Data Analytics Services

• Monitoring revenue, expenses and profitability of a company.
• Profitability analysis and financial performance management.
• Budget planning, formulating long-term business plans.
• Financial risk forecasting and management.

• Customer behavior analysis and predictive modeling.
• Customer segmentation for tailored sales and marketing campaigns.
• Personalized cross-selling and upselling offers for extended customer lifetime value.
• Predicting customer attrition and customer churn risk management.
• Customer sentiment analysis.

• Sales channel analytics.
• Pricing analytics to design pricing strategies.
• Identifying and predicting sales trends.
• Conducting product performance analysis.
• Tracking customer interactions with a product to identify pain points leading to churn.
• Conducting competitor benchmarking.

• Real-time asset monitoring and tracking.
• Predictive and preventive maintenance, developing asset maintenance strategies.
• Planning asset investments.
• Asset usage analytics, planning and scheduling asset modernization/replacement/disposal strategies.

• Employee/department performance monitoring and analysis.
• Employee experience and satisfaction analysis.
• Employee retention strategy optimization and management.
• Employee hiring strategy analysis and optimization.
• Labor cost analytics.

• Operational capacity planning and optimization based on the analysis of incoming shipments, customer delivery schedules, vehicles availability, and personnel shift schedules.
• Predictive analytics for vehicle maintenance (failure prediction, recommendation of maintenance actions, etc.).
• Vehicle demand forecasting.
• Predicting optimal amounts of fuel needed based on the analysis of driving patterns.
• IoT data analytics (data on cargo temperature, humidity, etc.; data on driver behavior, data on vehicle condition, etc.) for safe cargo delivery.

• Overall equipment effectiveness analysis and optimization.
• Manufacturing process quality optimization.
• Equipment maintenance scheduling.
• Power consumption forecasting and optimization.
• Production loss root cause analysis.

• Patient health condition monitoring, condition-based alerting.
• Patient treatment optimization.
• Assessment of patient risks and personalized care plan recommendations.
• Proactive care (defining trends and patterns in patient condition requiring a doctor’s attention).
• Fraud detection in healthcare insurance.
• Medical staff workload prediction and work shifts optimization.
• Optimization of clinical space and equipment usage.

• Retail business performance analysis, monitoring sales and profitability.
• Demand analysis and forecasting.
• Multi-echelon inventory optimization.
• Assortment and merchandising planning and optimization.
• Data-driven recommendations on optimal product promotion activities.

Within a subscription fee, you carry out data analysis on a fully customized cloud analytics platform built and maintained by ScienceSoft.

ScienceSoft’s data analysts collect and process your data to deliver quick one-time or recurrent analytics insights to you.

ScienceSoft’s consultants help you choose an optimal data analytics strategy and guide you on designing, developing, implementing and improving a proprietary data analytics solution.

We design and implement an analytics solution with the basic functionality to address your current data analytics needs and scale up as they grow. Your data analytics solution may include such elements as DWH, OLAP cubes, data visualization, data science, big data components etc.

ScienceSoft helps upgrade the existing data analytics solution to achieve maximum ROI and meet the new data analytics needs.

ScienceSoft implements a robust data management framework to organize your processes of data collection, storage, access, security, analysis, etc.

• Extract, transform, load (ETL) or extract, load, transform (ELT) design and implementation.
• Data governance (data security, quality, availability, etc.) implementation.
• Data warehouse and data marts design and implementation.

• Big data infrastructure setup and support.
• Big data quality and security management.
• Big data capture, analysis and reporting.

• Data preparation and management.
• Development and tuning of machine learning (ML) (including deep learning) models.
• Development and tuning of data mining models.
• Designing and implanting AI solutions.
• Image analysis software development.

• Business intelligence and data analytics infrastructure design and implementation.
• Ad hoc and scheduled analytics querying and reporting.
• Natural language user interface.

• Data gathering and cleansing for ML model training.
• ML modeling.
• Development of software modules that will use ML output.

• Interactive dashboarding.
• Custom and pre-built visuals.
• Multiple visualization techniques (symbol maps, line charts, bar charts, pie charts, etc.)