Embrace Data Science in your enterprise. Get customized analytics tailor-made to solve your business woes.
We have successfully delivered and are maintaining multiple Data Analytics & AI services projects in the B2B domain which are envisioned towards helping enterprises in the FMCG, Fashion, Retail and other Manufacturing and Distribution sectors to become “data literate” and cultivate a data-oriented decision-making culture within their organization.
Solve identified specific problems and their constraints in the subjected business domain with Data Analytics.
Bring data driven decision making culture in organization and take real-time decisions backed by ML algorithms and suggested business best practices.
Avail technical Data Analytics platform support and maintenance. Find opportunities to upgrade your current architecture.
Subscribe to Retention Models to improve your business by collaborating with our Data Science team with hot-line support.
With the best lot of Data Engineers, Data Scientists, Project Managers and Business Analysts at our disposal we apply Agile Analytics methodologies and Cross Industry Standard Process for Data Analytics & Decision Sciences.
Business Analysts and domain experts engage with your stakeholders and identify the key business problems to be addressed by using Data Science.
The quality and granularity of the data will be assessed to determine if they will support the objectives defined in the business understanding phase.
It includes processes such as data cleansing, feature engineering, and evaluation of feature importance. It is frequently in this step where the “art” of Data Analytics becomes most valuable.
The modelled data would be visualized, statistical tests performed and evaluated for answering the business problems formulated and refined in previous steps.
It is important to communicate the results to the stakeholders before deployment. This will undoubtedly lead to revisiting Business Understanding and other previous steps, which will refine expectations and results.
Actions include hardening the data infrastructure, educating end-users to interpret insights from dashboards, and reviewing the assumptions and limitations of the data and modelling techniques.