The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 15 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 0 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Dhillon: Enterprises underestimate their SaaS application usage by 10x
“If you bet companies a dollar that they're using X number of SaaS apps, They're off, but not by half. They're off by, like, 10 X, you know, because somebody in marketing is using something, and they go, it's not an application.”
Dhillon: Historical business intelligence offers zero value to modern tech companies
“This rear view mirror historical perspective is, is no longer of incremental value to a technology company or to an investment bank.”
Dhillon: Capital One built a massive business using early data science
“Capital One, who is, I would say, the original data science company, figured out tens of billions of dollars of business, giving credit cards to people who others had denied, and making it profitable using Data science.”
Dhillon: Enterprise architecture is shifting from batch processing to real-time streaming
“First, you're going away from a batch architecture of the nineties to a real-time streaming architecture. Like, we want our stuff now. This is 2016, and you know, we want to see a movie now. So, so you're going to go through a real shift towards streams of dat…”
Dhillon: You can buy network bandwidth, but latency comes from God
“For some machine data, factory floor data, power plant data, it may not go just because, you know, bandwidth you can buy, but latency you get from God.”
Dhillon: Data lakes will eventually drown out traditional data warehouses
“The rising tide of the data lake, we think, will drown out the data warehouse in the fullness of time.”
Dhillon: Replacing enterprise finance software is like open-heart surgery
“I mean, it's open heart surgery for the enterprise to replace finance. It's not simple. It's in many cases, you have to report earnings quarterly. So you really have to get this window just right.”
Dhillon: Modern web apps require new plumbing for document models
“One is the data types fundamentally being different require new kinds of plumbing. You know, this is digital plumbing we're talking about, but you're no longer using rows and columns. You're using a document model. The way the worldwide web works, the way brow…”
Dhillon: Enterprise data architecture is shifting from data warehouses to data lakes
“What we're seeing is a trend away from legacy data warehouses into data lakes, which are then consumed both by people using modern visualization products, like say a Tableau, and also by lots and lots of data scientists”
Dhillon: Data warehousing was originally an organizing principle, not a product
“Inman and Kimball came up with an organizing principle, you know, data warehousing was never a product. You couldn't go and buy one. It didn't exist, but it was an organizing principle to, and to corral Marshall and get benefits from the data that you had in y…”
Dhillon: Enterprise IT architecture is always a retrofit job
“The enterprise is a retrofit job. It always has been.”
Dhillon: Tension between CMOs and CIOs has largely resolved
“There was this tension between the chief marketing officer and the CIO. I think they've largely kissed and made up because A, the CIOs are getting to be more business people and the marketing people are getting more technical. So that tension has disappeared i…”
Dhillon: Big data's key innovation is automated cross-source data correlation
“In big data, to me, the fundamental breakthrough is providing information from multiple places and producing insights where the data finds the data.”
Dhillon: Traditional business intelligence yields diminishing marginal returns
“The traditional rear view mirror view of business intelligence has some element of return, but it's also at some point been well done. There are ways to improve that, but we're getting to a point of diminishing marginal returns on that.”