Data in Depth
Mountain Point
All episodes
Best episodes
Seasons
Top 10 Data in Depth Episodes
Goodpods has curated a list of the 10 best Data in Depth episodes, ranked by the number of listens and likes each episode have garnered from our listeners. If you are listening to Data in Depth for the first time, there's no better place to start than with one of these standout episodes. If you are a fan of the show, vote for your favorite Data in Depth episode by adding your comments to the episode page.
Data as a Differentiator: The Manufacturer's Roadmap for Competing on Analytics with Skye Reymond
Data in Depth
06/12/19 • 24 min
In our first ever episode, we talk with data scientist Skye Reymond. Skye lays the groundwork for our series focusing on how data and analytics are key competitive differentiators for manufacturing companies. Here are just a few highlights:
1:44: Start where you are
Skye: There’s a book that I reference when a company is assessing where they stand in their analytical strategy. It’s called ‘Competing on Analytics.’ It outlines 5 levels of maturity...
2:22: Stage 1: Analytically Impaired
Skye: This is when a company is flying blind, they’re very reactive, the systems might not be integrated, and their data is poor quality.
3:13: Stage 2: Localized Analytics
Skye: These companies collect transactional data, something like you would see in an ERP system. But it’s still very reactive.
5:58: Stage 3: Analytical Aspirations
Skye: These companies are making investments in the right talent and tools. They’re preparing to use analytics to improve a distinctive capability of their company. They have a roadmap to automation.
6:57: The Road Map
Skye: A really good place to start is in the area of your business that you believe is going to be your differentiator. So if you have a repair shop, maybe your differentiator is service. If you’re a logistics company, your differentiator is going to be speed of delivery...
8:19: Stage 4: An Analytical Company
Skye: This is an enterprise-wide analytical strategy that’s viewed as a company priority... They're often using more automated analytics and more advanced modeling techniques... things like artificial intelligence, time series forecasting...
9:30: Building A Data-Driven Culture
Skye: It really needs to start from the top down... The next step is to give the people who are doing the jobs day-to-day some ownership and input. These are the experts who can give you some of the most valuable insight as you’re figuring out your analytics strategy.
10:31: Stage 5: The Ultimate Level
Skye: This is [a company] using analytics as a key component in their competitive strategy...analytics are fully automated, completely integrated. Decisions organization-wide are data-driven. Analytics are the central theme to how the organization operates.
11:52: The Last Differentiator
Skye: Analytics are really going to be the last differentiator. Analytics are going to be the big advantage that makes companies win over others.
16:05: Descriptive, Predictive, and Prescriptive Analytics
Andrew: I like this concept because it helps you connect your analytical strategy toward tangible goals for your business and it really guides your thinking towards asking the right questions...
References:
Data-Driven Marketing with Francois Gau
Data in Depth
09/27/19 • 25 min
In this episode, we talk with Francois Gau, owner and CEO of Levy Industrial. Francois outlines his data-driven approach for generating growth for industrial and tech-based B2B companies.
This episode is the first of a two-part segment with Francois. During this portion of the interview, Francois paints a picture of what a modern marketing program looks like in the context of the manufacturing industry. He outlines how manufacturers can move beyond the old standbys of trade shows and distributor newsletters — digging into concepts like e-commerce, engagement with the end customer, and marketing automation. And he shares his tried and true tips for market mapping and customer segmentation. Behind it all? Data, data and more data. You won't want to miss it!
- Connect with Francois on Twitter or LinkedIn.
- Check out Levy Industrial.
---
This season, we're giving away a pair of Bose QuietComfort 35 wireless headphones!
How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.
- Subscribe on ANY of the following:
- Review us on Apple Podcasts or Stitcher;
- Follow us on Twitter @DataInDepth; and
- Tweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth.
