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Data Warehousing Services
Data warehouse services include advisory, implementation, support, migration, and managed services to help companies benefit from a high-performing DWH.
Since 2005, ScienceSoft has been helping its clients consolidate data in an efficient DWH solution and enable company-wide analytics and reporting.
What Makes ScienceSoft a Trustworthy Partner
- Data warehousing services since 2005.
- Data analytics expertise since 1989.
- Designing and implementing business intelligence solutions since 2005.
- A dedicated team of DWH solution architects, data engineers, DevOps specialists, database administrators, QA specialists.
- Expertise in delivering complex and large-scale solutions (incl. real-time data warehouses) for 30+ industries.
- An in-house PMO and established project management practices to achieve project goals regardless of time and budget constraints.
- Quality-first approach based on a mature ISO 9001-certified quality management system.
- ISO 27001-certified security management based on comprehensive policies and processes, advanced security technology, and skilled professionals.
What makes ScienceSoft different
We achieve project success no matter what
ScienceSoft does not pass mere project administration off as project management, which, unfortunately, often happens on the market. We practice real project management, achieving project success for our clients no matter what.
Data Warehouse Services by ScienceSoft
Data warehouse advisory services
ScienceSoft’s team designs a data warehouse and plans out its implementation as well as renders advisory support while migrating or upgrading your legacy solution to optimize DWH performance and costs.
Service details
ScienceSoft’s team builds a DWH tailored to your unique data consolidation and storage needs and implements it into your ecosystem.
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Data warehouse migration
ScienceSoft helps you optimize DWH performance and lower total cost of ownership by moving your existing on-premises data warehouse to the cloud with no business process disruptions.
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ScienceSoft offers a comprehensive DWH testing set, which can include ETL/ELT testing, BI testing, DWH performance testing and security testing.
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Data warehouse support
ScienceSoft provides DWH support to help you identify and solve DWH performance issues, achieve DWH stability for timely and quality data flow for business users, lower DWH storage and processing costs.
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ScienceSoft rents out a full-scale data warehouse integrated with your existing BI and analytics infrastructure on a subscription fee basis.
Service details
How Much Will Your DWH Project Cost?
Estimate the Cost of Data Warehouse Services
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In the meantime, would you like to learn more about ScienceSoft?
- Project success no matter what: learn how we make good on our mission.
- 35 years in data management and analytics: check what we do.
- 4,000 successful projects: explore our portfolio.
- 1,300+ incredible clients: read what they say.
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.
ScienceSoft’s Data Warehouse Portfolio
Why Build Data Warehouse Solutions with ScienceSoft
Get DWH Solutions Tailored to the Specific Needs of Your Industry
Below, we provide domain-specific examples of data that can be consolidated in a data warehouse to ensure comprehensive analytics and insightful reporting.
- EHR data, including patients’ medical history, diagnoses, medications, treatment plans, and lab results.
- Patient-generated health data (PGHD).
- Medical imaging data, including X-rays, MRIs, CT scans, etc.
- Billing and claims data.
- Data on asset utilization, status, and location.
- Data on patient-hospital interaction.
- Population health data.
- Clinical trial and genomic data.
- Laboratory management and test results data.
- HR data, including scheduling, employees’ performance, and compensation.
- SCM data.
- Equipment performance and event data.
- Full-cycle production data from the required systems, including HMI, PLC, SCADA, and MES.
- Data on asset condition, lifecycle, and current value.
- Financial performance data.
- Customer communication history, order details.
- Supplier, procurement, inventory, warehouse, and logistics data.
- Employees’ performance data.
- Data on financial transactions.
- Customer accounts balance and activities.
- AR/AP, sales, cash flow, and other financial management data.
- Customer demographics.
- Borrowers’ credit scores and financial statements.
- Data on the usage of banking products and apps.
- Customer feedback and other bank-customer interaction data.
- Marketing campaigns performance data.
- Data from financial marketplaces, including currency exchange and inflation rates, stock quotes.
- Data on banking environment security, e.g., audit trail data, history of data transfer, and user logins.
