We talk with DataStax Vanguard Lead, Chelsea Navo about how the Vanguard team and DataStax helps enterprises transform to meet the challenges posed by disrupting technology and competitors. 

Highlights!

0:15 - Introducing Chelsea and her elite team of architects (aka the DataStax Vanguard team)

1:57 - Defining enterprise transformation: companies are simplifying complex enterprise architectures. Many companies are looking outside their own industries, sharing ideas such as breaking down data silos and streamlining batch processing.

4:00 - Common trends include retooling batch workloads to meet real-time requirements, reworking systems to handle large data sets and streaming data.

6:05 - One very common use case is Customer 360, which is an enabler for many other use cases

7:37 - Techniques: teams around the world and across industries when they invest in people and getting them trained up on cutting-edge technology, whether in person or online resources like DataStax Academy

9:46 - Chelsea argues for the value of getting small “skateboard” solutions in production vs the traditional proof of concept (POC)

14:17 - Follow on use cases after Customer 360 typically include personalization and fraud detection

16:27 - Common challenges in enterprise transformation include the data platform and data modeling, but also lacking complete knowledge of the current state architecture, and knowing where to start

19:25 - The people challenges in enterprise transformation are important as well. Having an executive champion backing the project is key. Helping people to change their mindset and understand that change is good for careers is equally important

23:25 - The mindset shift is mostly focused around technology and process - domain model expertise is as important as ever, or even more important as we bring UX design into the mix

25:56 - Even if you aren’t in a large enterprise, the data layer is still typically the hardest part of your application

WATCH

Speakers

Chelsea Navo

Vanguard Lead Presales Architecture at DataStax