$100.00
Data Analytics is no longer optional — it’s a core component of modern business strategy. From uncovering insights to forecasting future outcomes, it empowers organizations to make smarter, data-backed decisions that lead to success.
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Portfolio & Awards
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Pricing Table
| Features |
Small $9,500 / total Initial Business Idea Analysis and Documentation |
Medium $20,000 / total Solution Analysis Vision & Scope |
Large $35,000 /total Solution Analysis Vision & Scope (large-scale) |
|---|---|---|---|
|
Discovery Timeline |
up to 3 weeks |
from 4 to 7 weeks |
from 6 to 9 weeks |
|
Define Business goals and objectives of the expected product |
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Requirements Elisitation Sessions (stakeholders interviews, workshops) |
up to 5 workshops |
up to 7 workshops |
up to 9 workshops |
|
Requirements Modeling and Documentation |
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Quality Attributes Workshop |
– |
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Business Requirements Specification |
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Competitor Analysis |
– |
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Product Succcess Metrics |
– |
– |
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Functional Decomposition |
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Key Business Processes & Sequence Diagrams |
– |
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Non-Functional requirements analysis and documentation |
– |
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Software Architecture Vision and High-level Design |
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Entity Relationship Diagram |
– |
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Technology Stack |
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Technical Risks Identification |
– |
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Architecture decisions report |
– |
– |
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Wireframes |
up to 3 core user flows |
up to 10 core user flows |
up to 15 core user flows |
|
Product Information Architecture |
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Business Model Canvas |
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Customer Journey Map |
– |
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Value Proposition design Workshop |
– |
– |
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Delivery Time |
– |
– |
– |
Description
Data Analytics is the process of examining, transforming, and interpreting data to uncover useful information, patterns, and insights that support decision-making. With the increasing availability of big data, businesses rely on analytics to drive performance, optimize processes, and gain competitive advantage.
🔍 What is Data Analytics?
Data analytics involves using statistical techniques, algorithms, and software tools to make sense of raw data. It turns data into actionable knowledge across various domains, including marketing, finance, healthcare, logistics, and more.
📚 Types of Data Analytics
- Descriptive Analytics: Explains what has happened based on historical data.
- Diagnostic Analytics: Analyzes why something happened by identifying patterns and causes.
- Predictive Analytics: Forecasts future trends using statistical models and machine learning.
- Prescriptive Analytics: Recommends actions based on possible outcomes and optimization models.
🛠️ Common Tools for Data Analytics
- Microsoft Excel – For basic analysis and visualization
- Power BI / Tableau – For advanced data visualization and dashboards
- SQL – For querying and managing structured data
- Python / R – For scripting, statistics, and machine learning
- Google Analytics – For website and digital marketing analysis
- SAS / SPSS – For statistical modeling and enterprise-grade analytics
💡 Benefits of Data Analytics
- ✅ Informed decision-making with real-time data
- 💰 Cost reduction by identifying inefficiencies
- 📈 Improved customer targeting and personalization
- ⏱️ Faster response to market trends and consumer behavior
- 🔒 Enhanced risk management and fraud detection
🏢 Business Applications
- Retail: Customer segmentation, inventory management, dynamic pricing
- Healthcare: Patient monitoring, diagnosis predictions, operational efficiency
- Finance: Credit scoring, algorithmic trading, risk modeling
- Marketing: Campaign performance, ROI analysis, lead conversion optimization
- Manufacturing: Predictive maintenance, supply chain forecasting
📈 Key Metrics and KPIs
- Revenue growth
- Customer acquisition cost (CAC)
- Customer lifetime value (CLV)
- Churn rate
- Operational efficiency ratios
📊 Data Analytics vs. Business Intelligence (BI)
While both are closely related, Business Intelligence focuses on descriptive and diagnostic analytics — reporting what has happened and why. Data Analytics extends further into predictive and prescriptive areas, using more advanced techniques and machine learning.
