Enterprise Data Management
This feature enables your company to store, move, and integrate data as and when necessary. Before analyzing the data, you can manage it in a way that breaks down silos.
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Our data analytics solutions can empower and engage users by blurring the boundaries between the virtual and physical realms.
Dark data or unused information about online transactions and connected devices can ruin many possibilities and create hurdles in grabbing new opportunities. AI and automation technologies can combine to provide actionable insights and 360-degree customer services in real-time. Our advanced data analytics solutions can help you achieve this objective.
Whether you run a customer-centric enterprise or want to move your business process to the cloud, customized data analytics solutions always remain handy for your organization. We, at Rejig, believe that a reliable data analytics solution can lead your business toward success by establishing value creation services.
Entrepreneurs focus on getting deep insights and understanding the underlying value of every type of data. e. Our customized and comprehensive data analytics solution integrates features based on BI (Business Intelligence) and Analytics to assist entrepreneurs make insightful business decisions quickly and accurately.
Rejig houses experienced BI and analytics consultants who have hands-on experience of working on various BI projects. We have leveraged proven methodologies and cutting-edge tools.
Our customized data analytics solutions can help modern enterprises to leverage the benefits of AI and ML (Machine Learning) technologies along with Big Data. Various industries, irrespective of their scale and business model, can get actionable insights that can help them make informed decisions.
Our advanced, reliable and feature-rich data analytics solution is designed to serve the dynamic and complex needs of various industry sectors including BFSI (Banking, Financial Services, & Insurance), electronics & high technology, manufacturing, power, utlity ,eCommerce and other data-driven industries.
The first step is to determine the data requirements or how the data is grouped. Data can be separated by age, demographic, or gender. Data values may be numerical or divided by category.
The second step is the process of collecting the data. This can be done through a variety of sources such as IoT sensors, controllers, wearable, mobile/computers, cameras, etc.
Data will be collected, standardized, normalized, and cleaned so it can be analyzed properly. Once the datasets are cleaned, they can be analyzed by using various techniques.
Modeling and algorithms (also know as mathematical formulas) may be applied to the data for identifying relationships among the variables for generating an accurate model.
An accurate predictive model is developed based on the aggregated data using statistics, curve fitting tools, or machine learning techniques for forecasting future events.
The predictive analytics goes beyond sorting and describing data, therefore, it relies heavily on complex models designed to make inferences about the data it encounters.
These models utilize algorithms and ML to analyze past and present data to provide future trends. Each model differs based on the specific needs of those employing predictive analytics.
Decision trees use branching to show possibilities stemming from each outcome or choice. it helps the solution understand various needs and inputs for specific data.
Regression techniques assist with understanding relationships between variables. Neural networks utilize algorithms to figure out possible relationships within data sets.
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