Data Optimization and Automation

Inviso’s experts can help you devise and execute an intelligent data strategy so you can gain a critical competitive edge.

Data Modeling

We can help you attain optimal data modeling results. Data modeling is a method of structuring, storing, and securing data optimized for consumption. It’s an essential step of creating any reporting solution. It helps analysts understand the relationships between different sets of data and ensures data integrity. Business intelligence tools like Power BI are now accessible to non-technical users, which has caused some companies to lose focus on architectural components required for a robust reporting solution leading to data errors, automation, and performance issues. We can help you avoid or fix these issues.

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Data Storage

Analyzing how, when, and why your organization wants to use your data helps us determine the best and most cost-effective way to store your data. Our architects use tools and techniques like SQL Data Warehousing and Azure Blob Storage to help you get the most effective use out of your digital information so you can focus on building your business.

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Data Automation

Your organization can gain huge efficiencies by eliminating manual steps and ensuring reliable outcomes. Our developers have a wealth of experience using tools like SQL Integration Services and Azure Data Factory to fully automate the flow of data from points A to Z as well as process any business logic or calculations taking place in between, all while maintaining strict security and following data governance best practices.

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Data Analytics

We use tools like Analysis Services to quickly serve up large pre-processed and pre-aggregated data sets while maintaining strict data governance like row-level-security and role-based perspectives. Because of its robust performance, scalability, and security our Enterprise customers often prefer using Analysis Services Cubes as the backend for their Power BI solutions. Our developers have deep experience building these solutions both on standard SQL Server backends and in Azure as well as using end-user telemetry data to continually improve performance.

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