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At Cypher 2022, Analytics India Magazine hosted the fourth edition of Data Science Excellence Awards presenting awards to companies and individuals in various categories, recognising the innovation and hard work of achievers in the field of analytics, data science, and artificial intelligence.
Best AI/ML Product of the Year
The Best AI/ML Product of the Year was bagged by four companies in this edition: AB InBEV GCC, Colgate Palmolive, Tredence, and Philips India.
ABInBEV is the world’s leading brewery company serving new ways to celebrate the future. They developed CatExpert.ai, an optimised assortment recommendation system focusing on alcoholic beverages by clustering similar stores and ascertaining demand transference.
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Colgate-Palmolive, using their interconnected framework of therapeutic knowledge and technology, developed an end-to-end machine learning model that improves Return on Ad spend on Amazon Search (ROAS). The model is 80 percent accurate and consists of multi-staged classification models and cascaded regression.
Tredence by focusing on solving the ‘last mile’ problem—a gap between value realisation and insight creation—developed Customer Cosmos that helps unlock the fragmented and vastly untapped customer data to provide solutions for developing customer centric strategies. With ML models and algorithms, the idea allows retailers to leverage first-party consumer data to accelerate business.
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Philips India, the leading health technology company, using AI driven technology launched three computer vision based solutions using Philips Lumea app. The model has the ability to measure body parts and analyse the hair follicle using computer vision to provide better treatment and track the progress with great accuracy.
Best AI/ML Implementation
Best Machine Learning/Artificial Intelligence Implementation was awarded to four companies: Accenture, CitiCorp Services India, Tiger Analytics, and Unilever.
Accenture, using their leading capabilities in digital, cloud, and security, derived a personalised recommendation solution that helps to target the right customers with most relevant offers to drive high CLTV, incremental sales and customer loyalty.
CitiCorp Services India Pvt. Ltd’s Analytics Information Management (AIM) used a computer vision solution to develop an automated check payments monitoring system served by credit card customers that oversees that all checks are processed accurately, timely, and ensure compliance with regulations.
Tiger Analytics’ Computer Vision platform provides solutions for CPG and retail segment in cases like planogram analysis, video analytics, PPE compliance, and manufacturing line. Some unique benefits include real-time alerts on stocks, frictionless experience for customers, and full compliance management.
Unilever, owner of FMCG brands like Dove, Sunsilk, Hamam, Lux, Rexona, uses a distribution management system using AI to industrialise and scale models globally. The platform is customised on a country level and therefore the pipeline and recommendations are integrated based on the distributor and the historical data of quantity of sale.
Top GCCs using AI/ML
The award for Top Global Captive Centres using AI/ML was awarded to six companies for excellent use of machine learning and artificial intelligence: AstraZeneca, HSBC Software Development (India), Maersk Technology Centre, Rakuten India, Societe Generale Global Solution Centre, and American Express.
AstraZeneca’s AZ Brain initiative developed an AI and ML based engine delivering insights from patient data to physicians that enable doctors and clinicians to provide better care for the patients. The product is an in-house development and for internal usage only.
HSBC Software Development (India) Pvt Ltd uses Change Analysis and Risk Evaluation (CARE), a system that predicts success rate of the change going into production. The algorithm is based on statistical analysis of data collected from machine learning to predict risks related to the ‘To be deployed’ change and therefore helps in analysing the success rate of a change.
Maersk Technology Centre’s Innovation, Data Science & Automation (IDA) has been providing products and solutions for various problems related to machine learning for a long time. In the last two years, the team has developed products like Contract Lifecycle Management (CLM) Universal Search, Inland Pricing Workbench (IPW), Freetime extension revenue optimiser, Email Case Classification (ECC), Tamper Detection in Signed Contract Documents, Smart Inland Delivery recommender, and Shipping Instructions extractor.
Rakuten India has been working in technology streams like Mobile, Web Analytics, AI/ML, Backend engineering, etc. and enabling businesses to leverage the technology. The company has developed various products in various fields like AIris, PITARI, Credit Scoring, AI for Store location, AI for Healthcare, Automated Product Catalogue, and Optical Character Recognition (OCR).
