The tools and techniques of data science and advanced analytics can be used to solve many problems. In some cases – self-driving cars, face recognition, machine translation – those technologies make tasks possible that previously were impossible to automate. That is an amazing, transformative accomplishment. But I want to sing a paean to a mundane but important aspect of data science – the ability to intelligently put lists of things in a better order. For many organizations, once you have found some insights, and are into the realm of putting data products into production, the most substantial value can be found by identifying inefficient processes and making them efficient. Twenty or thirty years ago, that efficiency-gain might have been addressed by converting a paper-based process to a computer-based process. But now, prioritization – putting things in the right order – can be what it takes to make an impact.
Data warehouses are not just for business intelligence (BI) anymore. You can maximize the value of your data engineering, data science, and analytics work by investing in building out a multi-use data-platform that serves business users, Analysts, Statisticians, and intelligent applications. In my last post, data-dies-in-darkness, I described how you can improve your organization’s data quality by exposing more data to more people. You can stretch this idea even farther by expanding the stakeholders of your data warehouse to include intelligent applications.