Blog: Jill Dyche

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Having The Right Gear

In which Jill hangs out with a bunch of bikers and learns something new.

My dog Lu and I went hiking last Sunday after the rains stopped. At the top of the trail, there’s an abandoned missile site, a relic from the cold war. The site has been converted by the Santa Monica Mountains Conservancy into a picnic area, and is a regular meeting place for weekend warriors, especially hardcore mountain bikers.

I was sitting with a group of them comparing protein bars when a disheveled looking biker arrived. He didn’t sport the standard issue biking gear of helmet, quick-dri bike shorts, and clip-on shoes, but instead wore knee-length cargo pants and a tank top. His bike looked more like a 10-speed than a mountain bike. His head was helmet-free.

“Guys, can I get to Sullivan Canyon from up here?” he asked the rough-hewn throng of diehards.

“Not on that, you can’t,” replied one, gesturing toward the 10-speed.

The group went on to explain that Sullivan Canyon had been washed out. In some places the mud was a foot deep and you’d have to walk your bike. There were rockslides to navigate. The novice’s bike clearly wouldn’t hold up on Sullivan. There were suggestions of easier ways down the trail.

The novice politely thanked the group and took off in the direction of Sullivan Canyon. I admired the bikers’ forbearance as they watched him ride off.

The experience reminded me of a conversation I had a while ago with an ETL developer. He was trying to reconcile customer data from his client’s different distributors and business partners. Of course, the data varied wildly by source system, and he was using the ETL tool to write custom data cleansing code. It was brutal work, and when he explained that his client was running out of money for the project, I wasn’t surprised.

I politely suggested that a data quality tool could significantly reduce his manual efforts. A tool would let him profile the incoming data from the different data sources, then subsequently establish rules to handle anomalies. Over time, he could refine the rules and save himself a lot of work. “We’re too far into it,” was all he would say.

I couldn’t help thinking that this was misinformed. Had my friend known about but rejected the data quality tool in order to perpetuate his contract? Did he need an update on the features of these tools? Or did he, like the novice bike rider, assume the conditions simply weren’t that bad?

Makes me wonder if the novice biker made it to the bottom of Sullivan Canyon and, if he did, what cuts and bruises he’d have to show for the ride. And whether he’d think they were worth it.

Technorati Tags: data quality, ETL, Business Intelligence, data governance

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