- Credit rating scores.
- Business financial statements.
- Loan portfolio data.
- Data on loan servicing.
- Data on loan repayment transactions and their status.
- Borrower data, including demographics, income, location, company size, industry, etc.
- Data on hedging strategies, collateral, and securities.
- Data from financial data marketplaces, e.g., market interest and currency exchange rates, real estate value.
- Data on insurance products, e.g., policies and their terms, prices.
- Claim management data, e.g., insurance type, claimant, damage, settlement status.
- Customer-related data, including demographics and inquiries history.
- Data on agents’ activity.
- Financial management data, including sales, payroll, received premiums, paid claims.
- Data from IoT, computer vision, and asset monitoring systems of the insurer, commercial customers, third-party telematics providers.
- Data from the internal systems of credit rating bureaus, medical information bureaus, social security administration, police administration.
- Data on portfolio asset allocation.
- Billing, payouts, and taxation data.
- Data on client communications, demographics, preferences.
- Data on investment deals and transactions.
- Data on selling-purchasing operations.
- Data on received and due payments.
- Capital market data, e.g., stock prices, bond yields, currency exchange rates.
- Data on market, credit, and liquidity risk factors.
- Inventory management data, including stock levels across storage and selling locations.
- Data on customer demographics, preferred payment and shipment methods.
- Customer sentiment data driven from surveys, service-related interactions, and social media content.
- Data on customer behavior online and in brick-and-mortar stores.
- Shopping card data (for ecommerce stores).
- Payment transactions data.
- Order fulfillment data, e.g., product demand, order fulfillment status.
- Data on marketing campaigns.
- SCM data, e.g., supplier capacity and performance.
- Telematics data, e.g., real-time data on vehicle location and state, fuel consumption.
- Data on cargo condition.
- Driver behavior data.
- Weather and traffic data.
- Data on customer preferences, order history, and feedback.
- Personnel schedules data.
- Inventory levels and demand data.
- Financial management data, including cost, cash flow, revenue, profit, and payroll data.
- Data from property research and internet listing services, e.g., property listings and features, prices, availability.
- Historical customer-property matching data.
- Data from GIS and public services, e.g., spatial data, data on geographical boundaries and zoning.
- Customer-related data, including demographics, property inquiries history, communication logs.
- AR/AP data, financial statements.
- Property management data, e.g., info on tenants, rental income, lease terms, maintenance requests.
- Customer-generated content like reviews and social media comments.
- Data on construction management, e.g., project progress and costs, resource utilization.
- Data on regulation related to zoning and land use, environment, and accessibility standards.
- Smart meter data, e.g., on energy consumption, peak demand periods.
- Grid data, e.g., grid performance, voltage levels, outage events, load distribution.
- Weather data.
- Data on asset condition, performance, and maintenance history.
- Renewable energy data.
- Data on customer demographics, preferences, and energy usage patterns.
- Financial management data, e.g., on revenue, expenses, and profitability.
- Environmental impact data, including carbon emissions, water usage, and waste generation.
- Seismic and microseismic data on underground rock formations.
- Historical drilling and exploration data.
- Data on reservoir characteristics like fluid and phase behavior, hydrocarbon saturation, porosity.
- Operational data from drilling and production equipment.
- Data on equipment status and maintenance schedules.
- Environmental impact data, e.g., greenhouse gas emissions, water usage, waste disposal.
- Refinery data.
- Data on supply chain management.
- Customer-related data, including demographics, preferences, feedback.
- Network data, e.g., capacity utilization, maintenance activities, outages, latency.
- Billing data, including billing records, pricing plans, discounts, payment history.
- Call detail records, including info on call duration, type, location, and quality.
- Real-time and historical equipment data.
- Data on competitor activity.
- Data on compliance regulations.
- LMS data, e.g., online course completion rates, engagement metrics, utilization of digital learning tools.
- Student-related data, e.g., demographics, academic performance, attendance records, learning styles.
- Data related to teaching personnel, including qualifications, performance evaluations, professional development dynamics.
