The title of my recent blog post Chaos in the Big Data Brickyard made Mike Wheeler think it was a reference to the Indianapolis Motor Speedway, which is known as “The Brickyard” because it was paved entirely with bricks way back in 1909 (today, three feet of the original bricks remain at the start/finish line). This was a reasonable assumption by Wheeler since he is a NASCAR fan (thus making his last name a great example of an aptronym) and thus prompted his blog post Yeah, But Who Won The Race?
“The term brickyard taken without any context,” Wheeler explained, “turned out to be another random brick of fact laid on an already crowded foundation. Context is what provides relevance to facts. Without a frame of reference into which a fact can be inserted it can easily become meaningless or, even worse, detrimental to the decision-making process.”
As usual, I agree with Wheeler (except about being a NASCAR fan — my apologies to Mike and his fellow auto racing fans).
In my post Big Data, Sporks, and Decision Frames, I blogged about how having the right decision frame (i.e., understanding the business context of a decision) is essential to whether big data and data science can provide meaningful business insight.
Data modeling is still important and data quality still matters. As does metadata, data management and business intelligence, data monitoring, communication, collaboration, change management and the many other aspects of data governance.
“A successful man,” David Brinkley once said, “is one who can lay a firm foundation with the bricks others have thrown at him.” A successful big data initiative is one that can lay a firm foundation with the bricks of best practices that the data management industry has been rightfully throwing at us for a long time now. Big data does not obviate the need for those best practices — even though it does occasionally require adapting our best practices as well as adopting new practices.
Big data is not the be all and end all, as it is sometimes overhyped, but instead, to paraphrase the great philosophers Pink Floyd:
All in all, big data is just another brick in the wall.
Clicking on the link will take you to the episode’s blog post:
- Defining Big Data — This episode of the Open MIKE Podcast, with assistance from Robert Hillard, discusses how big data refers to big complexity, not big volume, even though complex datasets tend to grow rapidly, thus making them voluminous.
- Too Big to Ignore — Guest Phil Simon, author of the book Too Big to Ignore: The Business Case for Big Data, offers advice on getting started with big data and remembering that big data is just another means toward solving business problems.
- Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
- Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, experimentation, and correlation.