Building a robust predictive model involves multiple steps. You can pick and choose what you want us to do for you or we can all all of them for you.

 

Data Cleaning

With data being the lifeblood of business, the process of data cleansing has become increasingly important. We correct spelling mistakes, handle missing data and weed out nonsense information. Without this, junk data will generate junk results and mislead the business. We collect data from multiple (legacy) systems, scrub them to correct, complete, format, de-duplicate and establish controls.

 

Data Structuring

The data has to be efficiently organized and, the structured & unstructured data updated to fully leverage all available information.

.

Predictive Analytics

Once the data is cleaned, it is modeled using most relevant machine learning algorithms for clear interpretation of business information. Our scientists apply a variety of exploratory data analysis techniques to begin understanding the messages contained in the data. This may result in additional data cleaning or additional requests for data (if needed). The model is evaluated for its accuracy, robustness and sensibility in logically explaining cause and effect. The iterative nature of these activities makes it crucial to have the right data scientists working on the problem. This also makes it hard to force-fit prepackaged statistical products to a given problem.

 

Model Deploy

Once the model is iterated, tested and built it is deployed using standard and open source tool sets and hosted where requested by the client (cloud or anywhere else).

 

Model Maint

As the environment constantly evolves so should the predictive model. It must account for the development of new relationships between variables to preserve the accuracy of prediction. We monitor and update the model so the latest data is always assimilated for higher prediction accuracy.

 

So why us? 

… Because we bring a unique offering, team and focus to you.

Diversity of team-experience– Our team has management consulting and senior executive experience in companies like A.T. Kearney, Booz & Co, Outerwall (parent of Redbox), Amazon, SAS, Citibank, NetApp, WNS-Marketics, Nokia Siemens, Motorola, DSM and Wipro.

Multinational client experience– Our veteran team has advised multinational clients in US, UK, China and India.

Single focus of our firm is predictive analytics. We do this so as to have the best-of-breed analytics capabilities.

Tailored-tools are built just for your business setting. It’s not a “one size fit some people” approach.

Host & Maintain– We make the use of predictive models hassle-free, by hosting it for you and maintaining it so it is relevant long after the engagement is over. You can be operational without significant involvement from your IT department.

 

Click Here for Testimonials