The Dataiku Blog

A Peek into the Regulatory Future of AI?

Whether you realized it or not, Data Privacy Day 2019 (yes, it exists!) has already come and gone. But this year, it was perhaps more significant than most not only because topics of bias,...

ethics, EnterpriseAI, interpretability | February 12, 2019 | Lynn Heidmann

Tackling the Cold Start Problem for Recommendation Engines

I recently moved to New York City, a true abundance of riches. I wanted to find “my coffee shop,” but sampling different locales is exhausting and disheartening. I cannot fathom trying a new cafe...

recommendation, machine learning, user experience | February 06, 2019 | Claire Carroll

Super Bowl Beer Recommendation Engines

The Super Bowl has a lot of traditions: great ads, good comfort food, and if you’re unlucky, a free cab home. But there is an important question that is likely to impact your overall happiness and...

recommendation, sports analytics, beer | February 03, 2019 | Claire Carroll

Dataiku Named Challenger in Gartner 2019 Magic Quadrant for Data Science and Machine-Learning Platforms

Closely following December’s Series C funding announcement, we’re pleased to share that Dataiku has been named a Challenger in the Gartner 2019 Magic Quadrant for Data Science and Machine Learning...

News, Corporate, announcement | January 30, 2019 | Lynn Heidmann

Discovering (or Disproving) Aliens with Data Science

Many real Americans believe they have seen aliens, and we have the reporting data of UFO sightings to prove it. The dataset records the time, date, location, shape of the sighting, and the story...

Data Science Basics, data project, Tools | January 29, 2019 | Rebecka Flynn

Go from Data-Driven to Insights-Driven

The road from a pile of data to business insights and from insights to action is paved with good intentions, but often learning things from data takes excessive time and energy. More often than...

Team, Data Strategy, automation | January 28, 2019 | Claire Carroll

Joining the Dataiku Team: What Is It Like?

As Dataiku grows, we’re trying really hard to hold on to the traits that make us a great place to work, including an inclusive family atmosphere and our fun company culture.

dataiku, hiring, Onboarding | January 25, 2019 | Taylor Mecham

How to Hire for Technical, Data-Centric Roles

Unemployment is low, and the demand for skilled workers is high in many industries. That means top tech talent is more in-demand than ever, and 87% of hiring managers find it difficult to get the...

Data Engineering, Team, hiring | January 17, 2019 | Claire Carroll

4 Trends for AI in the Enterprise in 2019 [QUIZ + INFOGRAPHIC]

In 2018, the world saw the rise of automated machine learning, deep learning, and - best of all - real-life applications of these technologies, all of which have started to pave the path to...

trends, EnterpriseAI, 2019 | January 11, 2019 | Lynn Heidmann

Top 8 Use Cases for Machine Learning & AI in Marketing

According to a 2018 study by MemSQL, more than 60 percent of marketers say artificial intelligence (AI) is the most important aspect of their data strategy. But naming AI as a key strategy and...

marketing, Infographic, ai | January 09, 2019 | Lynn Heidmann

AI Gotchas (& How to Avoid Them)

Now that every company out there is doing artificial intelligence (AI) - or at least claims to be - it’s becoming more and more clear that it’s way too easy to fall into a few simple traps that...

ethics, EnterpriseAI, interpretability | January 08, 2019 | Lynn Heidmann

New Year's Resolution: Help Data Scientists Help You

Earlier this month, Forbes cited the key to getting value from data science as teamwork. That means in the coming year, with an increased focus on collaboration, lines of business will likely have...

organization, POC, Data Strategy | December 31, 2018 | Lynn Heidmann

How AI Will Change Brick-and-Mortar Retail in 2019

Data science, machine learning, and AI have clear applications for e-commerce, and given their relative ease of implementation, most online retailers are already deeply invested in strategies like

deep learning, retail, computer vision | December 26, 2018 | Claire Carroll

4 Reasons Santa Needs Machine Learning & AI

You know how the song goes - he’s making a list, he’s checking it twice. But if you ask us, all Santa Claus should really be doing come December is doing some light maintenance on his machine...

data science, recommendation systems, predictive maintenance | December 24, 2018 | Lynn Heidmann

Dataiku Series C: New Year, New Chapter

Democratizing data science is something Dataiku - both as a company and a product - has been working toward since its founding in 2013. So it’s with great pleasure that we announce today the...

Corporate, announcement, Funding | December 19, 2018 | Lynn Heidmann

Top Insights from 50 Chief Data Officers

Whether you’re a Chief Data Officer (CDO) yourself, you report to one, your organization has one, or you/your organization is looking to hire one, there is still much to learn about the...

big data, Data Strategy, Data Revolution | December 14, 2018 | Claire Carroll

Exploring the Gender Pay Gap with Publicly Available Data

There are plenty of studies that discuss the gender pay gap (here’s just one example). But as a data practitioner, I find those studies a bit frustrating. Not because I reject these pay gaps, but...

data science, dataiku, data project | December 12, 2018 | Alexandre Hubert

Let Automation Carry You from BI to AI in 2019

By now it’s clear that businesses that execute on artificial intelligence (AI) successfully are the ones that will move from doing one-off analysis, and even one-off models, to being able to...

organization, ai, automation | December 11, 2018 | Lynn Heidmann

Why Machine Learning Interpretability Matters

Even though machine learning (ML) has been around for decades, it seems that in the last year, much of the news (notably in mainstream media) surrounding it has turned to interpretability -...

data science, machine learning, interpretability | December 04, 2018 | Lynn Heidmann

The State of Data in Astronomy

Astronomy’s approach to data has drastically changed over the past two decades. From improved data collection methods to ML-based analytics, astronomers have more access to data than ever before.

Academic, machine learning, scale | December 03, 2018 | Anna Alonso
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