5 Ways AI and Machine Learning are Changing the Face of Manufacturing with Louis Columbus
Data in Depth
09/13/19 • 29 min
In this episode, we talk with Louis Columbus, a Forbes columnist and principal at IQMS. Louis talks through five ways artificial intelligence and machine learning are revolutionizing the manufacturing industry — from marketing and sales to efficiency, from addressing the talent gap to ensuring quality, safety and security.
13:24 - Labor Challenges
I visit a lot of manufacturers. I visit probably five to 10 manufacturers a month, and I periodically do surveys every quarter as part of my role at Dassault IQMS. We did a survey and we asked what are the top impediments to your growth in 2019, and we asked them in April. We asked 150 North American discrete manufacturers in the mid tier of the market, and number one at 67% was we don't have enough people. Labor is incredibly challenging right now for the mid tier, the American manufacturing or the North American manufacturing.
19:21 - Zero Trust Security
Zero trust security by definition is always verify, never trust approach to every security perimeter on a manufacturing location, and what zero trust security does is it verifies every device and it treats every device and every identity as a new part of the security perimeter. And so with the growth more and more of real time monitoring of machinery and every various threats or if it's that a manufacturer has being exposed, it's really critical to be able to protect it down to that specific device level.
22:38 - AI and Machine Learning Impacts on Safety
Now what's really fascinating when you go and walk shop floors and you meet manufacturers who have invested anywhere from five to 10 million dollars in new smart connected machinery. That machinery's got all kinds of safeguards in it. But more importantly, those machines have the ability to heal themselves, but also have their own innate operating systems. And how this all relates to safety is they will tell you if they are getting to a heat point or to a point with their own metrics of like, hey, this may get borderline unsafe in this environment, so therefore I'm gonna shut myself off. Or, therefore I'm gonna send you an alert to the quality manager. Safety is much more sophisticated than many opponents of AI and machine learning and manufacturing give it credit for, much, much more sophisticated.
---
This season, we're giving away a pair of Bose QuietComfort 35 wireless headphones!
How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.
- Subscribe on ANY of the following:
- Review us on Apple Podcasts or Stitcher;
- Follow us on Twitter @DataInDepth; and
- Tweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth.
Boosting the Bottom Line: 3 Ways Data Can Drive Profit for Manufacturers with Mike Wertheim
Data in Depth
08/30/19 • 32 min
In this episode, we talk with Mike Wertheim from Hayward Industries. Mike shares how Hayward is using data to boost the company's bottom line and outlines his team's top priorities for 2020.
3:41: 3 Ways Data Can Drive Profits
Mike: Data is important to everybody. For Hayward, we are a manufacturing company and it comes down to the bottom line. So, I'll give you 3 ideas of some things that make data very important to [us].
We are a big company in a mature market. The way to grow in that market is through acquisition. When you acquire a company and there's a different set of data, there's a lot of problems.
The other thing is we have traditionally been a B2B company. And we're trying to compete in this age of online purchasing and there are so many channels that people buy their products from. To do that, we've really got to go deeper and reach out to the actual consumers when possible.
The last thing is technology. IoT is a big thing, but we're trying to do more than just make the next great product. We're trying to gather the data that's going to help us make business decisions, grow business, and use those smart devices for our business intelligence.
6:13: Mergers, Acquisitions, and Master Data Management
Mike: I've been with the company 5 years and [during that time] we have acquired 5 different companies. That gives us a major focus on things like master data management, where we've got to all be talking the same language.
9:47: B2B2C
Mike: We're so blind when it comes to selling our products through a distribution channel. So now in conjunction with our distributors, we are actually collecting data about the products that they sell to their customers. That data is so valuable. It's probably the number one thing that our executives are looking for.
15:16: How IoT is Driving Service and Product Development
Mike: We have a chemical monitoring system, a floating connected device. The consumers have an app where they can see what's going on and they can share that information with servicers. So servicers can see... "oh, you're having problems. Maybe I should come out and help you."
