Data Warehouse Pricing
How to Plan Your DWH Project Budget
In data warehouse development since 2005, ScienceSoft outlines the main cost factors of DWH implementation to help businesses in 30+ industries confidently plan their data analytics initiatives.
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Data Warehouse Cost in Brief
The major factors influencing the cost of data warehouse implementation include the number of data sources, the diversity of data types, the data processing specifics (batch or real-time), the chosen architecture, the tech stack, and the deployment model. The requirements for analytics & reporting complexity, data quality management, security, and regulatory compliance also influence the TCO.
Depending on these variables, the cost of data warehouse implementation can range from $30,000 to $2,000,000.
On average, data warehouse implementation brings a 400% five-year ROI with a payback period of 9 months and leads to a 30% increase in the productivity of data and analytics teams.
Key Factors Defining Data Warehouse Cost
Data warehouse pricing depends on particular business purposes and the solution’s technological complexity. Below, our experts outline the main factors to consider when planning your DWH implementation budget. To make the cost ranges easy to compare, we outlined three major DWH pricing brackets: from a basic system to a large-scale enterprise data warehouse.
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Basic data warehouse |
Data warehouse of medium complexity |
Advanced data warehouse |
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Data sources
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Up to 5 internal sources (e.g., ERP, CRM, SCM). |
All the required internal sources. |
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Data diversity and complexity
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Structured data arriving at scheduled intervals. |
All data types (structured, unstructured, and semi-structured) arriving at scheduled intervals. |
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Data processing specifics
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Batch data processing (e.g., once every 24 hours). |
Batch and real-time processing. |
Batch and real-time processing. |
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Data quality management
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Manual. |
Partially automated. |
Maximally automated. |
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OLAP tools
?
Online analytical processing (OLAP) tools present data in multiple dimensions, enabling users to consolidate, drill down, slice, and dice it from various perspectives. |
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Reporting
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Via market-available reporting and visualization tools (e.g., Microsoft Power BI, Tableau). |
Via market-available reporting and visualization tools (e.g., Microsoft Power BI, Tableau). |
Via market-available reporting and visualization tools + custom data visualization modules. |
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Analytics complexity
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Rule-based analytics. |
Rule-based and ML/AI-powered analytics. |
Rule-based and ML/AI-powered analytics, including real-time and big data analytics. ML training modules for continuous updating of ML models. |
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Cost
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$30,000–$150,000 |
$150,000–$600,000 |
$600,000–$2,000,000+ |
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Additional Cost-Defining Factors
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Data volume. |
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The number of BI users (overall, daily, concurrent). |
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The diversity of user roles (C-level, analysts, data scientists, department-specific BI users, etc.). |
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DWH deployment format (on-premises, cloud-native, cloud-only, hybrid). |
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Data security requirements (e.g., end-to-end data encryption, row-level security). |
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Fault-tolerance, scalability, and availability requirements (e.g., data redundancy, data backup mechanisms & backup frequency, failover mechanisms, DWH monitoring and alerting systems). |
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Regulatory compliance requirements (e.g., HIPAA, PCI DSS, GDPR). |
Data Warehouse Implementation: Cost vs. ROI
When optimizing DWH implementation costs, it may be tempting to give up advanced capabilities like automated data quality management. However, features requiring substantial initial investments may secure lower operating costs and higher productivity of the entire system in the long run. For instance, implementing an efficient cloud DWH with a feasible degree of data management automation can bring up to 400% five-year ROI with a payback period of around 9 months, driven by a 30% increase in the productivity of data management and analytics teams. Thus, the benefit/cost ratio and ROI are essential factors to consider when planning your DWH budget.
How ScienceSoft Optimizes DWH Development Costs Without Compromising Quality
Implementing DWH and BI solutions for optimized data storage and enterprise-wide decision-making since 2005, ScienceSoft knows how to build a robust data warehouse that drives maximum value at an optimal cost.
Expert data sources audit
We exclude redundant data sources (e.g., due to their little relevance for BI purposes or duplicate information), which allows our customers to avoid expenses on unnecessary integrations, capacity, and quality management procedures.
Prompt MVP delivery
With a data warehouse MVP, you can start gaining payback and gathering user feedback long before the release of the full-featured solution.
Vendor neutrality
Having partnerships with AWS, Azure, Oracle, and other global tech leaders, we opt for the technologies that will bring the most value in your case, considering the data volume and analytics requirements and the existing tech stack.
Implement a Data Warehouse With Experts
In data analytics services since 1989, ScienceSoft has implemented specialized solutions for a broad range of demanding industries, including healthcare, manufacturing, BFSI, retail, telecoms, and more. Our analysts, data scientists, and software engineers are ready to share their experience to implement a tailored DWH for your unique needs.
DWH implementation consulting
We understand that DWH implementation is a large undertaking that requires careful planning. ScienceSoft’s experts will conduct a feasibility study, design the DWH architecture, find the optimal tech stack, and estimate the project cost and ROI to help you avoid unnecessary risks.
DWH development
Finding a reliable vendor for any large-scale IT initiative is half the battle won. Let us prove our expertise in practice: ScienceSoft is ready to develop a cost-effective implementation plan and deliver a high-performing solution that will address your current business needs and will be easy to evolve in the future.
Our Customers 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. 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.
About ScienceSoft
ScienceSoft is a global IT consulting and software development company headquartered in McKinney, Texas. Since 2005, we’ve been helping our customers implement secure, scalable data warehouses with the optimal cost-benefit ratio. Relying on ISO 9001 and ISO 27001-certified management systems, we guarantee high service quality and full security of our customers’ data.