Ford has exploited the strengths of big data analytics by directing them internally to improve business results. In doing so, they scour the metrics from the company’s best processes across myriad manufacturing efforts and through detailed outputs from in-use automobiles — all to improve and help transform its business, says Michael Cavaretta, Ph.D., technical leader of predictive analytics forFord Research and Advanced Engineering. Cavaretta is one of a group of experts who gathered last week for The Open Group Conference.
Cavaretta has led multiple data-analytic projects at Ford to break down silos inside the company to best define Ford’s most fruitful data sets. Ford has successfully aggregated customer feedback, and extracted all the internal data to predict how the best new technology features will improve their cars.
As a contributor to The Open Group conference focusing on “Big Data — The Transformation We Need to Embrace Today,” Cavaretta explains how big data is fostering business transformation through efficient access to deeper insights into more types of data, thereby improving processes, quality control and customer satisfaction.
The interview is moderated by Dana Gardner, principal analyst at Interarbor Solutions.
Download the podcast (29:46 minutes) or use the player:
Here are some excerpts:
Dana Gardner: What’s different now in being able to get at this data and do this type of analysis from five years ago?
Michael Cavaretta: The biggest difference has to do with the cheap availability of storage and processing power, where a few years ago people were very much concentrated on filtering down the datasets that were being stored for long-term analysis. There has been a big sea change with the idea that we should just store as much as we can and take advantage of that storage to improve business processes.
Gardner: How did we get here? What’s the process behind the benefits?
Cavaretta: The process behind the benefits has to do with a sea change in the attitude of organizations, particularly IT within large enterprises. There’s this idea that you don’t need to spend so much time figuring out what data you want to store and worry about the cost associated with it, and more about data as an asset. There is value in being able to store it, and being able to go back and extract different insights from it. This really comes from this really cheap storage, access to parallel processing machines, and great software.
I like to talk to people about the possibility that big data provides, and I always tell them that I have yet to have a circumstance where somebody is giving me too much data. You can pull in all this information and then answer a variety of questions, because you don’t have to worry that something has been thrown out. You have everything.
You may have 100 questions, and each one of the questions uses a very small portion of the data. Those questions may use different portions of the data, a very small piece, but they’re all different. If you go in thinking, “We’re going to answer the top 20 questions and we’re just going to hold data for that,” that leaves so much on the table, and you don’t get any value out of it.
We’re a big believer in mashups and we really believe that there is a lot of value in being able to take even datasets that are not specifically big-data sizes yet, and then not go deep, not get more detailed information, but expand the breadth. So it’s being able to augment it with other internal datasets, bridging across different business areas, as well as augmenting it with external datasets.
A lot of times you can take something that is maybe a few hundred thousand records or a few million records, and then by the time you’re joining it and appending different pieces of information onto it, you can get the big dataset sizes.
Gardner: You’re really looking primarily at internal data, while also availing yourself of what external data might be appropriate. Maybe you could describe a little bit about your organization, what you do, and why this internal focus is so important for you.
Cavaretta: I’m part of a larger department that is housed over in the research and advanced-engineering area at Ford Motor Company, and we’re about 30 people. We work as internal consultants, kind of like Capgemini or Ernst & Young, but only within Ford Motor Company. We’re responsible for going out and looking for different opportunities from the business perspective to bring advanced technologies. So, we’ve been focused on the area of statistical modeling and machine learning for I’d say about 15 years or so.
And in this time, we’ve had a number of engagements where we’ve talked with different business customers, and people have said, “We’d really like to do this.” Then, we’d look at the datasets that they have, and say, “Wouldn’t it be great if we would have had this. So now we have to wait six months or a year.”
These new technologies are really changing the game from that perspective. We can turn on the complete fire-hose, and then say that we don’t have to worry about that anymore. Everything is coming in. We can record it all. We don’t have to worry about if the data doesn’t support this analysis, because it’s all there. That’s really a big benefit of big-data technologies.
The real value proposition definitely is changing as things are being pushed down in the company to lower-level analysts who are really interested in looking at things from a data-driven perspective. From when I first came in to now, the biggest change has been when Alan Mulally came into the company, and really pushed the idea of data-driven decisions.
Before, we were getting a lot of interest from people who are really very focused on the data that they had internally. After that, they had a lot of questions from their management and from upper level directors and vice-president saying, “We’ve got all these data assets. We should be getting more out of them.” This strategic perspective has really changed a lot of what we’ve done in the last few years.
Gardner: Are we getting to the point where this sort of Holy Grail notion of a total feedback loop across the lifecycle of a major product like an automobile is really within our grasp? Are we getting there, or is this still kind of theoretical. Can we pull it altogether and make it a science?
Cavaretta: The theory is there. The question has more to do with the actual implementation and the practicality of it. We still are talking a lot of data where even with new advanced technologies and techniques that’s a lot of data to store, it’s a lot of data to analyze, there’s a lot of data to make sure that we can mash up appropriately.
And, while I think the potential is there and I think the theory is there, there is also work in being able to get the data from multiple sources. So everything which you can get back from the vehicle, fantastic. Now if you marry that up with internal data, is it survey data? Is it manufacturing data? Is it quality data? What are the things do you want to go after first? We can’t do everything all at the same time.
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