Data is asset rather than cost see how Ford cars embrace big Data

Editor's note: Big data, you say, I say, everybody is saying. The question is how to effectively embrace big data? How do you really make good use of big data? Let it serve the enterprise.

In the case of car manufacturers, how to use large data to improve product quality, improve production, streamline business processes, improve business models, and improve efficiency? Look at the data as assets rather than costs, embrace the big data and see how Ford is doing at the forefront.

Ford has been using the strength of large data analysis to guide companies to improve their corporate performance. Ford explores the best process metrics, improving or helping to change its business model through massive processing of data and detailed output data from inside the vehicle.

Ford Research and Advanced Engineering Predictive analysis (predictive Analytics for Ford study and Advanced UB) technical leader Michael Cavarreta (Michael Cavaretta led Ford's multiple data analysis projects to break the island of internal data and best define Ford's most productive dataset.

Ford has successfully aggregated customer feedback, using all the internal data to predict how Ford could use the new technology to maximise the car's performance.

Cavarreta, a member of the Group of Experts at the open organization meeting in Newportbutch, Calif., was a contributor to the Open organization Congress, and Ford's speech titled "Big Data-the transition we need to embrace today". Cavarreta in this paper explains how large data should be used to foster enterprise transformation by effectively digging more types of data, and further improving process, quality control and customer satisfaction.

We share some of the interviews we've edited from Interarbor FX chief analyst Dena Gadna Dana Gardner:

Q: What is the difference between the data and the data analysis now obtained 5 years ago?

A: The biggest difference is the inexpensive availability of storage and processing power. A few years ago, people focused on filtering the storage datasets used for long-term analysis. Now that our mindset has changed, we can store as much data as we can and use our storage advantages to improve our business processes.

A great change in attitude

Q: How did we do this? What is the process of supporting benefits?

The process of supporting benefits is a great change in the attitude of the enterprise, especially in the internal IT department of large enterprises. Our new idea is that it does not need to spend a lot of time sifting through the data that needs to be stored, without worrying about the costs and other things that are involved in the asset. It's value to be able to store data, trace data, and get different insights from it. Of course, these are all derived from cheap storage and we have access to parallel processing machines with great software.

I like to talk to people about the possibilities that big data offers, and I often say that I've never met someone who gives me too much data, you can use all the information and answer a variety of questions because you don't have to worry about what's missing. You have all the data.

You can ask 100 questions, each of which uses only a very small portion of the data. Those questions may be used in different parts of the data, a very small part, but even the smallest part is completely different. If you think "we're going to answer 20 important questions, just keep this part of the data," then you'll miss a lot of information and you won't get any value. ”

"We are a big proponent of aggregating information, firmly believe that even if there is no large data scale data sets have great value, we do not have to dig deep data, do not need to obtain more detailed information, but expand the width of the data can be extended to other internal data sets, connect different business areas, and expand to external data sets. ”

"Most of the time, you can get only hundreds of thousands of or millions of of the data, but you can get the size of a large dataset in a few days, adding different information and adding to the information." ”

(Responsible editor: The good of the Legacy)