End-to-end Frustration - after speaking with countless consultants, here is a collation of some of the quotes I've jotted down along the way.
Would you build a Ferrari before the road?
“Data scientists are frustrated because their models aren’t deploying effectively.”
“Engineers are frustrated as they’re expected to work around legacy infrastructure.”
“Architects are frustrated because they’re expected to stitch together incompatible systems.”
“C-suites are frustrated because ML/AI projects aren’t delivering in time or in budget.”
“Everyone is just frustrated!”
“I can prove it. If ML had genuinely been such a great impact on efficiency – the golden promise of ML, wouldn’t productivity per head have improved? It’s been two years of large scale investment and it hasn’t shifted at all.”
"There’s still complete misunderstanding of data's value proposition in market.”
“Two years ago, the rhetoric was all about how to instil a data-lead culture. Today, everyone is STILL working on the strategy of how to deliver transformation change - how to organise themselves in general with respect to data.”
This theme surfaces time and time again, from all corners of the market. When people only focus on the possibilities, they often fail to consider the pragmatics.
“Clients are throwing money at it, but not at culture.”
Machine learning isn't an add on. It needs a culture.