The data warehouse has been an analytics workhorse for decades for business intelligence teams. Unprecedented volumes of data, new types of data, and the need for advanced analyses like machine learning brought on the age of the data lake. Now, many companies have a data lake for data science, a data warehouse for BI, or a mishmash of both, possibly combined with a mandate to go to the cloud. The end result can be a sprawling mess, a lot of duplicated effort, a lot of missed opportunities, a lot of projects that never made it into production, and a lot of financial investment without return. As time passes, companies are finding ways to combine the strengths of these two strategies and mitigate the weaknesses, inventing whole new ways to analyze data. Machine learning, advanced analytics, the movement to the cloud – these are all changing data architectures in unexpected ways.
- *Consider successful data architectures from companies like Philips, Simpli.fi, and The TradeDesk
- *Discuss the shifts in recent years as data lakes and data warehouses race to the middle
- *Dive into the impact advanced analytics, machine learning, and shifting analytics strategies are having on a wide range of industries
- *Look at how the movement to the cloud affects analytics, is everyone really going to the cloud?