For any organisation, data is key to success. Considering data science an invaluable tool to solve your business needs, we provide a variety of data solutions including predictive analytics, statistical modelling, data engineering, attribution modelling, personalization, decision/portfolio optimization and content optimization.
At RPAi, our talented team takes a deep dive into your data sets, using state-of-the-art analysis tools and advanced logics to reveal actionable business insights. From there, we incorporate mathematics and statistics throughout all of our practices, leveraging the power of machine learning and predictive analytics to develop strategies that guide your organizational goals.
RPAi uses cutting-edge tools for data preparation, management and harmonization. Our data preparation method comprises of 1. Data exploration 2. Data cleaning 3. Data changing 4. Data shaping 5. Data publishing for analysis
RPAi leverages statistical methodologies, machine learning or a blend of both these prevalent practices to create analytical models on big data. The statistical modelling service spans: 1. Data analysis 2. Data interpretation 3. Data explanation 4. Data presentation
We take industry-standard approach to insight generation. The process includes gathering, organizing and retaining data, generating various analytics models from the data, and analyzing the analytics models for building actionable business strategies. It covers: 1. Data mining 2. Model building...
Our insight deployment process focuses on applying the analytical results into the routine decision making process and automate it. 1. Write back of insights to current processes and systems 2. Gathering results of analytics implementation through feedback loop 3. Advancement...
Financial services companies are highly information-driven and stand to gain tremendously from insights from their information to improve their top-line as well as bottom-line. Data science can help banks in almost all areas of work including the following: · Risk monitoring · Trade surveillance · Payments · Fraud · Claims · Fintech · Customer experience
Data science is employed in many spheres of human life. The value of the algorithms and their efficiency can hardly be underestimated. The use of data science in the field of media and entertainment has become an art. The most vivid and remarkable data science use cases in media and entertainment industry. · Personalized marketing · Real-time analytics · Recommendation engines · Content distribution on social media · Collecting and analyzing customer insights
Healthcare industry is generating a copious amount of data every day. Electronic medical records, billing, clinical systems, data from wearables, and various pieces of research continue to churn out huge volumes of information. This presents a valuable opportunity for healthcare providers to ensure better patient care powered by actionable insights from previous patient data. Data scientists across the world are gradually revolutionizing the healthcare industry. From improving care delivery to achieving operational experience, they’re working to optimize every aspect of healthcare operation by unlocking the potential of data.
Almost every industry has been in one way or another affected by the emergence of data science technologies. The retail sector is no exception. Data science provides a great opportunity for retailers to take advantage of the customer data they own and turn it into actionable insights that will end up boosting revenue. Data science plays a vital role in almost all sectors of retail such as assortment, recommendation, Logistics and Supply Chain Management, Demand Forecasting, Price Optimization for products, Predictive Maintenance, Churn prediction, and Data-Driven Product Management Customer sentiment analysis.
Supply chains are one of the main areas in which the use of the Data Science technologies can cause a revolution in the optimization and automation of processes. Data Analytics is used extensively in Supply Chain for following purposes: · Planning, Product Launches to Replenishment planning · Scheduling of resources and assets · Landed Costing , Transportation Analysis · Demand Planning · Fulfillment Process Analysis · Vendor Analysis · Purchase Order Analysis