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Data Analytics Services

Providing data analytics services, ScienceSoft relies on 35 years of experience and mature project management practices. With domain-focused business analysis, transparent pricing, and proactive risk mitigation, our customers get their projects delivered on time, on budget, and within the agreed scope.

Data Analytics Services - ScienceSoft

Alex Bekker

ScienceSoft's Head of Data Analytics Department

Building Data Analytics Solutions for Different Domains

A data analytics company, ScienceSoft helps businesses from 30+ industries integrate, aggregate, and analyze various data types from multiple data sources to address their most ambitious needs at department and enterprise levels.

By industry

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  • 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.
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  • 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.
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Transportation and logistics 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.
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  • Continuous monitoring of bank stability indicators.
  • Institution performance forecasts.
  • 360-degree view of customers.
  • Identifying cross-selling and upselling opportunities.
  • Insights into customer service management.
  • What-if modeling for timely mitigation of market, credit, and operational risks.
  • Fraud detection.
  • Automated compliance checks and non-compliance alerts.
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  • Portfolio performance analytics with asset-specific benchmarking.
  • Factor exposure, performance attribution, and risk attribution analysis.
  • Continuous monitoring of market, credit, and liquidity risks.
  • What-if scenarios under various risk factors and portfolio rebalancing options.
  • Identifying insider trading, pump and dump schemes, HFT manipulation, and other kinds of fraud.
  • Insights into tax management.
  • Continuous compliance monitoring and alerting.
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  • AI-powered insurance recommendations tailored for certain customer segments.
  • Finance analytics with underwriting profitability monitoring and product-specific scenario modeling.
  • Monitoring insurance-related risks with stress testing.
  • Operational analytics insights for improving claims processing, customer service, and other internal processes.
  • Workforce analytics for agents' performance assessment, sales incentive programs adjustment, and top talent retention.
  • Insights into the performance of external agencies and partners.
  • Predictive and prescriptive analytics for insurance planning and optimization.
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  • Providing insights into market trends and analyzing property values to support informed investment decisions.
  • Automated buyer-seller matching and customer-specific property recommendations.
  • Comprehensive portfolio management with expenses tracking and cash-flow forecasts.
  • Calculating and monitoring rental, occupancy rates, and other property performance KPIs.
  • Multidimensional customer segmentation with AI-powered suggestions on segment-specific targeting.
  • Predictive analytics to forecast property value or identify property that is likely to be sold or bought in the near future.
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  • Providing insights into borrowers' creditworthiness and predicting default and NPL risks.
  • Lending products performance analytics.
  • AI-powered recommendations for loan portfolio optimization.
  • Identifying bottlenecks in underwriting, loan approval, servicing, debt collection, and other operational processes.
  • Continuous compliance monitoring and non-compliance alerting.
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  • Continuous monitoring of energy generation and distribution with AI-powered optimization recommendations.
  • Analyzing renewable energy share in the energy grid.
  • Predictive and preventive maintenance analytics.
  • Analyzing energy consumption patterns to enable efficient resource allocation.
  • Predictive analytics to forecast energy demand.
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  • Exploration management analytics to identify optimal drilling locations and estimate oil and gas reserves.
  • Predictive analytics for estimated ultimate recovery and production rates forecasting.
  • Equipment predictive and preventive maintenance.
  • Environmental impact insights.
  • Real-time monitoring of processes and assets (e.g., production, transportation, pipelines, storage tanks, pumping sections) with immediate alerting.
  • Refinery optimization and quality control analytics.
  • Insights into supply chain management.
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Telecoms analytics