- Curriculum data like lesson plans, learning objectives, assessment results.
- Data on parent engagement, e.g., communication logs, meeting records, surveys.
- Data on the climate in an educational institution, e.g., surveys, discipline incidents, bullying reports.
- Financial data, including budget allocations, grant funding management, expenditure tracking.
- Data on customers, including demographics, preferences, interactions.
- Customer feedback data, e.g., client satisfaction surveys, feedback comments, testimonials, online reviews.
- Operational data, including resource allocation, budgets, tasks completion.
- Employee data, e.g., skills and qualifications, productivity, training records.
- Data on service delivery.
- Time-tracking data like billable and non-billable hours, time spent on certain tasks.
- Marketing-related data, including info on lead generation sources, conversions, marketing campaigns performance.
- Financial management data, e.g., revenue streams, invoicing cycles, AP/AR data.
- Knowledge management data like KB usage metrics, knowledge sharing activities.
- Customer demographics and interactions data.
- Customer feedback data from surveys and online review platforms.
- Data on bookings, reservations, and cancellations.
- Financial data, including sales and revenue.
- Operational data, including info on room occupancy, inventory levels, staff performance.
- Data on advertising channels and marketing campaigns.
- Data on competitors’ offerings and pricing strategies.
- Audience demographics data.
- Data on audience engagement, including viewership, ratings, likes, clicks, shares.
- Data on audience sentiment from social media networks, online review platforms, and surveys.
- Data on financial management, e.g., profitability, revenue attribution, production budgeting.
- Content consumption data, including info on watch time, device usage.
- Advertising and monetization data, e.g., ad impressions, conversions, revenue from ads and subscriptions.
- Data on market trends and competitor activity.
With dozens of DWH projects in our portfolio, we still haven’t had a chance to say: “Oh, it’s just like that X project, remember? Let’s use it as a template!” And honestly, I don’t think this will ever be the case. Even same-industry companies face unique challenges, which requires different approaches to make data work. I believe that tailoring DWH solutions to specific business concepts and processes is what drives customer satisfaction with ScienceSoft’s services.
Our Clients Say
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.
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.
bioAffinity Technologies hired ScienceSoft to help in the development of its automated data analysis software for detection of lung cancer using flow cytometry. In addition to the solid technical expertise shown by ScienceSoft, its developers demonstrated a profound understanding of laboratory software specifics and integrations. We would recommend hiring ScienceSoft to anyone looking for a highly productive and solution-driven team.
Technologies We Use
Our Cooperation Highlights
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Service deliveryTo maximize the value of our services, ScienceSoft:
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Flexible pricing models
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Data Warehousing Services FAQs
What if our data is voluminous? Do you have experience in big data?
ScienceSoft is equally proficient in working with both traditional and big data. We have 11 years of experience in end-to-end big data services, including big data analytics and visualization.
How to ensure our employees actually use the DWH?
Making it easy to reach company-wide DWH adoption is one of our priorities. To achieve this, we design DWH capabilities with unique user needs in mind. For instance, we enable zero code reports creation for BI users with a limited tech background and ensure easy solution navigation. We also create detailed software documentation and provide training for your internal teams.
We implemented a DWH. What’s next?
After implementing a data warehouse, it’s crucial to keep it high-performing and stable and ensure its capabilities correspond to the changing needs of your organization. This is achieved through ongoing DWH maintenance (e.g., continuous monitoring and adjustment of hardware and software configurations) and timely evolution (e.g., adding new data sources, data models, and reports).
What is the difference between a data warehouse and a database?
The difference between a data warehouse and a database is in the nature of data the storages handle and the purposes they serve. A data warehouse stores highly structured, pre-processed data from multiple sources to enable its analytics via BI reports and queries. A database handles real-time operational and transactional data from one application to enable app transactions.
What are the leading DWH vendors?
According to the 2023 Forrester Wave Report, Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse Analytics are singled out as leaders and strong performers. ScienceSoft widely uses these platforms in our practice. Visit our dedicated page to learn more about their specifics.
Consider ScienceSoft as Your Reliable Technology Partner