Analytics
Our Analytics services have been designed to act as an enabler for your Digital Enterprise and ensure you’re ahead of the game.
Predicting Business Outcomes with Data Science & AI
The evolution of Machine Learning has helped in accelerating the delivery and increasing the accuracy of business predictions based on your historical data patterns. But do you know what you need to get the best results?
Our Analytics services are focused on solving your complex business challenges, produce measurable gains in business outcomes and drive your growth journey through innovation.
We help a host of sectors to predict business outcomes and offer a full cycle of analytics services for functions across the enterprise from Finance, Sales, Customer & Marketing HR, Operations, Risk & Fraud and Supply Chain.
How We're Different
Our talent pool, experience across a range of Analytics platforms and accelerators help us deliver analytics at speed.
Our Experience
Ready made Accelerators
Platform expertise
The success of Analytics depends on the experience of Data Scientists, Business Analysts and Data Engineers. Our team expertise is built through real time experience and ongoing learning in state-of-the-art labs.
Platform expertise
Ready made Accelerators
Platform expertise
We have experience in all the leading statistical modelling platforms such as IBM Watson, MS Cortana, and Google Analytics. Plus we have expertise in data tools like Apache Spark, Apache Storm, and Mahout—allowing us to apply Machine Learning algorithms to large, varied, and rapidly changing data sets.
Ready made Accelerators
Ready made Accelerators
Ready made Accelerators
We offer Faster Insights and quicker ROI through our pre-defined Frameworks and Accelerators. We use our best practices and library of resources, so you don’t have to reinvent the wheel.
Salvage Car Digital Drive
Client challenge
A Salvage Car client wanted to use a scientific, data driven methodology to set reserve prices for their salvage cars during auctions. The reserve prices were being set based on the “gut” feel of insurance company executives meaning revenue loss and/or unsold inventories. The data required to create price prediction models was unclean, un-integrated and messy. There was no Machine Learning deployment or Life Cycle Management strategy in place.
How we made value
Admire conducted multiple workshops with key experts to determine the existing process of reserve price setting and the probable variables involved in making the decision. We shortlisted these key predictor variables and performed data profiling, and analysis to determine distributions, outliers, missing data etc. Using this info we defined and implemented methods to manage outliers and impute missing values using a variety of machine learning algorithms and deployed them on Azure ML Services.
Admire impact
We ensured a reduced inventory up to 10% and maximization of revenue. Our data driven method of determining sale prices helped in setting an optimal base price and we provided a feedback loop for continuous improvement, including a dashboard to help determine when the model results drift from reality. ML Services.
Admire
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