance companies have been paying for fewer claims and at less cost per claim. Further, the combination of reduced traffic congestion due to fewer cars on the road and ever-improving road infrastructure has been instrumental in keeping accidents down.

What has been the implication of the behavioral changes on insurance premiums?

The change in driving mileage and reduction in accidents should mean that the average premiums paid are trending down. A look at the average premiums charged over the last few years, however, indicates the numbers have actually increased rather than decreased.

As seen from the analysis, the behavioral changes that altered driving patterns and decreased mileage seem to be long-term and to have resulted in reduced claim expenses. However, insurance companies are taking advantage of these favorable conditions and increasing their returns without passing the benefits on to customers.

There is a strong case for correction in premiums charged sooner rather than later. Drivers are keeping their fingers crossed and waiting for the burden on their pocketbooks to lessen.

Data Sources for this article include:

·         The U.S. Federal Highway Administration: www.fhwa.dot.gov

·         The U.S. Census Bureau: www.census.gov

·         The National Highway Traffic Safety Administration: www-fars.nhtsa.dot.gov/

·         The Insurance Information Institute: www.iii.org/fact-statistic/auto-insurance

Exponential growth in data and information at hand is forcing organizations to rethink their business models

Exponential growth in data and information at hand is forcing organizations to rethink their business models. With the rise of digital channels (both as sales channel and marketing channel) the business environment is becoming more complicated and challenging, and the competition is intensifying. Firms are responding by setting up dedicated analytics (or information science or insights) divisions, or repositioning existing teams, to enable a culture of data-driven decision making.

There has been significant momentum in recent years with rampant hiring of analytics talent. However, business need to assess if the investment has paid off by achieving the intended objective, or showing signs of positive ROI. Here are SIX steps for businesses in their journey to maximize analytics ROI:

1. Set up effective communication and active partnership between analytics and business units
Analytics has the potential to be a game changer for business in driving competitive advantage, not just an enabling/supporting function. Active partnership between the business units and analytics, and collective decision making can better harness the potential of analytics. Progressive organizations are increasingly leaning towards data-driven as opposed to data-enabled business KPIs.

2. Align analytics success with organizational KPIs
Ensure that the targets for analytics divisions are aligned with the broader organizational KPIs and reflect quantifiable influence on revenues/costs on the rest of the organization. Even a virtual P&L for the analytics division, with incentives linked to the impact to overall business, empowers the analytics team, and also promotes the culture of data-driven decision making.

3. Effectively operationalize analytics for repeated consumption
Transition from having a series of ad hoc analyses supporting business needs to a tightly integrated operations with day-to-day decision making. It is critical to realize the long-term benefits and the long shelf-life of analytics outputs to maximize the ROI. Successful organizations are building solutions to embed analytics as part of decision systems to drive real-time decision making.

4. Define a clear approach to vendor selection and partnership
Define a 3 to 5 year analytics roadmap, assessing internal capabilities and identifying the gaps that business is looking to fill. Identify the key strategic requirements around data management, exploratory analytics, modelling expertise, operationalizing solutions, product expertise, short-term resourcing, etc. The priorities change as the analytics maturity grows within the organization but the decision around choice of vendors, timing of engagement, duration of partnership and nature of RFP will have to be closely aligned with the roadmap.

5. Balance the top-down and bottom-up approach to analytics
While the growing influence of analytics warrants a top-down pan-organizational view of business priorities, a fine balance of “top down” and “bottom up” can strengthen stakeholder engagement at multiple levels and expedite the journey of institutionalizing analytics. After all, analytics has existed in some shape or form in the past, possibly de-centralized and distributed across multiple divisions. Building on past achievements and actively engaging the respective teams can build quick credibility and promote partnership.

6. Make the organizational structure agile and responsive
Consumers are expecting near real-time information; competitors are constantly rethinking their business models; an agile and responsive organizational structure can promote consumption of analytics within the organization. Further, as the analytics organization and/or strategy evolves, the rest of the organization need to show the agility to quickly adopt to the evolved processes.

The global analytics business process services (BPS) market has witnessed strong year-on-year growth of more than 35 percent and is poised for similar growth in the future. As a result, analytics BPS is among the fastest growing markets in the BPS industry.

