Over the past eight or nine years, applying DevOps practices to various areas of technology within business has grown in popularity and produced demonstrable results. These principles are particularly fruitful when applied to a data analytics environment. Bob Eilbacher explains how to implement a strong DevOps practice for data analysis, starting with the necessary cultural changes that must be made at the executive level and ending with an overview of potential DevOps toolchains. Bob also outlines why DevOps and disruption management go hand in hand.
- The benefits of a DevOps approach, with an emphasis on improving quality and efficiency of data analytics
- Why the push for a DevOps practice needs to come from the C-suite and how it can be integrated into all levels of business
- An overview of the best tools for developers, data analysts, and everyone in between, based on the business’s existing data ecosystem
- The challenges that come with transforming into an analytics-driven company and how to overcome them
- Practical use cases from Caserta clients
About Bob Eilbacher:
Bob Eilbacher is the Vice President of Operations at Caserta. An experienced operations and client services professional with a successful track record of providing technology solutions and services that focus on uncovering analytics insights and driving efficiency across an enterprise, Bob works directly with clients to develop strategies and implement solutions that transform structured and unstructured data into analytics-driven business insights. He has a strong background in technology and a deep appreciation for finding the right solution. Previously, he held executive roles at Verint and Ness Technologies.