The other thing we do is we sell chemicals in what you would think of as pods, like Tide pods. They're color-coded. So your connected device tells you, "Oh, you've got an issue. Please drop in 2 blue, 3 green and a red into your pool." So, we're connecting on the service side, and we're connecting on the consumable side.
---
This season, we're giving away a pair of Bose QuietComfort 35 wireless headphones!
How to enter: To be eligible to win, you must complete ALL of the following steps by 11/11/19.
- Subscribe on ANY of the following:
- Review us on Apple Podcasts or Stitcher;
- Follow us on Twitter @DataInDepth; and
- Tweet us letting us know when you’ve completed all the steps. Be sure to mention @DatainDepth.
Robots for Hire: The Future of IoT
Data in Depth
02/26/20 • 17 min
In this episode, we talk with Zach Boyd from Hirebotics a company that allows manufacturers to hire a robot for hourly work like they would an employee. Zach explores the ways that data and the Internet of Things (IoT) can be harnessed to provide proactive and preventative remote support and maintenance to their customers.
“We Cloud Connect our robot so that we can gain real-time insights into how that robot's performing, what problems it might have. And with that, we're able to 98% of the time, come to a resolution, identify that root cause and provide a fix from a mobile application that we've developed versus sending somebody on a plane to go help that customer.”
Connect with Zach on LinkedIn.
Demystifying Serverless Machine Learning
Data in Depth
06/29/20 • 25 min
In this episode, we sat down with Carl Osipov with CounterFactual.AI and the author of Serverless Machine Learning In Action. Carl shared some real-world use cases for serverless machine learning and identified strategies to get the most from a machine learning investment.
“One of the things that happens at the beginning of a machine learning project — and this is a well-known problem for data scientists and machine learning practitioners — is spending way too much time cleaning up their data sets and focusing on things like data quality instead of actually building out machine learning solutions. I think, as practitioners, machine learning developers and engineers have created a set of techniques over the past few years to help formalize and accelerate this process. But it’s still a concern, especially if you think about scenarios that are common to manufacturing where different data silos have to come together for a machine learning system. This also happens in the scenarios where manufacturers acquire companies and then integrate data and use that data for machine learning systems. What happens is that if companies don’t actually have a rigorous approach for transitioning their machine learning systems code into operations, they find themselves in a situation where data scientists and machine learning engineers actually end up doing a lot of operations involved in putting machine learning systems into production. So what I’m describing here is what I call an ML ops trap. This machine learning operations trap, where these highly compensated practitioners are essentially spending their time working on something that’s not their core competency.”
Connect with Carl on LinkedIn.
Using IoT to Maximize Efficiency
Data in Depth
06/15/20 • 24 min
In this episode, we talk with Ed Kuzemchak from Software Design Solutions. Ed digs into the ways companies can use the Internet of Things (IoT) to increase efficiency. He shares advice on how to identify areas of opportunity to implement IoT and strategies to make the most of an IoT investment.
“I think the most important part for a company is to look at systems they have today and say “what part of these systems that we have, can we make more efficient or more cost effective or higher performing if we had better information?’ Cause that's really all that IOT is all about. It's about gaining data where you didn't used to have data or you couldn't get good or up-to-date data. You know, if you had to wait until the reports came back from the field, from your field sales tech or your field service techs on machine failures, you might have a two week lag on machine failures. And the data that you're looking at is always two weeks old. Well, what if it was only five seconds old?”
Connect with Ed Kuzemchak on LinkedIn.
06/01/20 • 26 min
In this episode, we sat down with Kyley Darby from Mountain Point and Skye Reymond with Terbium Labs. Kyley and Skye explore how manufacturers can leverage descriptive, predictive, and prescriptive data to optimize business outcomes. They also dig into the ways Salesforce’s Einstein Analytics can help companies better plan for the future.
