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Too Slow, Too Costly? Time to Change.
Low performance, low scalability
Slow processing times for data integration and extraction significantly delay decision-making. When you started building your data warehouse, nightly load took 2 hours, are reports were always on time. How did it happen, that months into the project, you don’t get your fresh new report landing on your desk shortly after coming to the office?
Costly & time-consuming maintenance
Without automation and standardized frameworks, each report and each analysis requires dedicated support, leading to costly inefficiencies. At the end of the day, you pay too much in operational costs and wasted resources, with dedicated people & teams for manual monitoring and supervising only, instead of building real value.
Lack of flexibility and best practices
Rigid data models with no best practice in place lead you to barriers in reusing and scaling your data. When you grow your teams, you should be able to onboard them in weeks, not months. And the know-how should not disappear when someone from the BI team leaves the company, either.
High infrastructure costs
Inefficient processes and lack of data lifecycle management cause systems to run at full capacity, inflating your monthly cloud infrastructure costs. Without optimized ELT processes and a proper framework, a data warehouse may need to operate at up to 19 times higher costs per hour than it should.
