At the highest, simplest level, a machine learning (ML) model is an algorithm that ingests data and spits out insights, predictions or recommendations. It has two important phases — first you have to train your model (training) then you let the model run (inference).

Recently, I was trying to explain how important data is in machine learning success and eventually came up with the five ways that your machine learning model is like the big, blue, always-hungry Cookie Monster who has appeared on Sesame Street for over 50 years.

1) Data consumption

Cookie Monster is a voracious consumer of cookies; the more cookies…

In the book Designing Cloud Data Platforms, separating storage from compute is a key tenet of a layered cloud data platform design. It brings scalability, cost savings, flexibility and maintainability. But established ways of operating are slow to change — traditional data warehouse architects advocate for processing in the data warehouse while modern cloud platform design dictates that processing should happen outside the data warehouse.

Modern cloud analytics data platforms are complex things — in more ways than one. To start, they include many different components and layers all working simultaneously. This includes an extract, transform, load (ETL) layer; an…

We all know the feeling: the new software is finally rolled out after months, or sometimes years, of hard work by your team. Then, instead of the sound of users calling to share how much the new software improved their lives, all we hear is crickets.

Bringing data together and enabling self-service analytics is no different. Make no mistake, it’s a massive project and its success depends on adoption. But we all know that as a rule people don’t like change.

How to encourage adoption of self-service analytics was a big topic of conversation at the leadership track of TDWI’s…

The other day, a friend of mine asked what I thought B2B marketing would be like by 2025. That’s six years from now — not exactly right around the corner. But it was a fun conversation, so I thought I’d share my thoughts and hopefully invite some discussion around future trends in data-driven marketing.

1. The shift to marketing as a revenue and profit generator will continue, and those not on board will see their marketing teams and budget disappear.

Long gone are the days where CEOs are satisfied with marketing leaders simply droning on about things like brand awareness…

Lynda Partner

Data geek, tech exec

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