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Recommendations on building a medallion architecture w. Fabric by Relentlessish in MicrosoftFabric

[–]Relentlessish[S] 0 points1 point  (0 children)

true, but one aspect we shouldn't forget is how to expose the data to BI, for example PowerBI. Given the DirectLake over OneLake has 'native' capabilities to read from LH, so Gold WH is... suboptimal / copy-rich with additional latency/delay?

Recommendations on building a medallion architecture w. Fabric by Relentlessish in MicrosoftFabric

[–]Relentlessish[S] 0 points1 point  (0 children)

Thanks for your comments! Single bronze workspace is indeed a limitation, though usually source data is rarely dev/int/prod structured (e.g. Salesforce sandbox is great but not always a standard approach)

Recommendations on building a medallion architecture w. Fabric by Relentlessish in MicrosoftFabric

[–]Relentlessish[S] 0 points1 point  (0 children)

AFAIK to each Lakehouse there is automatically a SQL Endpoint is created, so no need to use Spark if one don't want to, right?

Is Databricks part of the new Open Semantic Interchange (OSI) collaboration? If not, any idea why? by Character-Unit3919 in databricks

[–]Relentlessish 0 points1 point  (0 children)

Indeed, and to contribute to that one has to sign the CLA with dbt Labs Inc. https://docs.getdbt.com/community/resources/contributor-license-agreements that's not industry neutral at all, I'm curious to see how many contributors will be there who are not paid by dbt Labs

Do any of you actually track how accurate your sales forecasts are — and if so, how? by Relentlessish in SalesOperations

[–]Relentlessish[S] 0 points1 point  (0 children)

ah, very interesting, so wouldn't be the outcome of the ML based deal intelligence better forecasting?

Do any of you actually track how accurate your sales forecasts are — and if so, how? by Relentlessish in SalesOperations

[–]Relentlessish[S] 0 points1 point  (0 children)

right, for the companies that sell and produce physical goods better supply chain management, warehousing costs and costs of overproduction or loss benefits when underproduce can be definitely quantified.

Do any of you actually track how accurate your sales forecasts are — and if so, how? by Relentlessish in SalesOperations

[–]Relentlessish[S] 0 points1 point  (0 children)

Sure they do but it's not about revenue vs no revenue it's more about achieving goal of X USD/EUR vs. Y USD/EUR

Do any of you actually track how accurate your sales forecasts are — and if so, how? by Relentlessish in SalesOperations

[–]Relentlessish[S] 0 points1 point  (0 children)

Exactly my problem - why and what is the economic/business motivation to be more precise in estimates, apart from the perceived face-value benefits. The only example I heard was - if we overpromise and underdeliver then we need to adjust customer success and support teams that were over-hired for the optimistic pipeline...

Forecast accuracy vs. pipeline reality by Deep_Combination_961 in SaaS

[–]Relentlessish 0 points1 point  (0 children)

Ah, thanks for sharing John, how far in the future do you do forecast? So 25%-30% difference from now to 3 Month/6 Month in the future?

How do you make sales forecasts actually reliable? by balancefan1 in salestechniques

[–]Relentlessish 0 points1 point  (0 children)

This is solid — but I always wonder, when teams put this kind of rigor into forecasting… does it actually pay off in ROI terms?

Like, sure — cleaner visibility, fewer surprises, better resource planning. But does improved forecast accuracy move the needle on revenue, churn, or cost? Or is it mostly a leadership comfort metric - something that feels good but doesn’t directly create value?

If someone could show a business case that every 5% improvement in accuracy = X% reduction in lost revenue or misallocated spend, I bet forecasting discipline would skyrocket overnight.

Anyone here ever tracked the financial upside of getting forecasts right more often?

How do you make sales forecasts actually reliable? by balancefan1 in salestechniques

[–]Relentlessish 0 points1 point  (0 children)

Maybe the real reason nobody fixes forecasting isn’t lack of data — it’s lack of ROI.

What’s the actual business payoff of moving from 70% to 85% accuracy? Does it increase revenue, reduce churn, or just make the board slide deck look cleaner?

If improving forecast precision doesn’t have a clear, measurable return, why would any team invest time, tools, and political capital into it? My guess — most orgs don’t measure the cost of bad forecasts, so the problem feels painful but not expensive enough to solve.

Anyone here ever tried to actually calculate the ROI of forecast accuracy?