Although North America dominates buyer geography, emerging geographies of Europe, Middle East and Africa (EMEA) and Asia Pacific (APAC) regions will be key drivers of the growth of the analytics BPS market in the coming year.

The market is composed of two key segments of providers: traditional BPS providers that have augmented services to include a combination of industry-agnostic as well as industry-oriented analytics services for a diversified client base, and specialists that entered the market with proprietary solutions and typically target specific buyer industries. Although BPS providers dominate market share, specialists are growing at a faster pace.

These results and other findings are explored in a recently published Everest Group report: “Analytics Business Process Services (BPS) – Service Provider Landscape with PEAK Matrix™ Assessment 2015.”

*** Download a complimentary 12-page preview of the report *** (Registration required.) This preview summarizes report methodology, contents and key findings and offers additional analytics-focused resources.

The full report provides insights into the global analytics BPS market, assesses the analytics capabilities of service providers and evaluates their positioning on the Everest Group PEAK Matrix, a proprietary framework for assessing the relative market success and overall capability of service providers based on Performance, Experiences, Ability and Knowledge.

Service Providers Positioned on PEAK Matrix

•  Among the 19 global analytics service providers examined by Everest Group, Accenture, Genpact, IBM, Mu Sigma and TCS were classified as Market Leaders who excel above other companies in the marketplace.
•  In the next category, Major Contenders included Absolutdata, Dunhumby, EXL Services, Fractal Analytics, HP, Infosys, Opera Solutions, Tech Mahindra, Wipro and WNS.
•  Emerging Players comprise CGI, Chainalytics, Minacs and Neeyamo.
•  Since this is the inaugural publication of the Analytics BPS PEAK Matrix, no Star Performers were named, as this category recognizes upward movement on the PEAK Matrix from year to year.

*** Download a complimentary four-page preview of the PEAK Matrix report ***
This preview features the PEAK Matrix diagram and a table summarizing the services assessment of the 19 companies profiled in the full report.

“Looking ahead, we anticipate three disruptive forces in the analytics BPS market,” said Rajesh Ranjan, partner of Everest Group. “First, service providers will ‘productize’ basic reporting and descriptive analytic solutions and then focus their investments on predictive and prescriptive capabilities. Second, we’ll see machine learning and automation applied at increasing rates. And, third, as analytics BPS impacts business outcomes, we’ll see a rise in pricing that have outcome-based components in this market.”

*** Download Publication-Quality Graphics ***
High-resolution graphics illustrating key takeaways from these reports can be included in news coverage, with attribution to Everest Group. Graphics include:
● Analytics BPS: Explosive Growth
● Analytics BPS growth trajectory: EMEA and Asia Pacific
● Analytics BPS providers: BPO providers dominate market share, specialists growing at a faster pace

***Additional Resources***
Report: Analytics Business Process Services – Deciphering the Analytics Code

About Everest Group
Everest Group, an advisor to business leaders on the next generation of global services, has a worldwide reputation for helping Global 1000 firms improve performance by optimizing back- and middle-office business services. Through practical consulting, original research and industry resource services, Everest Group helps clients maximize value from delivery strategies, talent and sourcing models, technologies and management approaches. Visit http://www.everestgrp.com and research.everestgrp.com.

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T.A. Associates-backed Fractal Analytics, among the first movers in the Indian analytics space, is on an aggressive growth path. Following the acquisition of Mobius Innovations, it is betting on more IP-driven acquisitions, with a focus to touch revenues of US $100 million in two years

In the year 2000, when Pranay Agarwal, Nirmal Palaparthi, Srikanth Velamakanni and two other co-founders approached their first client with an analytics solution to help companies take quicker decisions, they were asked the one question that led them to rise to the challenge of building a successful company. “Over 100 PhDs are doing the same thing you do, how can you be any better when you have a team of just six employees?” And, their response was reflected in the way they carefully built every process in the company, which expects to record US $100 million in revenues by FY17.

Every day, companies take a lot of decisions around how to get closer to customers and how to scale their business further. With every decision, they can either drive it through the lens of data or they can rely on experiential or gut feeling. Our pitch to clients was, if every decision could be made better by a few basis points with the help of data, it can have a significant impact on their profitability.

