Turning the Lights On Data with George Firican

Listen to this episode on Anchor FM

If data isn’t managed, the repercussions spread throughout the organization, from reports to decisions. In this episode of the DATAcated on Air podcast, host Kate Strachnyi talks with George Firican, founder of LightsOnData. They touch on data quality, data storytelling, data stewardship, data governance, and surfing. George began in computer science by working for a start-up. That job spurred his career in interacting with data and working with databases. George loves educating people about data, so listen in to learn more!

You will want to hear this episode if you are interested in...

  • George’s journey to data [03:33]
  • What is LightsOnData? [09:44]
  • The LightsOnData show [18:25]
  • Latte’s and LinkedIn [27:01]
  • Tips for starting a podcast [29:15]
  • The impact of poor data quality [35:08]
  • Addressing the data issue [38:36]
  • Why do we have poor data quality? [42:02]
  • George’s typical week [47:33]


Initially, LightsOnData’s focus was on consulting, and training was secondary. When the pandemic hit, the focus shifted towards training and educating. George found that he thoroughly enjoyed the process of helping people understand data. Over time, he has done more and more of the training and less and less of the consulting work. He started providing educational courses, templates, resources, and ways to learn more about data management and governance.

George learned data by reading through many books, taking courses, and attending conferences. The available content was primarily theoretical, so he had to figure out how to utilize that new information when he went back to work. George wanted to create something better than his own journey to help others in theirs. His goal with the courses at LightsOnData is to put together a demo tutorial so that the attendees can immediately apply whatever they’ve learned.

Poor data quality

The impact of poor data quality goes far beyond the data itself. An expensive example is NASA’s $125 million Mars Orbiter which crashed in 1999 due to failing to convert English units of measurement and the metric system successfully. The data might not have been insufficient if it had been consistent, but it was clear that both entities involved didn’t have data governance to make them aware of the issue. There were assumptions made on both sides, which cost millions of dollars.

A study in 2016 found that in the US alone, the cost of poor data quality costs businesses around $3 trillion a year. Poor data quality influences a lot more than finances. In healthcare, poor data quality can lead to death by something being misinterpreted, mistyped, or the wrong medicine dosage to address an issue.

Using clean data

Companies like to focus on cool, shiny things like data science, AI, and machine learning without paying attention to the underlying data, which tends to be something that isn’t professionally managed. Resources aren’t being invested into data quality, and companies are dealing with the aftereffect. A perfect example is when managers are pleased about their data but don’t fully understand the teams behind that report. The teams are manually going into the system and ensuring that some data transformation and cleaning are happening. That means the reports might be in order, but there’s a lot of human power involved in generating them. They should be tackled at the source.

Data reliability, data observability, and data mesh will help make more people aware of the data quality issue. Implementing a new ERP or CRM provides the opportunity to surface more of the data and have people interact with it. Data mesh, data observability, and a data catalog will bring the data to multiple employees rather than the handful it was limited to before. Now there are more opportunities and voices to raise issues. Knowing that there is an issue is helpful, but something needs to be done with it. Instead of having someone clean data every time, why not have a program that tackles it at its source when possible?

Resources & People Mentioned

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