The Dataiku Blog

Lynn Heidmann

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Recent Posts

Back to School: The Changing Role of Data in Education

With summer officially over and fall setting in, the back-to-school excitement abounds. I sat down with Josh Hewitt, Director of Academics at Dataiku, and Diane Igoche, Curriculum Development...

* Use Cases & Projects, * Dataiku Company | September 06, 2019 | Lynn Heidmann

Bringing the Human-Centered Touch to AI in Human Resources

AI will bring major benefits to HR, significantly broadening the reach of recruiters and allowing businesses to more effectively scour the Earth for high-quality job candidates. It offers the...

* Use Cases & Projects, * Scaling AI | September 05, 2019 | Lynn Heidmann

Top Use Cases for AI in Banking

Global business information provider IHS Markit predicts that the business value of artificial intelligence (AI) in banking will reach $300 billion by 2030. Here are some of the most promising use...

* Use Cases & Projects, * FEATURED | September 03, 2019 | Lynn Heidmann

Bringing AI Skills to the Classroom

Just in time for the new school year, Dataiku’s latest academic partnership with Teradata University Network (TUN) is set to bring AI tools and skills to the classroom. The program empowers...

Academic | August 29, 2019 | Lynn Heidmann

AI Maturity Survey: Where Are We in the Path to Enterprise AI?

With all the media hype and coverage around AI, one might think that every company out there has Enterprise AI all figured out and is extremely mature in their data journey. However, we surveyed...

* Scaling AI, * FEATURED | August 28, 2019 | Lynn Heidmann

Building a Data Pipeline to Clean Dirty Data

A data pipeline is a series of steps that your data moves through. The output of one step in the process becomes the input of the next. Data (typically raw data) goes in one side, goes through a...

Data Preparation, Technology, Data analysis | August 23, 2019 | Lynn Heidmann

AI vs. Machine Learning vs. Deep Learning

Talking about AI is increasingly complex because it’s often used alongside (or even interchangeably with) the terms machine learning (ML) and deep learning (DL). Why do people use these terms...

machine learning, deep learning, artificial intelligence | August 19, 2019 | Lynn Heidmann

AI in Banking: An Inside Look

We released a white paper in mid-July about the challenges, solutions, and steps to get started with AI in banking, an industry that (like many others) has a lot of the essential ingredients...

banking, artificial intelligence, operationalization | July 29, 2019 | Lynn Heidmann

How AI is Transforming Banks & Banking

Data has always been the foundation of the banking industry. What has changed in recent years, of course, is the amount of data available and the speed at which it is processed as well as the need...

EnterpriseAI, financial services, Data Transformation | July 15, 2019 | Lynn Heidmann

5 Tips from GE Aviation for Starting a Self-Service Data Initiative

GE Aviation went from data silos to self-service data in just a few years, and now they have more than 1,800 users empowered to use data in their day-to-day work. Here are their top five tips for...

organization, self-service analytics, change management | June 12, 2019 | Lynn Heidmann

Practical Deep Learning

If this month’s Google I/O conference is any indication, then incorporating machine learning (and deep learning) into existing products and processes to make them more efficient or useful is the...

data science, deep learning, Education | May 21, 2019 | Lynn Heidmann

How to Create a Data Team

Hype around AI means that more and more, businesses are dedicating huge sums of money to assembling large data teams and setting them loose, hoping they produce results on their own. Often, they...

organization, Data Strategy, Enterprise Ai | May 17, 2019 | Lynn Heidmann

How AI Will Change Marketing (and Marketers)

It's no secret that introducing data science, machine learning, automation, and (eventually) AI into the world of marketing will be a critical factor to success. Yet it also means a fair bit of...

marketing, segmentation, business | May 13, 2019 | Lynn Heidmann

How Will Machine Learning and AI Change My Organization?

Whether businesses have a specific aversion to change or they simply find change too difficult to manage, the bottom line is that it holds companies back. Yet in the age of machine learning and...

organization, artificial intelligence, change management | May 09, 2019 | Lynn Heidmann

Data Scientist vs. Data Analyst - What’s the Difference?

Whether you’re a student deciding on a career path, a data analyst looking for a change, or a business owner looking to hire data talent, the question of data scientist vs. data analyst (or...

organization, data science, Data analysis | May 03, 2019 | Lynn Heidmann

When Good Data Team Hiring & Open Source Aren’t Enough

Smart hiring (and retention) of data scientists combined with the power of open source can get companies started in their machine learning (ML) efforts. But the path to Enterprise AI means...

data governance, Open Source, Enterprise Ai | April 23, 2019 | Lynn Heidmann

Making Sense of Startup Ecosystem Data

When it comes to data on startups, Startup Genome is the gold standard - their yearly report on the startup ecosystem is well-respected (not to mention well-cited). But how are they able to find...

start-up, case study, data project | April 11, 2019 | Lynn Heidmann

GE Aviation: From Data Silos to Self-Service

Despite the hype of machine learning and AI, not many businesses today have actually managed to bring transformational change through data - GE Aviation is an exception. Since March 2017, the GE...

self-service analytics, Data Revolution, Innovation | April 09, 2019 | Lynn Heidmann

Planning & Forecasting in the Age of AI

Forecasting and planning are some of the very oldest use cases of modern statistics - businesses as far back as the 1950s used computer-based modeling to anticipate risks and make decisions. But...

data science, supply chain, forecasting | April 05, 2019 | Lynn Heidmann

AI Isn't Just for the Big Players: Why SMBs Need It Too

According to Forbes, as of 2018, only 11 percent of small and medium businesses (SMBs) use AI, and 41 percent feel that it’s too complex for their needs. But AI and machine learning don't need to...

strategy, SMB, Enterprise Ai | April 02, 2019 | Lynn Heidmann
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