“We started Fractal Analytics at a time when the industry was at a very nascent stage. We saw every company churn a lot of data and we realised that math would be used to help them make better decisions. That was our premise,” states Velamakanni. Thus, the IIM Ahmedabad graduates put their heads together and built a model that primarily focused on problem solving. “Every day, companies take a lot of decisions around how to get closer to customers and how to scale their business further. With every decision, they can either drive it through the lens of data or they can rely on experiential or gut feeling. Our pitch to clients was, if every decision could be made better by a few basis points with the help of data, it can have a significant impact on their profitability,” he explains. That, according to them, was their competitive advantage.

Business model evolution

With the analytics industry slated to be one of the fastest growing segments in the IT enabled services space (according to a 2012 Gartner Report), the founding team realised that their business model needs to evolve from a traditional consulting business to one that is inexplicably linked with technology. “That’s when we developed solutions such as Customer Genomics and Concordia, which essentially deliver a non-linear pricing advantage,” adds Velamakanni. While Customer Genomics acts as a personalized marketing platform, which addresses various aspects such as customer acquisition, loyalty and attrition, Concordia leverages a scalable data harmonization platform to solve complex, high volume data.

Typically, Fractal Analytics’ strength lies in delivering analytics solutions for consumer-facing industries such as packaged goods, finance and insurance, for the simple reason that it began operations with a focus on these sectors. However, Velamakanni indicates that the company is gradually penetrating into the healthcare and lifestyle space as well. “Currently, our focus markets are the U.S., the U.K., Europe and Australia. And, our client acquisition strategy is driven towards long-term gains. We don’t want to be everything to everybody,” he points out.

Setting a steady pace of growth

2013 was a year of highs for the company. First one being the US $25 million fund-raise from Boston headquartered private equity firm T.A. Associates.  “The funds left us with sufficient cushion to setup operations in Bengaluru and build it, to invest in improving our client solutions and to invest in acquisitions,” shares Velamakanni. In January 2015, Fractal Analytics acquired Singapore-based Mobius Innovations to strengthen its personalised marketing solutions business. Mobius, which was founded by Nirmal Palaparthi, an ex-co-founder of Fractal Analytics, offers mobile-based solutions for brands to deliver personalised content and promotions to their customers. “Mobius filled a gap that Customer Genomics was trying to offer. We are also exploring other acquisitions, either products with IP related to analytics products or companies in the analytics space itself which can help us scale faster,” he indicates.

A second milestone for the company was in the official launch of the People Principle, a culture which places trust and freedom among employees to function as they best perceive suitable. The Principle includes aspects such as the freedom for employees to choose the role, projects or manager they would like to pursue and work with, self-regulation in issues pertaining to work timings, vacation, expense claims, dress code and more. “We strongly believe that if the employees are happy, they will take care of our clients. That’s why we place extreme trust in the people we hire and give them the freedom to pursue their interests so that even when they are tired of pursing a role, instead of quitting, they can shift to a role they are interested in,” explains Velamakanni.

While the Principle has been in place since founding days, given the rapid growth spurts (in employee base) over the years, the founding team put it on paper only in 2013. In fact, from a client satisfaction standpoint, the company recorded an annual NPS (Net Promoter Score) of 49, which is said to be among the highest in the world, only next to companies such as Netflix and Apple.

Another interesting aspect about the company’s people policy is its hiring strategy. While its Principles ensure that it doesn’t dilute its talent base, it trades smartness over experience when recruiting talent. “In an industry which is moving so fast, knowledge and learnability is more important than investing time in identifying people with the right skills. Whatever skills one may have, they become obsolete very quickly. So, we want people who are willing to learn. We prefer to hire people who want to figure things out by themselves,” opines Velamakanni. In fact, by June, the company sees its employee base touching 1,000 with close to 120 recruits being freshers hired through university recruitments.

Growing on partnerships

In August 2014, Fractal Analytics entered into a partnership with Aimai, the world’s largest loyalty management firm. “Initially, Aimai wanted to work with suppliers but they realised that this will be a much bigger partnership. Thus, we will be combining their customer data repository and our IP expertise to build customer analytics focused intellectual property,” states Velamakanni.

