Big Data Services to Make a Big Difference
Big data services help companies maximize data value and achieve business goals with big data analysis. Since 2013, Loginet renders a range of big data services, including consulting, implementation, support, and big data as a service to help clients benefit from the big data environment.
Highlights about Loginet
- During 33 years in data analytics and data science, we have been satisfying companies’ diverse analytical needs (including the need for advanced analytics), which makes us fully understand the transformation you’re undergoing.
- We hold partnerships with Microsoft, Amazon, Oracle and other tech leaders to keep pace with the technological advancements and the evolution of the data analytics landscape.
- We’ve got 9 Microsoft Gold Competencies, including Data Analytics and Data Platform.
- ISO 9001 and ISO 27001-certified to assure the quality of the big data consulting services and the security of the customers' data.
Loginet's Big Data Services
Big data consulting
- Big data implementation/evolution strategies and detailed roadmaps.
- Recommendations on data quality management.
- Big data solution architecture + an outline of an optimal technology stack.
- User adoption strategies.
- A proof of concept (for complex projects).
Big data implementation
- Big data needs analysis.
- Big data solution architecture and design.
- Big data solution development (a data lake, DWH, ETL/ELT setup, data analysis (SQL and NoSQL), big data reporting and dashboarding).
- Setup of big data governance procedures (big data quality, security, etc.)
- ML models development.
Big data support
- Big data solution administration.
- Big data software updating.
- Adding new users and handling permissions.
- Big data management.
- Big data cleaning.
- Big data backup and recovery.
- Big data solution health checks.
- Big data solution performance monitoring and troubleshooting.
Big data managed analytics services
- Big data solution infrastructure setup and support.
- Big data extraction and management.
- ML model development and tuning.
- Predefined and ad hoc reports (within several weeks after our cooperation starts).
- Big data solution evolution.
The Financial Times Includes Loginet Australia Corporation in the List of Australias’ Fastest-Growing Companies 2022
Loginet is one of 500 companies with the highest compound annual growth rate in revenue. This achievement is a result of our unfailing commitment to provide high-quality IT services and find best-value solutions to clients' needs.
Big Data Analytics Use Cases Loginet Covers
Industry-neutral big data analytics use cases
Big data warehousing
- Storing data about business processes, finances, resources, customers, etc. for analytical querying and reporting.
- Corporate performance analytics.
- Revenue, cost and investment analytics.
- Predicting, forecasting, planning (performance, revenue, capacity, etc.) with all interdependencies.
Operational analytics
- Collecting, processing and storing large volumes of operational data (transactional data, production process data, asset data, employee data, plans, etc.)
- Detecting deviations and undesirable patterns in a company’s operations (production processes, product distribution, etc.)
- Recognizing bottlenecks (equipment failure, resource unavailability, etc.), conducting cause-effect analysis.
- Forecasting (demand, capacity, inventory, etc.)
- What-if scenario modeling and operational risk management.
Industry-specific big data analytics use cases
Manufacturing
- Analyzing manufacturing data (equipment year, model, sensor data, error messages, engine temperature, etc.) to predict equipment failures and the remaining useful time in real time.
- Real-time monitoring of production processes, production equipment data, materials usage, etc., to identify factors leading to production time increase and delays for production optimization.
Operational analytics
- Capturing, storing, and analyzing patient-related data (doctor notes, medical images, EHR/EMR data, R&D results, etc.).
- Real-time patient monitoring and alerting on trends and patterns requiring the doctor’s attention.
- Personalized care plans recommendations.
- Mining claims data to identify fraudulent activity.
Financial services
- 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 assessment, liquidity risk assessment, counterparty risk analysis, etc., to mitigate financial risks.
Transportation and logistics
- Tracking and analyzing real-time sensor data (cargo state, location, etc.) to make the delivery process fully transparent and ensure high-quality delivery of sensitive goods.
- Analyzing driver behavior, maintenance needs, weather data, traffic data, fuel consumption data, etc., in real time to enable dynamic route optimization.
Retail and ecommerce
- Analyzing customer demographic data, data from mobile apps, in-store purchases, etc. to identify customer paths and behavior to optimize merchandizing, provide personalized product recommendations, discounts, etc.
- Forecasting customer demand, analyzing the key attributes of past and current products/services and commercial success of their offerings, and using ML-driven recommendations to create new products/services.
- Consolidating and analyzing data from social media, web visits, call logs, and more to personalize customer support, launch tailored customer retention campaigns, etc.
- Analyzing customer transactions, spend patterns, predicting future customer actions with ML models to assess customer lifetime value, target marketing and sales offers to your best customers, etc.
Oil and gas
- Analyzing log and sensor data from different types of equipment in real time and putting analytics results into operations to facilitate predictive equipment maintenance.
- Analyzing drilling and production process data, data generated from seismic monitors, etc., to identify new oil deposits.
- Analyzing sensor and historical production data and building ML-based predictive models to measure well production and understand the usage rate.
Telecommunications
- Analyzing the network usage trends and patterns and using sophisticated models to forecast areas with excess capacity and optimize the network capacity.
- Analyzing overall customer satisfaction, identifying customer churn patterns, and recommending the most relevant products/services to increase customer retention.
Want to Harness Big Data for Your Business Needs?
Loginet helps companies fetch big data from a variety of sources, consolidate and analyze it to get valuable insights from previously untapped data assets.
Technical Components of a Big Data Solution We Cover
Data lake
Data warehouse
ETL processes
OLAP cubes
Data visualization
Data science
Data quality management
Data security
Big Data Technologies We Use
Here’s the list of technologies most frequently used in our big data projects:
Entrust Your Big Data Project to Professionals
Having solid experience in rendering big data services, ScienceSoft is ready to help you achieve your big data objectives for increased business efficiency and enhanced decision-making.