  • Continuous network performance monitoring and analytics to forecast excess capacity areas and optimize network capacity.
  • Providing insights into customer management to foresee and prevent churn, tailor offerings, and increase customer retention.
  • Identifying operational bottlenecks and providing AI-powered optimization recommendations.
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  • Analyzing students' and parents' feedback on the quality of teaching and the learning environment to improve the service.
  • Students' performance analytics with alerts on potential intervention.
  • Insights into the patterns of learning platforms usage to enhance teaching and learning outcomes.
  • Enrollment forecasting for resource allocation optimization.
  • Financial analytics with insights into grant revenue, endowment value, cash balance, wages, and more.
  • Analyzing teachers' performance and providing insights into top talent attraction and retention.
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  • Multidimensional customer segmentation to enable automated customer-agent matching, effective ad targeting, and personalized services.
  • Analyzing customer sentiment and satisfaction for data-driven service improvement.
  • Operational analytics to improve staff performance and process efficiency.
  • Predictive analytics to forecast resource needs and optimize staff allocation.
  • Financial analytics based on internal and external data to detect revenue leakage and improve business profitability.
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Travel and hospitality

  • Tracking and analyzing customer interactions, preferences, and feedback to optimize customer relationship management.
  • Operational analytics, including service quality and employee performance analysis.
  • Providing real-time personalized recommendations on destinations, lodging options, events, etc.
  • AI-powered pricing optimization based on market demand, individual customer preferences, and predicted price movements.
  • Analyzing results of promotions, discounts, loyalty programs and their influence on business profitability.
  • Forecasting demand to maximize revenue from hotel rooms, flights, and other related services.
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  • Multidimensional audience segmentation with granular comparisons, e.g., demographic breakdown vs. genre audience.
  • Audience engagement analytics with insights into cross-platform customer behavior and preferences.
  • Real-time personalized content recommendations.
  • Content performance analytics.
  • Forecasting content demand and popularity, including box office success.
  • Tracking the effectiveness of advertising and marketing campaigns with real-time targeting adjustment.
  • Tracking compliance with regulatory standards, including those related to personal data handling.
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By analytics area

  • 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.
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  • Identifying demand drivers, consumer demand forecasting and planning.
  • Supplier performance monitoring and evaluation.
  • Predictive route optimization.
  • Determining the optimal level of inventory to meet the demand and prevent stockouts, inventory planning and management.
  • Identifying patterns and trends throughout the supply chain for enhanced supply chain risks management.
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Sales and product analytics

  • 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.
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  • 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.
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Asset analytics

  • 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.
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HR analytics

  • 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.
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Ready to Discuss Specifics?

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About ScienceSoft

  • 35 years in data analytics and data science.
  • 19 years in business intelligence and data warehouse services.
  • 11 years in big data.
  • years in Microsoft Power BI services.
  • Competencies in machine learning, artificial intelligence, data science, and image analysis.
  • In-house PMO to support large-scale and distributed projects. Expertise in Lean, Agile, and DevOps methodologies. PM certifications (PSM I, PMP, and more).
  • ISO 9001 and ISO 27001-certified to assure the quality of the data analysis services and the security of the customers' data.

How You Benefit from ScienceSoft as Your Analytics Partner

Time-saving automation

We set up automated data management and governance processes and implement self-service BI to ensure you can easily create ad hoc reports without any coding skills, and your IT team doesn’t have to manage data manually.

Easy-to-read reports

We use various data visualization techniques to highlight the most important analytics insights in each report and make them easy to scan at one glance.

Reliable insights due to trustworthy data

We consolidate your disparate data sources into a DWH to serve as a single point of truth for enterprise-wide analytics. Our robust ETL processes will guarantee your data is always accurate, consistent, and complete to facilitate dependable analytics.

Value-focused data analytics

As an expert analytics consultancy, we don’t simply build reports — our goal is to help you fully utilize the potential of your analytics solution and discover new optimization opportunities hidden in your data (e.g., for operational cost reduction, productivity improvements).

