Author: Lynda Partner

Dear Data, I remember when we first met. It was waaaaay back in university when I signed up for a stats course thinking it would be easy. Turned out it was, but I didn’t really get you back then. All…

As with any journey, having a good roadmap and a well-tuned vehicle make for a smoother trip. For an analytics program, the engine for success comes from a modern data platform. Today, this almost always means a cloud-based solution and…

I was talking to someone the other day who asked me why they should consider outsourcing their planned VM migration to the cloud. They felt they were perfectly capable of doing it themselves. The question I asked in return was,…

Once you’ve established your destination, it’s time to hit the road. An organization’s analytic journey typically progresses through four stages of maturity, as follows: See: Deliver insights on where the business is today and was historically. Predict: Project future scenarios…

Anyone who’s been on a road trip has heard this question. It usually comes from the backseat of the car, where it can be difficult to see the route ahead, and near-impossible to judge progress. For those tasked with managing…

Data. According to Merriam Webster, data is “information in digital form that can be transmitted or processed.” Sounds simple. Sounds like something that’s everywhere. However, smart, influential business leaders will tell you data is important. It’s powerful. It’s a key…

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…

Machine learning (ML) workloads have some unique properties, characteristics and complexity that separate them from less advanced analytics. Developing an ML model requires data scientists to understand what kind of data their model will need, and to run a number…

Data warehouse or data lake? We break down the pros and cons of each 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…

“We have many disparate data sources and we’re having a hard time getting a global view of all our data across our organization.” “Our data is currently all in <enter data warehouse name here> and we want to migrate it…