There is a reason the phrase "drowning in data" joined the popular lexicon. Not only is data everywhere, but the speed at which it can be captured seems to multiply exponentially with every turn of a calendar page.
Indeed, the sheer volume of data can be overwhelming if there's no strategy in place to make it work for your organization.
"More data is not more better," says Steve Wise, vice president of statistical methods for manufacturing quality software provider InfinityQS International, Fairfax, Va.
It may seem a surprising statement coming from Wise, who is a numbers guy. Earlier in his career, he was an industrial statistician at Honeywell Aerospace, and while employed at Boeing Co. (IW 500/14), he co-authored industry standard D1-9000 Advanced Quality System for the aerospace company's suppliers.
Wise's statement isn't meant to suggest that less data are better. His point is that there should be an exit strategy, a purpose behind the data collection. Typically, when it comes to quality-related data, the purpose falls into any of three categories: compliance, learning or process control.
What shouldn't be overlooked is the story the data are telling. "The data will start talking -- up is good, down is bad, for example," Wise says. It can help you better assign where tolerances need to go or decide whether to make or buy a component. "Of course, if you don't have the infrastructure to do something about [the story], you will just get frustrated," he adds.
The InfinityQS vice president describes statistics as the "science of predicting the future." He also proposes the idea that data have two lives. The first life is what data are telling you "right now." It is the data needed by the people responsible for making good product in the moment. Then there is the second life of data, which is the historical data. Mining that data provides organizations with opportunities to improve conditions for the future, he says.
"If you don't look at the data again, you are missing the boat," Wise says.
Making Smarter Data Decisions
Data is only as helpful as you make use of it to fix problems. During its September users conference, quality software provider InfinityQS International outlined a series of who, what, when, where, why and how questions that provide structure for the data decision-making process:
- What data should be collected?
- Who should do the collecting?
- When is the data being collected?
- Where is the data saved, and can those who need it get to it?
- Why are you collecting this data?
- How are you collecting it?
- And not to be overlooked: When are you acting on the data?