Our Data Analytics Portfolio

Quality Services – Happy Customers

We first contacted ScienceSoft to get expert advice on the creation of the centralized analytical solution. After we got a clear project roadmap, we commissioned ScienceSoft to develop a part of the solution, covering invoicing. The system automates data integration from different sources and provides visibility into the invoicing process. We have already engaged ScienceSoft in supporting the solution and would definitely consider ScienceSoft as an IT vendor in the future.

bioAffinity Technologies hired ScienceSoft to help in the development of its automated data analysis software for detection of lung cancer using flow cytometry. Our project required a large amount of industry-specific methodology and algorithms to be implemented into our new software connected to EHR/LIS systems, which ScienceSoft’s team handled well due to a profound understanding of laboratory software specifics and integrations.

We commissioned ScienceSoft to build a flexible database with user interfaces for managing our test data stored as time-based CVS files. ScienceSoft delivered a fully functioning solution regardless of the new requirements that appeared during the project. We are planning to extend the logic of our reports and dashboards and data processing options in our solution, and we’ll definitely be considering ScienceSoft as our partner in this initiative.

How We Ensure Smooth Sailing of Our Projects

With over three decades of experience in analytics services and established project management practices, we drive project goals regardless of time and budget constraints as well as changing requirements. 

We utilize our ISO 9001-certified quality management system throughout the project life cycle.

We assess project risks in advance to provide realistic estimation of time & budget.

We foster cooperation, trust, and respect to achieve effective teamwork.

Being ISO 27001-certified, we guarantee that we collect and store your business data securely.

We maintain and update accurate project documentation to support future software evolution.

We ensure full transparency of project progress with the help of custom KPIs, tailored reporting procedures, and efficient task-tracking systems.

Having worked with 30+ diverse industries, we speak your language and understand your domain's unique challenges and needs.

To ensure high user adoption and smooth knowledge transfer, we are ready to conduct user training for your team.

ScienceSoft USA Corporation Is a 3-Year Champion in the Financial Times Rating

Three years in a row (2022–2024), the Financial Times has included ScienceSoft USA Corporation in the list of 500 fastest-growing American companies. This is the result of our dedication to driving project success despite any constraints and disruptions.

Data Analytics Services & Costs at ScienceSoft

ScienceSoft provides flexible service options to satisfy any data analytics needs. Data analytics services costs may range from $7,000 to $1,000,000+, depending on the service type and the complexity of analytics requirements. The major cost factors include data quality and complexity, data processing specifics (batch or real-time), characteristics of existing infrastructure and data sources, the necessity for big data and ML/AI techs, and more.

Analytics as a Service (AaaS)

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

Costs: $10K–$30K+/mo

About the service

Managed data analysis

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

Costs: $10K+ for one-time service; $7K–14K/mo


About the service

Data analytics consulting

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.

 Costs: $10K–$50K+

About the service

Data analytics implementation

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.

Costs: $30K–$1M+

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Data analytics modernization

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

Costs: $20K–$200K+

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Data management services

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

Costs: $30K–$1M+

About the service

ScienceSoft's Solutions â€“ from Automation to Advanced Data Analytics

Data integration and data warehousing

  • 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.
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Big data

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

  • 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.
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Business intelligence

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

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

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

How to build a data warehouse from scratch: approaches, plan, software, and costs

ScienceSoft shares experience on efficient construction of a data warehouse (DWH): approaches, plan, skills required, software we recommend, and DWH development costs.

Data warehouse software: 5 best data warehousing tools

Get a structured overview of data warehouse software: key features of a DWH system and a list of our proven tools to build a DWH solution.

Best software to build a data warehouse in the cloud: features, benefits, costs

ScienceSoft’s cloud data warehouse consultants present the list of 6 cloud-based data warehouse platforms that cover 99.9% use cases in data warehousing.

Business Intelligence implementation: plan, software, costs, and required skills

ScienceSoft advises on business intelligence implementation in your company: plan, tools, costs, skills for building an effective BI and analytics solution.

Real-time data warehouse: architecture, use cases, and key techs

ScienceSoft’s data engineers describe the architecture of a real-time data warehouse, outline its key components and use cases, and list the most reliable techs.

We are excited to hear about your data analytics needs!

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