“‘To move forward and look beyond the “what has happened,” manufacturers need to start pulling data together in a centralized manner — to switch from seeing what has happened to “what could happen, what could we change?” I think having data all over the place is something that holds them back.” - Kyley Darby
“I’ll add to that, Kyley. In the past, a lot of these methods have been really technical and if you don’t have access to the technical talent that’s necessary, you can find yourself following a predictive model that’s incorrect. This can cause the business to lose a lot of money, time, and effort. That technical talent that can utilize predictive and prescriptive analytics has historically been hard to find. But, fortunately, with things like Einstein, Salesforce is making this skill more accessible to everybody. So I think in the future, you’re going to see more of that, where you don’t need an entire data science team, but a good understanding of Einstein, if you’re a Salesforce user, and what those results are going to mean for your business” - Skye Reymond
05/12/20 • 20 min
In this episode, we talk with Bastiane Huang with OSARO. Bastiane digs into the practical uses of deep learning and machine learning. She explores beyond the academic applications of machine learning and details some real-world scenarios, including the ability to expand the use of robots in less structured environments.
“We use machine learning to allow robots to react to changes in the environment, learn to handle a wide range of different items, and have a range of different tasks. And more importantly, to learn, “Oh! This task [required] minimum human supervision.” So this way, you can really save a lot on human costs and on a lot of the surrounding systems,. These kinds of surrounding systems are usually more than four to five times the robot costs, so it's really significant. And lastly, it also enables robots to be used in new use cases. For example, you don't really see robot arms being used in warehouses right now. Because in a typical warehouse that has millions of different products it’s not feasible to program a robot. You're able to deal with a million different products in a million different ways. So now, because of machine learning, robots can be used in this kind of less structured environment. ”
08/14/19 • 16 min
On this episode of Data in Depth, we dig into how sophisticated data and analytics are transforming logistics and changing the way companies manage their supply chains. We talk with James Lumb, CEO of Zenkraft, who offers cutting-edge examples of how logistics data can be used to avoid waste, improve customer experience, and even improve your product line.
Chapter markers:
4:48 - Supply Chain 360
6:05 - Internet of Things
6:54 - Product Improvement Feedback Loops
8:55 - The Power of Data Integration
9:57 - 10x ROI
12:03 - Customer Engagement Opportunities
13:10 - Artificial Intelligence and Data Sharing in Logistics
14:01 - Quoting Shipments in CPQ
15:34 - Win a Pair of Bose Headphones
---
We want to say “THANK YOU” for subscribing and following the first season of Data in Depth. So we’re giving you the chance to win some great prizes.
One lucky listener will snag a pair of Bose QuietComfort 35 wireless headphones! On top of that, we’re giving away other awesome swag including Yeti insulated coffee mugs!
How to enter:
To be eligible to win, you must complete ALL of the following steps.
- Subscribe on ANY of the following platforms:
- Provide a review on Apple Podcasts or Stitcher;
- Follow us on Twitter @DataInDepth; and
- Tweet us letting us know when you’ve completed all the steps. Be sure to use the #DataInDepth hashtag and mention @DatainDepth.
Enter by November 11, 2019. Full contest details >
Show more best episodes
Show more best episodes
FAQ
How many episodes does Data in Depth have?
Data in Depth currently has 26 episodes available.
What topics does Data in Depth cover?
The podcast is about Analytics, Management, Data, Podcasts, Big Data, Technology, Manufacturing, Iot, Business, Artificial Intelligence and Machine Learning.
What is the most popular episode on Data in Depth?
The episode title 'Using IoT to Maximize Efficiency' is the most popular.
What is the average episode length on Data in Depth?
The average episode length on Data in Depth is 23 minutes.
How often are episodes of Data in Depth released?
Episodes of Data in Depth are typically released every 14 days, 2 hours.
When was the first episode of Data in Depth?
The first episode of Data in Depth was released on Jun 12, 2019.
Show more FAQ
Show more FAQ