Big Data

The implementation of big data strategies and detailed roadmaps.
Recommendations on how to manage data quality.
A detailed description of the architecture of a data solution, including a description of the ideal technology stack.
These strategies are intended to facilitate the adoption of new technologies by the general public.
A demonstration of the feasibility of the project.

Big data requires interpretation.
The architecture and design of a data solution that is large.
Data science solution development (a data lake, DWH, ETL/ELT setup, data analysis (SQL and NoSQL), big data reporting and dashboarding).
The process of establishing procedures for big data governance (such as the quality of data, security, etc.)
The company’s focus is on the development of 3D models, this is accomplished through the use of 3D modeling software.

The administration of big data solutions.
The process of updating software with large amounts of data is called big data software.
Adding new users and managing permissions.
Big data management.
Data cleansing.
Big data backup and recovery.
The solution’s health is evaluated.
The ability to monitor and diagnose the performance of a big data solution.

PRAGMA INTERNATIONAL provides:

The infrastructure for storing and analyzing large amounts of data.
The process of extracting and managing large amounts of data.
The company’s expertise is in the development and tuning of the MQL model.
Reports that are pre-defined and ad hoc (within a few weeks of our partnership starting).
The evolution of big data solutions.
Non-industry-specific big data applications.

Data that is stored about business processes, finances, resources, customers, etc. that can be analyzed and reported on for analytical purposes.
The use of performance analytics in the workplace.
Revenue, cost and investment analysis.
Planning, forecasting, and predicting all of the various aspects of performance (revenue, capacity, etc.).

Collecting, processing and storing large quantities of operational data (transactional data, production process data, assets, employees, plans, etc.).
Identifying irregularities and undesirable patterns in a company’s operations (production processes, product distribution, etc.).
Identifying obstacles (equipment failure, lack of resources, etc.), conducting a cause-effect analysis.
Forecasting (demand, capacity, inventory, etc.).
The use of scenario modeling and operational risk management.

Using manufacturing data (equipment model, year, sensor data, error messages, engine temperature, etc.), to predict equipment failures and the remaining useful life in real time.
Real-time monitoring of production processes, equipment data, materials usage, etc., that can be used to identify factors that lead to increased production time and delays in production. This is used to optimize the process and reduce the time required to produce.

Analyzing integrated transactional data, interaction events, customer behavior in real time, identifying complex AML transactions, creating advanced risk models, etc., to identify potential fraud and fraud patterns.
Consolidating and analyzing data on assets and liabilities and conducting credit risk assessments, liquidity risk assessments, counterparty risk assessments, etc., to minimize financial risks.

Analyzing the network usage patterns and trends and employing sophisticated models to forecast areas with excess capacity and to optimize the network capacity.
Customer satisfaction analysis, identifying customer churn patterns, and recommending products and services that will increase customer retention.

Tracking and monitoring the real-time data from sensors (such as cargo state, location, etc.) to make the delivery process completely transparent and ensure the delivery of sensitive goods with high quality.
Using real-time data to analyze driver behavior, maintenance needs, weather data, traffic data, fuel consumption data, etc., in real time, this enables dynamic route optimization.

Assisting in the collection, storage, and analysis of patient-related data (doctor’s notes, medical images, EHR/EMR data, research results, etc.).
Real-time patient monitoring and notification of trends and patterns that require the doctor’s attention.
Recommendations for personalized care plans.
Mining claims information is utilized to identify fraudulent activity.
Forecasting the demand for healthcare products, the risks associated with suppliers, and the effects of supply and demand on the healthcare supply chain.

Analyzing log and sensor data from different types of equipment in real time and applying the results to operations in order to facilitate predictive maintenance.
Analyzing data from drilling and production processes, seismic monitors, etc., to identify new oil reserves.
Using historical production data and sensors to analyze the data and create predictive models that estimate well production and understand the usage rate.

Analyzing customer demographic information, data from mobile apps, in-store purchases, etc. to identify customer paths and behavior, this is used to optimize merchandising, provide personalized recommendations, discounts, etc.
Planning for future demand, analyzing the past and present attributes of products and services, and utilizing recommendations from ML to create new products.
Consolidating and analyzing data from social media, web visits, phone logs, and other sources to personalize customer support, launch targeted customer retention campaigns, etc.
Analyzing customer transactions, spending patterns, predicting future customer actions with machine learning models that assess the value of a customer over time, target marketing and sales offers to your most valuable customers, etc.

The components of a big data solution that we discuss include: