We start with a business intelligence vision and strategy. Whether you are just getting started or have a strategy in place and need our help with the implementation, we are there to help.
We always take a use case based approach to analytics and start with the current state assessment. After analyzing the gaps in the current state architecture, we provide recommendations on the next steps.
We adopt analytics to business requirements and opportunities and streamline access to multiple data sources again based on your priorities.
Our team of machine learning experts can also help you with techniques for creating ML pipelines, feature engineering, vectorization and tuning hyperparameters to decrease model training and increase model scores for your custom-made machine learning models.
We aim to automate the process throughout the data lifecycle and also build in governance for your analytics solutions.
We work with the majority of the BI tools like Sisense, Tableau, Power BI, Google Data Studio, Looker, Amazon Quicksight, Amazon Athena, Oracle Analytics Cloud, OBIEE and Thoughtspot.
We are also experts in open source tools using Python, Jupyter and R and the majority of libraries like Tensorflow, Keras, H2O, PyTorch, scikit-learn and NLTK and have worked with the majority of the algorithms like linear and logistic regression, support vector machine (SVM), Naive Bayes classifier, gradient boosting, k-means clustering, KNN, Artificial neural networks (ANN), Recurrent neural networks (RNN) and convolutional neural networks (CNN).
WATCH THE FREE “WHY DATA ANALYTICS FAIL” WEBINAR TODAY
In this webinar we talk about the major reasons data analytics fails and how to implement a data analytics strategy catering to strategic business outcomes.