Writer, classic rock lover, dog rescuer, company founder, software exec, and now independent management consultant--I speak, blog, and pester my friends about these topics. My current focus is getting IT and business organizations to collaborate more effectively and not kill each other. I also talk and write about big data, why analytics is fundamentally strategic, how to pitch business execs on IT projects, and why not to buy a dog from a pet store.

I’ve lived in London, Paris, and Sydney, but call L.A. home. #weatherwimp. I cultivate an organic vegetable garden and friends with issues. I’ve written three books, co-authored a fourth, and contributed to a bunch more. (I have another one in my head waiting to come out, but it’s crowded in there right now.) I prefer Def Leppard to Bon Jovi, mashed potatoes to brown rice, fly fishing to golf, Pinot Noir to Zinfandel, and nice people to assholes. I have a tattoo. I’m not telling you where. I feel guilty that I go hot and cold on social media, that I don’t spend enough face time with my friends, that my French is rusty, and that I ate that whole bag of Kirkland peanut butter cups in less than a week. I have to live with those things.

Data Governance and the Occasional Vow of Celibacy

Data Governance and the Occasional Vow of Celibacy

In which Jill explains that withholding a little data can get people’s attention.
Recently the chair of a data governance council at a bank asked for my thoughts on how to handle dormant data. The data—some cryptic financial rollup tables that hadn’t been accessed in a few months—was taking up space on the data warehouse. More importantly it would soon be subject to a major financial metadata effort. My client didn’t want his staff spending time defining data that no one was using.

(When the topic of de-commissioning data comes up I think about Information Lifecycle Management (ILM), which is the management and storage of data as it changes over time, from its initial creation through its eventual use and disposition. We’ve talked a lot with clients about data being dynamic and having a lifecycle. We call this lifecycle the “data supply chain.”)

I asked my client a series of questions about the data. Why was it loaded onto the data warehouse in the first place? Did it map to critical business requirements? Who’d requested it? What business processes did it support? How latent was it? Ultimately after some discussion I recommended that my client pull the data from the data warehouse and archive it. Then we’d sit back and await the backlash.

A risky move? You bet. I compare this strategy to the one used in the Aristophanes play “Lysistrata.” As you’ll remember from high school English, in Lysistrata the women of Greece decide en masse to withhold sex from their husbands until there’s an end to the Peloponnesian War. This was the ultimate power play and gave the term “cease fire” a whole new meaning.

A week later my client received an e-mail from a data analyst in the finance department inquiring about the missing data. It seemed as if this analyst used the data to create quarterly top line measure reports for the CFO. She’d just returned from a 3-month sabbatical and wondered what had happened to her tables.

This conversation resulted in the addition of two new guiding principles for the data governance council:

1: Usage of the data on the warehouse would be regularly monitored.

2: Data unused for a period of 4 months or longer would be archived unless the data steward requested an exception.

These new guiding principles invited a new level of—dare I say—intimacy with less-visible data by data stewards who had until then been focused on high-profile or heavily-queried tables. But just because the data wasn’t widely used didn’t mean it wasn’t beholden to the standard policy-making and oversight processes established by the data governance council.

The lesson? Sometimes you’ve got to take something away to recognize its real value. Just ask those randy Greeks!

photo by nan palmero via Flickr (Creative Commons license)

Big Data and Human Potential

Big Data and Human Potential

My Regression Theory

My Regression Theory