Sep 17, 2021
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?