For him and his team, the way forward is crystal clear. “We want to touch revenues of US $100 million in two years. For this, we want to acquire clients with whom we can sign US $10 million to US $30 million deals. Of course, acquisitions will also be a key focus area for us to help the business scale quicker,” he shares, on a parting note.

WHAT NEXT?

With the acquisition of Singapore-based Mobius Innovations, improve analytics solutions offerings for clients

Explore further acquisitions (either IP or analytics services) to scale the business further

Be an elephant catcher: sign US $10 million to US $30 million deals with clients to touch revenues of US $100 million in two years

Build customer analytics focused intellectual property in partnership with Aimai

HR PRACTICES AT FRACTAL

Hiring

In a fast moving industry, Fractal Analytics places a higher importance on knowledge and learnability than on skills. Hence, it focuses on hiring employees who are willing to learn, and perceives hiring employees with the required skills as an added advantage.

By June 2015, the company sees its employee base touching 1,000 with close to 120 recruits being freshers hired through university recruitments.

Retention

People Principles, an internal culture developed by the company, places trust and freedom among employees to function as they best perceive suitable. Some of them include freedom for employees to choose the role, projects or manager they would like to pursue and work with, self-regulation in issues pertaining to work timings, vacation, expense claims, dress code (wherein the company states, dress as though you’re meeting a client) and more. The founders believe that if the employees are happy, they will take care of their clients. Hence, even when an employee is tired of a particular role, he/she can shift into a team or project that piques their interest, hence ensuring higher retention rates.

While the Principle has been in place since founding days, given the rapid growth spurts (in employee base) over the years, the founding team put it on paper only in 2013. From a client satisfaction standpoint, the company recorded an annual NPS (Net Promoter Score) of 49, which is said to be among the highest in the world, only next to companies such as Netflix and Apple.

Authored by: Amit Gupta

Exponential growth in data and information at hand is forcing organizations to rethink their business models. With the rise of digital channels (both as sales channel and marketing channel) the business environment is becoming more complicated and challenging, and the competition is intensifying. Firms are responding by setting up dedicated analytics (or information science or insights) divisions, or repositioning existing teams, to enable a culture of data-driven decision making.

There has been significant momentum in recent years with rampant hiring of analytics talent. However, business need to assess if the investment has paid off by achieving the intended objective, or showing signs of positive ROI. Here are SIX steps for businesses in their journey to maximize analytics ROI:

1. Set up effective communication and active partnership between analytics and business units

Analytics has the potential to be a game changer for business in driving competitive advantage, not just an enabling/supporting function. Active partnership between the business units and analytics, and collective decision making can better harness the potential of analytics. Progressive organizations are increasingly leaning towards data-driven as opposed to data-enabled business KPIs.

2. Align analytics success with organizational KPIs

Ensure that the targets for analytics divisions are aligned with the broader organizational KPIs and reflect quantifiable influence on revenues/costs on the rest of the organization. Even a virtual P&L for the analytics division, with incentives linked to the impact to overall business, empowers the analytics team, and also promotes the culture of data-driven decision making.

3. Effectively operationalize analytics for repeated consumption

Transition from having a series of ad hoc analyses supporting business needs to a tightly integrated operations with day-to-day decision making. It is critical to realize the long-term benefits and the long shelf-life of analytics outputs to maximize the ROI. Successful organizations are building solutions to embed analytics as part of decision systems to drive real-time decision making.

4. Define a clear approach to vendor selection and partnership

Define a 3 to 5 year analytics roadmap, assessing internal capabilities and identifying the gaps that business is looking to fill. Identify the key strategic requirements around data management, exploratory analytics, modelling expertise, operationalizing solutions, product expertise, short-term resourcing, etc. The priorities change as the analytics maturity grows within the organization but the decision around choice of vendors, timing of engagement, duration of partnership and nature of RFP will have to be closely aligned with the roadmap.

5. Balance the top-down and bottom-up approach to analytics

While the growing influence of analytics warrants a top-down pan-organizational view of business priorities, a fine balance of “top down” and “bottom up” can strengthen stakeholder engagement at multiple levels and expedite the journey of institutionalizing analytics. After all, analytics has existed in some shape or form in the past, possibly de-centralized and distributed across multiple divisions. Building on past achievements and actively engaging the respective teams can build quick credibility and promote partnership.

