Big Data

Big data services help companies maximize data value and achieve business goals with big data analysis. Sukrom 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.

Sukrom's Big Data Services

  • 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 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 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 Analytics Use Cases Sukrom 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

  • 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.
Forecasting the supply demand, supplier risks, etc., to enable healthcare supply chain optimization and planning.
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.
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.
  • 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.
  • 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.
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