This is incredible I've really enjoyed reading this. It's amazing what a layman can deduce from the basic level of filtration available to their scope of understanding merely by indicators of intrigue. This is a very forward thinking conceptual framework for what feels like the future as I would like to see it formed. Excellent.
Amazing! I have a lot to catch up on. It really brings in the point of specializing over general knowledge on a subject. As a founder in anything it's wise to have some level of understanding. I look forward to learning more! Thank you.
This is the thesis, AND active deployment, Revscale Sales is working on. I have activated the Revscale AI in my LinkedIn Sales Navigator channel and it is generating 5+ 1 to 1 interactions a week. AI is on par with my email automated sequencing and will compound learning to outperform my automations in the next month is my bet.
Data models constantly changing. New products getting folded into the sales motion resulting in changes to process and language that impact existing automations and future system adaptations.
What's your best tip to maintain and improve data quality?
Well I'm biased to a certain POV due to workplace, but that's in relation to "Business Systems" as they're known, vs. the sea of other systems available. However, it's worth zeroing in on that domain given it's the starting point ("Source" systems) for BI and analytics.
I think, due to the passive copy/paste nature of integrations given the capabilities of iPaaS and ETL tooling (even Fivetran doesn't really do the "T" in ETL), companies have had to essentially build AGAINST data quality. Copying to two places means one can get cleaned up but the other can't. So extract from Salesforce to warehouse to dbt to clean up just means Salesforce stays a mess. Reverse ETL doesn't solve CRM data quality. It just writes in some new fields of clean stuff ... much later than it's useful, typically, unless the use case is just campaign-specific and not a responsiveness need.
But it seems the primary need of business systems is to provide business users - marketing, sales, CS, finance - with on-demand info so they can do their jobs: provide great customer experiences across the journey.
We don't need a single source of truth, therefore. We need a mechanism for distributing accurate information across the stack.
But that's impossible, literally. Unless we reconfigure the underlying "nature of integrations" referenced above: we need an entirely new way to transform data as it lives and breathes within and across Go-To-Market technology stacks.
This is incredible I've really enjoyed reading this. It's amazing what a layman can deduce from the basic level of filtration available to their scope of understanding merely by indicators of intrigue. This is a very forward thinking conceptual framework for what feels like the future as I would like to see it formed. Excellent.
Glad it connected and I hope you check out the other pieces, this one was written early on and we've taken the material much further since then!
Amazing! I have a lot to catch up on. It really brings in the point of specializing over general knowledge on a subject. As a founder in anything it's wise to have some level of understanding. I look forward to learning more! Thank you.
This is the thesis, AND active deployment, Revscale Sales is working on. I have activated the Revscale AI in my LinkedIn Sales Navigator channel and it is generating 5+ 1 to 1 interactions a week. AI is on par with my email automated sequencing and will compound learning to outperform my automations in the next month is my bet.
Howard I am even more excited for our conversation now, reaching out via LNKD.
This sounds impressive.
To discuss AI in RevOps though we have to discuss data quality. It's the main thing crushing RevOps utility, and also downstream BI/analytics utility.
And what a topic that is.
Data models constantly changing. New products getting folded into the sales motion resulting in changes to process and language that impact existing automations and future system adaptations.
What's your best tip to maintain and improve data quality?
It's true, it's a bit of a behemoth.
Well I'm biased to a certain POV due to workplace, but that's in relation to "Business Systems" as they're known, vs. the sea of other systems available. However, it's worth zeroing in on that domain given it's the starting point ("Source" systems) for BI and analytics.
I think, due to the passive copy/paste nature of integrations given the capabilities of iPaaS and ETL tooling (even Fivetran doesn't really do the "T" in ETL), companies have had to essentially build AGAINST data quality. Copying to two places means one can get cleaned up but the other can't. So extract from Salesforce to warehouse to dbt to clean up just means Salesforce stays a mess. Reverse ETL doesn't solve CRM data quality. It just writes in some new fields of clean stuff ... much later than it's useful, typically, unless the use case is just campaign-specific and not a responsiveness need.
But it seems the primary need of business systems is to provide business users - marketing, sales, CS, finance - with on-demand info so they can do their jobs: provide great customer experiences across the journey.
We don't need a single source of truth, therefore. We need a mechanism for distributing accurate information across the stack.
But that's impossible, literally. Unless we reconfigure the underlying "nature of integrations" referenced above: we need an entirely new way to transform data as it lives and breathes within and across Go-To-Market technology stacks.
And that's the patent Nick and Neelesh got with Syncari: "multi-directional sync across cloud applications" - https://patents.google.com/patent/US20210365410A1/en?assignee=syncari&oq=syncari
Very interesting 🤔🤔
I think we need both a single source of truth, data lineage and a common dictionary so we understand what's what in the revenue engine.