Societe Generale Global Solution Centre’s Data Service Centre of Excellence has been demonstrating success in financial services, wholesale banking, automotive leasing, compliance, and an early warning system to mitigate risks. Their service model improves business visibility through descriptive, diagnostic, and predictive analytics to measure and mitigate risks and enhance experiences for delivering ESG services.
American Express, the globally integrated payments company uses Document Intelligence that provides AI-powered automated document understanding in multiple use cases.
Data Engineering Excellence
For excellent use and producing outcomes in data engineering, five companies were awarded with Data Engineering Excellence award: Axis Bank Limited, Elsevier, IBM, Schneider Electric, and Wells Fargo.
Axis Bank Limited have engineered a framework for personalisation at a larger scale. The framework is developed from scratch on the company’s Big Data Lake with key tenets like Scalability, Configurability, and Simplicity and focuses on data provisioning, process execution engine, and centralised event management—giving millions of customers personalised recommendations daily using this Lambda architecture.
Elsevier developed an innovative and trustworthy chemistry information platform named Reaxys which is built on expert-curated chemistry information and equipped with cutting-edge technology. It enables and equips researchers to a database for research and development. Their Reaxys Content Catalyst (RCC) is an AI-powered, automated content enrichment production pipeline with the capability to expand coverage of Reaxys. The team also developed RCC Entellect, a platform to productionise data science components and handles millions of published documents daily.
IBM’s Software Labs produced an automated tool based on Data engineering which supports all the cloud and monitoring systems. It is an Automated AI-based tool for monitoring and backup of Kubernetes and CloudPak For Data integration and automation. Scaling this tool for AWS and Azure makes sure that no other tool is required to track system up time or monitoring.
Schneider Electric has built an internal solution for data automation and workflow automation using Apache Airflow and Apache Spark. This allows automation of data science development workflows from coding, data processing to machine learning training, and deployment platforms. It is an open source, completely scalable, corporate standards compliant data flow solution that runs on AWS EC2.
Wells Fargo Analytics developed a scalable automated data framework to source and apply QC, standardise, and store third party public or subscription-based data available in external networks. This is an improvement on the previous methodology of downloading data from distinct, multiple locations. This model included sourcing marketing and non-marketing data consisting of macro and micro economic metrics.
Data Science/AI For Social Good
One of the great uses of AI/ML and data science is to implement it for social causes and benefits. Three companies were awarded for applying AI and Data Science for social good: Capgemini, Ernst & Young, and Genpact.
Capgemini offered solutions and achieved success by developing a model for automated detection of road features using aerial imagery of training areas. The data was high-resolution images provided through the National Agricultural Imagery Program (NAIP) by the Department of Agriculture of the US. This helped in aiding recommendations for experts in biodiversity, sustainability, and conservation.
Ernst & Young developed a frog species distribution model (SDM) that used a variety of open-source geospatial datasets using machine learning. By modelling the importance and impact of presence and absence of frogs across different regions and time, the government and ecologists can understand the effect of this biodiversity and, inversely, understand the influence of climate change in order to take concrete actions.
Genpact developed four solutions for their racing team partner, Envision Racing, in developing electric car technologies. A Lap Estimate Optimizer (LEO), a model that explores the best solutions for racing dynamics in real-time by measuring remaining time, safety, and several other factors. Secondly, Augmented Race Intelligence that runs analytics on practice and simulation run data. A Race Analytics Engine that monitors radio communications data and a Carbon Footprint Calculator that automates data collection and creates reports to allow the team to make greener decisions.
Best Data Science Project Of The Year
Six from the huge number of entries for the Best Data Science Project of the Year were awarded: Course5, FIS, Kotak Mahindra Bank, Merck Group, Merkle, and MiQ.
