Insightful Data Quotes and Analogies That Will Have You Bursting With Laughter!
Laugh with Data!
Laughter can transform life on many different levels. Laughter is indeed the best medicine. In the next section are some humorous, insightful quotes and analogies with explanations around data and topics related to data―data quality, metadata, data lineage, data science, and data governance, that will hopefully brighten your day and make you laugh.
Data Quotes and Analogies
“If data had mass, the earth would be a black hole.”
―Stephen Marsland
Interesting isn’t it? With so much data around us, we have to be thankful that data is weightless. 😂
‘Data is like the milk in a latte.’
As explained by @George Firican in his LinkedIn Post, milk adds flavour and texture to coffee; on similar lines, data can add context and depth to a situation or problem, helping provide a more complete flavour of understanding.
“Data without metadata is like a supermarket full of tins with no labels.”
―Jonathan Sunderland
Hilarious comparison! Metadata is data about data. If tins in supermarket do not have labels, it is impossible to tell what they contain; similarly it is hard to understand data and their context without metadata.😂
“Data is a chemistry set. If you combine it in the right way, filter it, and warm it the right way you get insights.”
―Martin Telefont
You have to prepare the data, filter out bad data, correct data issues and consolidate the data correctly and process it the right way to make informed decisions. 😂
“Contact data ages like fish not wine…it gets worse as it gets older, not better.”
―Gregg Thale
Hilarious comparison, but so true too! Wine gets better with age, but fish emits a bad odor as time passes; same goes with contact data. With time, contact data (like phone numbers, addresses etc.) get outdated —so it kind of stinks! 😂
“Expecting data engineers to fix data quality is like asking your plumber to give you clean water.”
―Jonathan Sunderland
One needs to find out the root cause of the data quality issues. Bad data can happen due to number of reasons — it could a human data entry error, faulty business process, or due to data aging, to name a few. In such cases data engineers are not in a position to fix the data quality issue.
“We knew we needed data governance but ended up with the data police.”
―Guy Bradshaw
Data governance is the exercise and enforcement of policies, processes, guidelines, rules, standards, metrics, controls, decision rights, roles, responsibilities, and accountabilities to manage data as a strategic enterprise asset.
However, data governance is often seen as unnecessarily restrictive and is viewed as the data police.😂 Also, most organizations have experienced failed data governance programs, and hence data governance has a bad reputation.
However, data governance is important. With the importance of data increasing exponentially, and compliance and analytics calling for better quality data and improved data protection, data governance is a “must have”.
So how do organizations go about implementing data governance? This brings us to the next quote.
“The First Rule of Data Governance is: Don’t Talk About Data Governance.’”
―Tina Sebert
As I explained earlier, data governance has a bad reputation and organizations are skeptical about it. Hence, for data governance to be valued within an organization, it’s necessary to get creative when speaking about it. Don’t use the term “data governance”. Instead focus on the business value of data. As stated by Tina Sebert in her blog article, ‘The First Rule of Data Governance is: Don’t Talk About Data Governance’:
“…to get other departments excited about the value, you need to tell stories with data; talk impacts, customers, products. The focus needs to be on the business value of data.”
“Data lineage is like a family tree but for data.”
As stated by @Xavier de Boisredon, data lineage traces the relationships between data assets. Data is quite a traveler and travels through a lot of systems in an organization. Data lineage helps trace the data back to the source. In @Xavier de Boisredon’s words,
“Finding a data scientist is hard. Finding people who understand who a data scientist is, is equally hard.”
―Krzysztof Zawadzki
This is so very true! There is a lot of confusion around what is involved in data science, the job description, the roles and responsibilities of a data scientist and how it is different from the other roles like data analyst and data engineers. Data science is a complex discipline and touches several disciplines- mathematics, statistics, programming, machine learning, business analysis and data scientist needs to have all of these skills plus soft skills. Hence, it is hard to find a data scientist. This brings to my mind the next insightful and hilarious quote by Cassie Kozyrkov.
“Buyer beware: there are many data charlatans out there posing as data scientists. There’s no magic that makes certainty out of uncertainty.”
Concluding Thoughts
I hope you enjoyed these quotes or at the least, some of these tickled you a little. Don’t forget to share any other funny data quotes and analogies in the comments below.
In order enjoy more such quotes, read the book- Data Humour: Funny Data, Big Data, Statistics, and Data Science Quotes, Puns, and Punchlines.
If you have any questions or any inputs you want to share, just comment or connect on LinkedIn.
Biography: Rupa Mahanti is a consultant, researcher, speaker, data enthusiast and author of several books on data (data quality, data governance, and data analytics). She is also publisher of the “The Data Pub” Newsletter on Substack.
