Many companies have had BI and data warehousing installations for years, designed and created with traditional tools and approaches. At this point the results they deliver are mixed. Perhaps they can’t handle the unstructured nature of big data, or aren’t tapping into the new cloud-based applications the company is now using. Business people 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’re no longer being left behind.
A BI and data warehousing modernization effort might include:
- Incorporating self-service BI and analytics
- Adding an analytical sandbox
- Adding a data science lab (hub)
- Leveraging unstructured (big) data with the data warehouse
- Leveraging data from cloud applications such as Salesforce, Marketo and Concur
- Replacing data shadow systems
- Creating a logical data warehouse
- Using data virtualization techniques
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.