Author: Lynda Partner

The Importance of a Modern Cloud Platform Foundation: Part Four

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…

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Seven Reasons to Outsource Your Move to the Cloud

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,…

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The Four Stages of Analytics Maturity: Part Three

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…

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Top Business Drivers for Analytics Programs: Part Two

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…

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Accelerating Your Analytics Journey: Part One

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…

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Five Ways Your Machine Learning Model Is like Cookie Monster

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…

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The Top Four Reasons Cloud Data Platforms and Machine Learning Were Meant For Each Other and How to Make Them Work Well Together

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…

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Where in a Modern Data Platform Should You Process Your Data?

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…

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Dipping Your Toes Into Building an Analytics Platform on Google Cloud Platform

“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…

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How Pythian and Google helped reduce pesticide use in high-value crops

Semios is a Vancouver-based data analytics company for growers of high-value crops such as almonds and apples. It uses a combination of machine learning, in-crop wireless networks and half a million IoT sensors across 80,000 acres to offer real-time monitoring…

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