Scott Taylor, The Data Whisperer, at the Senzing Global User Conference
Establishing Data Truth First:
The Missing Foundation of Data Success
“If you don’t have the truth in your data, you’re not going to derive the meaning you are expecting,” declares data industry veteran Scott Taylor, pacing the stage in his characteristically animated style at the Senzing 2024 Global User Conference. “It is not chicken or egg here – it is egg and omelet.” This insight cuts to the heart of why so many data initiatives fail to deliver their promised value: the outcomes will only be as good as the data and, in Scott’s words, “Data is the new Bullsh*t!”
Taylor, known as “The Data Whisperer,” brings decades of experience working with world-class iconic data companies like Dun & Bradstreet and advising enterprises at every level of maturity. A sought-after consultant and author of “Telling Your Data Story: Data Storytelling for Data Management,” he’s observed how organizations consistently focus on advanced analytics applications while underinvesting in foundational data truth.
Hardware comes and goes. Software comes and goes. Data remains… You must determine the truth in your data before you derive meaning out of it.
– SCOTT TAYLOR
The core problem, Taylor explains, is misplaced priorities. Organizations are rushing to implement flashy analytics, AI, machine learning and data visualization tools – pouring attention and funding into these advanced capabilities – yet they’re building on shaky ground. These sophisticated tools are only as good as the underlying data they rely on and, too often, that foundation is weak or inaccurate.
In this engaging and humorous presentation, Taylor breaks down his personal data philosophy, which he boils down to three words: “truth before meaning.” When it comes to data truth, Taylor emphasizes it isn’t about “political truth” or “personal philosophical truth” but rather “business truth, organization truth.” As he says, “You can find that truth, so don’t let anybody say you can’t.”
The High Cost of “Naked Data”
Taylor’s examples of “naked data” – such as multiple versions of company names, inconsistent records and fragmented data – illustrate exactly the problems that advanced entity resolution AI solves. When he shows how organizations struggle with “275 different configurations in 7-Eleven” or can’t maintain consistent spellings of major brands like Coca-Cola and Nestle, he’s highlighting a fundamental challenge: how do you know when different records refer to the same real-world entity? How do you know who you’re really doing business with?
This is what Senzing entity resolution AI was built for. Taylor advocates for establishing “business truth” in data, and entity resolution provides the technological foundation for achieving this truth. It automatically identifies and links different records that refer to the same entity – whether that’s a person, company, product or other business entity. Just as Taylor emphasizes the need for a common language across the organization, entity resolution lays the solid foundation for this common language automatically and systematically.
Entity Resolution: The Answer to "Naked Data"
The core problem, Taylor explains, is misplaced priorities. Organizations are rushing to implement flashy analytics, AI, machine learning and data visualization tools – pouring attention and funding into these advanced capabilities – yet they’re building on shaky ground. These sophisticated tools are only as good as the underlying data they rely on and, too often, that foundation is weak or inaccurate.
In this engaging and humorous presentation, Taylor breaks down his personal data philosophy, which he boils down to three words: “truth before meaning.” When it comes to data truth, Taylor emphasizes it isn’t about “political truth” or “personal philosophical truth” but rather “business truth, organization truth.” As he says, “You can find that truth, so don’t let anybody say you can’t.”
When it comes to entity resolution – Nothing is more accurate than Senzing.
Nothing is easier. Nothing is more cost-effective.
Nothing.
But don’t take our word for it – you can get started with Senzing in minutes.
If anything seems hard – then something is wrong – so shoot us an email here support@senzing.com and we’ll help you, stat.
Video Highlights
1:15 Why the way we talk about data is holding the industry back
“I’ve never met a CEO or CFO who cares about how you’re going to do that stuff until they understand why it’s important. Focus on the why, not the how.”
11:35 Why truth before meaning is the foundation of data success
“You must determine the truth in your data before you derive meaning out of it. It is not chicken or egg here – it is egg and omelet. From GenAI to general ledger, we’ve got the same story – truth before meaning.”
17:05 The repeated pattern of naked data challenges across technology waves
“We took a look at their data and they had over 275 different configurations in ‘7-Eleven’ – lots of creativity in the field, none of it selling product.”
18:50 How to explain complex data concepts using the “four C’s” framework
“Structured data works harder than unstructured data, period, stop. The way you get value out of unstructured data is to put some form of structure on it. Code, company, category and country – these four C’s create a common language everyone in your organization can understand.”
30:40 The three V’s of data storytelling: vocabulary, voice and vision
“You want to establish an accessible vocabulary, harmonize to a common voice and align with your organization’s vision.”
42:50 Two types of data storytelling: management truth vs. analytics meaning
“Before you can tell any stories with data, you have to tell stories about the data – the data management story.”
43:30 Why stories work faster than processes for driving change
“Stories can work faster than processes, and an effective narrative can really galvanize your team.”
