Being a data driven company is something that we pride ourselves on at Ford. If you turn back the clock to World War II, you will see Henry Ford II hiring 10 young veterans from the U.S. Army Air Force’s Statistical Control Command – a group that we call the “Whiz Kids.” This team of veterans brought the lessons of organizing wartime logistics for the U.S. military to the problems of running a huge manufacturing enterprise.
Fast forward to 2006 when Alan Mulally took the helm as Ford’s CEO. His “data will set you free” mantra has been a guiding philosophy for our company. We now have more than 250 practitioners on three continents, and advanced analytics is used heavily across our operations to run the business, inform decisions and determine strategy.
As a Data Scientist at Ford for more than 15 years, I've seen firsthand the impact that analytics can have on the bottom line. While we’ve only skimmed the surface of Big Data, here are some lessons that we've learned along the way.
1. Don’t worry about failing
The number one key to getting value from Big Data is experimentation. Some of your Big Data experiments are going to fail. There is no getting around it. But some of the best insights come from the biggest failures. My best advice is to experiment often, fail quickly and apply the lessons you’ve learned to the next experiment.
2. Take an iterative approach
Similar to agile software development, the best Big Data strategy takes an iterative approach. If you’re looking to break down data silos, start with a few of your largest datasets and more important questions and start your analytics initiatives quickly. Look to continuously add value by answering more questions and adding more data sources. Never stop experimenting.
3. There’s no “one size fits all”
It can be tough to get started in Big Data. Many large enterprises have found it challenging to push analytics into their decision-making. Many large enterprises have found it challenging to push analytics into their decision-making. There is a lot of information out there about how to structure your organization and get the most from these new tools and technologies. Some organizations want to have a strong central group to focus on enterprise-wide problems. Others want to keep the analytics in the business functions to develop deep business knowledge and understanding. My advice is to look at how finance and IT are structured in your organization and incorporate the best of both.
Michael Cavaretta is a Data Scientist and Manager at Ford Motor Company. Read more about his expertise in Big Data, Text Mining, and Information Retrieval in his bio.
Check out more of Michael's posts on Big Data