6. Make the organizational structure agile and responsive

Consumers are expecting near real-time information; competitors are constantly rethinking their business models; an agile and responsive organizational structure can promote consumption of analytics within the organization. Further, as the analytics organization and/or strategy evolves, the rest of the organization need to show the agility to quickly adopt to the evolved processes.

More Stories By Amit Gupta

Amit Gupta is Engagement Manager – CPG, Retail and Tech for Fractal Analytics. Gupta came to Fractal in 2005 with more than nine years of experience helping businesses optimize marketing investments, optimize portfolio pricing and trade investments, develop communication & positioning strategies, and understand consumers better. He specializes in developing analytics frameworks for solving business problems, and leading them through execution to implementation. He is responsible for consulting, design and delivery of large analytics engagements with global clients across industries. Gupta earned an MBA from Indian Institute of Management, Ahmedabad (IIMA) and a Bachelor of Architecture from NIT, Jaipur. He is passionate about designing and developing analytics frameworks and approach to solve business problems.

Authored By: Amit Gupta

At Fractal Analytics, we believe in chasing three ‘I’s in what we do for our clients, or even for any internal development, to be able to deliver value. These ‘I’s are critical for our success (as we perceive it), and forms the core of our thinking.

These three ‘I’s are – (i) Insight (ii) Impact (iii)Innovation

• Insight refers to that one big learning or key takeaway from any piece of analysis that business can adopt for better decision making, removing inefficiencies from business processes, and to connect better with its customers.
• Impact is the change that the business will witness as the result of the analysis.
• Innovation is the new creative thinking that is introduced in conducting that piece of analysis or delivering the same to the business.

For these three ‘I’s to meet the intended objective, each need an additional ‘I’ in support for their meaningful existence – I for an I!

1. Interpretation
Need for the accurate Interpretation for the Insight
An insight can be as detrimental as beneficial, if not interpreted in the way it is supposed to be. Any insight delivered as an outcome of an analysis needs to be accompanied with relevant conceptual clarity and contextual familiarity (commonly referred to as CCCF) to be able to provide the right interpretation.

The person who is receiving the insight may not have the much needed CCCF to the extent that the person who is delivering the insight has, and may be a victim of wrong interpretation.

Analytics teams usually spend up to 40% of their time in presenting the insights to the consumer of analytics, in order to make it simple, straightforward and precise to help mitigate the risk of it being wrongly interpreted.

The key for presenting the insight for its right interpretation is:

1. Keep it simple
2. Be straightforward and direct
3. Identify the precise implication and mention that

2. Implementation
Need to drive Implementation to realize the Impact
Analysts must have an implementation plan in place for the business owner to realize the full impact, as discovered and predicted as the outcome of a study.

An implementation plan that:

1. Identifies the time-frame during which actions are to be taken in the market, and when the impact is to be realized
2. Accounts for the lead-time for planning and getting things in place for execution
3. Considers all existing constraints and actionability, and is critical for the business to see the impact.

3. Influence
Need to recognize the Influence that the Innovation will have
Any innovation in a process is meaningful if it has the ability to influence the outcome – an influence that can be measured and quantified. While an innovation can be in terms of enhancement of the way a problem is defined or approached to be solved, or redefining the process or delivery, the influence can be in the form of:

1. Reduction in effort or time
2. Enhancement in the width or depth of the insights, or learning through the analysis on the same piece of information/data
3. Easier consumption of analytics for the business through simpler representation of learnings

Overall, the purpose of any innovation can be realized as either a reduced effort for the same throughput or an enhanced throughput for the same effort.

More Stories By Amit Gupta

Amit Gupta is Engagement Manager – CPG, Retail and Tech for Fractal Analytics. Gupta came to Fractal in 2005 with more than nine years of experience helping businesses optimize marketing investments, optimize portfolio pricing and trade investments, develop communication & positioning strategies, and understand consumers better. He specializes in developing analytics frameworks for solving business problems, and leading them through execution to implementation. He is responsible for consulting, design and delivery of large analytics engagements with global clients across industries. Gupta earned an MBA from Indian Institute of Management, Ahmedabad (IIMA) and a Bachelor of Architecture from NIT, Jaipur. He is passionate about designing and developing analytics frameworks and approach to solve business problems.