Course5 entered the programme with their AI-driven Augmented Analytics platform, Course5 Discovery, that enables data-driven decision making and enables timely proactive actions by providing companies with access to their enterprise data. This drives sustainable impact in terms of value optimisation and cost reduction while focusing on customer satisfaction.
FIS made a Credit Assessment system that offers a complete solution for credit risk management, delivering benefits for credit and risk officers as well as front-line relationship managers. The system caters to all the processes of commercial lending like the analysis of borrowers’ performance and strategies. Along with this, FIS also developed Optimist (Ambit Credit Assessment) that assesses credit worthiness of customers and develops customer management strategies across the relationship cycle.
Kotak Mahindra Bank Ltd. built a customised, end-to-end integrated using predictive and prescriptive models. To solve the dual-problem of increasing cross-sell conversions and market share in instalment loans, campaign communication for maximum conversions with a combination of machine learning model and a composite prescriptive framework, the bank has also started building multivariate models.
Merck Group with a problem statement of finding optimal price points for Stock Keeping Unit with low margin to maximise the total profits developed a project using big data techniques to facilitate product manager’s decision making. The tool provides real time insights about any changes in demand or profitability, helps in controlling prices by measuring the metrics and knowing the customers for deciding suitable pricing.
Merkle built a Feature Extraction Engine on their JARVIS for data cleaning and engineering features. The platform deploys solutions like pricing insights, product enrichment, descriptive and predictive analytics dashboards. The feature extraction engine drives the data processing for several clients implementing insights dashboards, content services and pricing intelligence models. This allows the clients for user-defined features and contexts for uniform extractions across sessions or during training ML models.
MiQ Digital Pvt Ltd developed an algorithm for reaching consumers in a privacy-first cookieless world by predicting geo-contextual features that can replace retargeting, personally Identifiable Information data like IP address, while maintaining the performance of the marketing campaign. This is the first solution which tries to mine the behavioural web viewing attribute of a set of audience and then tries to target on those attributes without having any of the audiences personally identifiable data.
Top Domestic firms using AI/ML
Various domestic firms are leveraging AI and ML in their everyday work. For this award five recipients were selected from different fields: Data Labs, Dr. Reddy’s Laboratories, HDFC Bank, Max Life Insurance, and TVS Motor Company
Data Labs of Landmark group recently implemented the model for their Kids Growing feet Campaign that allows identification of customers whose kids have outgrown their apparel size and recommends them products based on previous purchases. It also predicts the next purchase size and date for the customers. The model has achieved 80% precision and accuracy and is well received by the stakeholders.
Dr. Reddy’s Laboratories developed their in-house platform, JARVIS, that combines internal and external data sources to provide a comprehensive view and actionable intelligence in the context of business. JARVIS has eliminated manual work for discovering and cleaning data by leveraging ML algorithms and reinforcement learning to uncover precise reliable actionable insights.
HDFC Bank’s Data Science and Analytics Unit has evolved from being a support function to now a forefront of driving digitally assisted business and have developed numerous projects and services using AI/ML. From sales oriented projects like Immediate Next Best Action (I-NBA), Easyemi Recommendation Advisor (ERA), Journey and Merchant Analytics to Data Engineering and AI/ML products like Automated in-house Transaction Classification Engine (TRACE), Eagle to interpreting complex features from SparkBeyond, AIME, and Horizon which is a first-ever web-based Embedded Analytics Framework.
Max Life Insurance has spearheaded the AI capability development and expansion across all business processes, like establishing an AI Works unit. The company has also delved into using cognitive intelligence solutions such as vision and speech AI and fully automating on cloud workbenches. Max Life also leverages external data to enrich the models and apply modern AI techniques to expand into HR and financial fields as well like NLP and VOX systems.
TVS Motor Company built a dashboard of lead classification models to predict the tendency and propensity of individual consumers’ buying to help the sales staff follow up on a timely basis. The Lead Classification Engine automated the complete machine learning lifecycle with the help of sophisticated tools and technologies. The future plans of the company is to leverage LCE model output to personalise the content and offers on the TVSM website in real-time.