Thursday – The Cost of Doing DataOps Wrong

Modern DataOps encompasses techniques from many diverse disciplines as well as experiences learned by the various practitioners. What happens when these best practices are ignored, and these lessons are not learned? Let’s take a stroll through a little history and look at experiences when DataOps is done right and done wrong.
Who should pay attention: Individuals involved in data management such as data engineers, data scientists, project managers, and those responsible for implementing data operations strategies should pay attention to the repercussions of mishandling DataOps.
When it’s a problem: Mishandling DataOps becomes problematic when organizations fail to learn from best practices and experiences, leading to inefficiencies, errors, and hindered data workflows.
Where it manifests itself: The negative impact of neglecting DataOps practices can emerge in delayed data processing, compromised data quality, increased operational costs, and suboptimal decision-making processes within an organization.
How to approach or mitigate the issue: Mitigating the issues associated with mishandling DataOps involves implementing robust DataOps frameworks, fostering collaboration between data teams, leveraging automation for data pipelines, ensuring data quality control measures, and continual learning and improvement.
Why it matters: Effective DataOps practices are vital for maximizing the utility of data, enhancing operational efficiency, making well-informed data-driven decisions, and gaining a competitive advantage in a data-centric business environment. Ignoring these practices can result in financial setbacks, missed opportunities, and diminished competitiveness in the market.

What you will learn:
1) The consequences of neglecting DataOps best practices
2) Lessons learned from past experiences of unsuccessful DataOps implementation
3) Importance of applying diverse techniques in Modern DataOps for successful outcomes

Topics Covered
Importance of proper DataOps implementation for data management professionals
Risks and consequences associated with neglecting DataOps best practices
Lessons from unsuccessful DataOps implementations and historical perspectives
Strategies for addressing and mitigating issues related to mishandling DataOps

Speakers