ANALYTICS

Implement your Data and Analytics strategy using our expertise in

What we do

Machine Learning and AI

ML and AI technologies are being used to solve problems in functional areas such as Sales and Marketing, Risk and fraud management , Smart transportation, Supply chain processes and HealthCare.

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Data Engineering

The ability to convert data into value or insight at any enterprise largely depends on the effectiveness of it’s data infrastructure.

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Data Analytics

Data Analytics techniques help you identify the right metrics, enable easier reporting, alleviate reliance on your IT staff, or simply, make sense of your data.

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Machine Learning and AI

AI and it’s subset, Machine Learning - have become so pervasive that most of us use it daily without noticing. ML and AI, in different avatars, are being used to solve business problems across industries. Broadly speaking, ML is a discipline that provides computers with the ability to learn from data without being explicitly programmed. Although the basic concepts of machine learning have been around for decades, interest now is at an all-time high as digital businesses are increasingly adopting ML, driven by the easy availability of sensor data, expanding bandwidth, sinking storage costs and availability of high quality open source frameworks and libraries.

What we do

  • Analysis - problem formulation through discussions with stakeholders, identification of different sources of data and articulation of expected outcomes
  • Data Exploration - EDA using Spark SQL , Pandas, and matplotlib
  • Implementation - Leverage our experience in libraries like Keras and TensorFlow to start creating Deep Learning models for your problem. Our experience in Python packages like scikit-learn, OpenCV, and the popular NumPy, SciPy, and matplotlib libraries will help you to create, train and tune models, feature engineering and testing your hypotheses quickly.
  • Deployment - move the models to production, integrating with existing applications

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Data Engineering

Data Engineering can be thought of as a discipline which is involved in preparing the “big data” infrastructure to be analyzed by Data Scientists. With a background in sound software engineering principles, Data Engineering teams design, build, integrate data from various resources, and manage big data. The goal is to optimize the performance of the enterprise’s big data ecosystem using ETL (Extract, Transform and Load) on top of big datasets and create big data warehouses that can be used for reporting or analysis by data scientists. Data Engineering is responsible for creating and maintaining ML pipelines which help enterprises focus more on the big data requirements and machine learning tasks in their projects instead of spending time and effort on the infrastructure and distributed computing areas

What we do

  • Analysis - discovery and identification of the different sources of data
  • Implementation- ingestion, transformation and cleansing requirements for each source
  • Pipeline Design - building ML pipelines using Spark or scikit-learn to streamline data workflows
  • ETL - Implement ETL using open source libraries like Pentaho

Data Analytics

Data Analytics as a discipline has transformed to include a variety of different business intelligence and application-related initiatives. For some, it is the process of analyzing information from a particular domain like website analytics while for others, it is the application of BI techniques to specific functional areas like sales, service or supply chain. Data analytics techniques help to convert a trained and tested model and mounds of user data into a digestible format so that business strategies can be designed around them. It can act as a check to make sure that data science teams only solve problems that deliver business value. Acting as a go-between for technical teams and business strategy, sales or marketing teams, Data Analytics translate data into actionable business insights.

What we do

  • Data Management and Business Intelligence - Managing multiple sources of data and designing ETL pipelines to bring it all together.
  • Dashboard and reporting Open source visualisation and reporting tools to create meaningful insights