Analytics
As an analytics leader, do you face resource constraints and need fast, easy-to-use solution that can demonstrate value from your data and make insights available, accessible and usable to your business users?
Our Analytics experts can help you tap into enterprise data to make better, more informed business decisions. Through the use of various business analytics techniques, we can help:
Define your Analytics strategy
Select relevant software technologies based on your requirements and budget
Design proof-of-concept prototypes
Upon successful validation of the prototype, help you to scale and deploy your analytics solution to a production-grade implementation
Talk to a Data Expert
Contact us to connect with one of our data experts who will review your data challenges and help map out steps to achieve data-driven decision making.
In today’s unpredictable environment, it’s more important than ever to gain continuous insight and therefore have the ability to react quickly. We can rapidly identify the factors that impact your organization performance, uncover and decipher patterns, trends, relationships, and outliers if any.
We use industry proven CRISP – DM for data mining and predictive analytics solutions. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases – Business understanding, Data understanding, Data preparation, Modeling, Evaluation and Deployment.
Technologies We Work With
Our consultants have experience supporting most modern analytics
tools and technologies.
Customer Experiences
Our analytics consulting services have enabled our clients get the best insight out of their data.
Consumer Products
We designed a weekly demand forecasting model technique using Holt Winters and Arima. This included establishing benchmarks for forecasting accuracy and expected lift from the proposed modeling technique. This improved forecasting accuracy resulted in optimizing the entire supply chain and delivered Economic Order Quantity (EOQ) efficiencies.
Retail
We leveraged technology and employed pseudo automated quantitative methods with Machine Learning to optimize their digital media spend and generate revenue lift through targeted online campaigns. We developed and deployed self- calibrating models using Spark after initially creating a Data Lake on Hadoop multi-node cluster.
Financial Services
We optimized online user experience and new customer acquisition with data driven insights delivered through mining semi-structured text data generated by various channels – online, in-branch, phone, mobile etc. We used Latent Dirichlet Allocation (LDA) and Support Vector Machines (SVM) based Machine Learning Topic model development.