BI and DW Modernization

Many companies have had BI and data warehousing installations for years, designed and created with traditional tools and approaches. But at this point they are not delivering the results companies need.  Their existing data warehousing environment makes it difficult to expand to new data sources or provide more sophisticated analytics for the enterprise. 

Perhaps they cannot effectively integrate new cloud-based applications, unstructured data, or big data. Or, maybe they cannot leverage a cloud data warehouse or implement a hybrid environment for a more cost- and resource-effective solution.

The bottom line is that businesspeople aren’t getting all the data they need for their analysis, and what they do have is difficult to work with.

There’s an opportunity to increase the ROI of their BI solution by modernizing it with new concepts and best practices and ensure they’ are no longer being left behind.

A BI and data warehousing modernization effort might include:

• Deploying cloud data warehousing in a hybrid environment or with mixed cloud platforms
• Incorporating self-service BI and data preparation capabilities
• Developing a data integration or data engineering framework with the functionality matching your needs such as ETL, ELT, data pipelines, streaming, API integration, and data virtualization
• Expanding the BI environment to include analytical sandboxes or data science labs (hub) for business analysts, data analysts, data engineers and data scientists
• Adding data lakes, advanced analytical schemas and a logical data warehouse (LDW) using relational, columnar, document and polyglot (or multi-schema) databases to handle unstructured , semi-structured data, and structured data
• Replacing data shadow systems, i.e. the proliferation of spreadsheets used to integration, transform, and analyze data, with more effective capabilities for integration and transformation enabling the business to use spreadsheets for analysis not integration

How we've helped others with their modernization needs

• At this industry research firm, data scientists were spending far too much time gathering data from various sources, cleaning it, importing and exporting, when they needed to spend more time building their predictive models. We created a data science hub that automated recurring data loading and extraction, streamlining their data preparation tasks. They were able to spend less time preparing and far more time analyzing the data.
• An insurance company's data warehouse was showing its age. We created a data virtualization solution that let the business analyze data across various systems without needing to be physically integrated. Some of their data sources would never have been integrated into the old data warehouse, so our solution saved them from having to continue relying on spreadsheets to get their data.