Data insights can only be as good as the quality of the data they’re based on. Inaccuracies and biases in your data can result in costly mistakes. In this talk, I highlight the typical lifecycle of data and the phases where bad data sneaks in. I also cover ways to prevent, diagnose & fix issues.

----

Businesses rely on good data to make thoughtful decisions. When that underlying data is of poor quality, it can lead to unexpected and costly outcomes. Thus, it is very important to prevent bad data from occurring in the first place, by understanding how it gets created. In this talk, I will explain the typical lifecycle of data, and walk through the phases where bad data gets introduced. Using relatable examples, I will explain the nuances of catching bad data early in the pipeline. I’ll also cover ways to diagnose common data problems, to find the root cause, and highlight ways to fix those issues. The talk provides a blueprint for anyone who works with data, to be proactive and intentional about prioritizing data quality.

To some it up, my key takeaways from the talk will include:
1) What is bad data and why should we care about fixing it?
2) How can we prevent bad data from occurring?
3) How can we diagnose conditions that resemble poor data quality?
4) How can we cure and fix bad data?

I have seen data at scale and all the products I’ve worked on had at least 40M+ users. Thus I’ve seen the impact of what bad data can do. Over the years, I’ve developed a robust framework to narrow down data issues quickly so that they can be fixed. I’ve also developed policies around checking for bias in data, that will help businesses be more thoughtful in their data solutions.

I have given plenty of variations of this talk, and just in the last year 2 of those sessions were billed as keynotes. I am able to adapt this talk to a more technical overview, a more business overview, or a mix of both depending on the audience.