Posts

I just love Lora Cecere

I confess. I just love Lora Cecere. I’ve been her client; she’s been my boss, and I call her a trusted friend and advisor. We’ve enjoyed conversations and exchanged ideas in hallways, convention centers, airports, taxi cabs, hotel bars and many restaurants across the country and gabfests by phone. One of my absolute favorite Lora moments came a couple of years ago at our old AMR Research headquarters in Boston when I stuck my head into her office and saw birthday cards on her desk. Ten minutes later, the two of us were sitting across the street in the South train station sharing a slab of cake, two coffees, a live chamber music performance and time just to sit, chat and celebrate her day. Read more

Kill the mouse, Daddy!

Those were the words my son used a few years ago when he spotted a baby mouse limping across our kitchen floor. It was so lame I could pick it up and put it outside, but the visceral reaction he had was the same one many of us have to bad data. Read more

The Biggest Philosophical Debate in Data Management

In a recent webcast I did with Jim Harris, he talked about two views of data quality: provider centric and consumer centric. According to the provider centric view, data are just digital representations of real world things. If the representations are accurate, then you can use them for anything. In other words, data is good as long as data providers do their job right. The consumer centric view says, data is good only if it’s fit for use, i.e., if it meets the declared needs of consumers. Read more

Does Big Data Mean Good Data?

Welcome the newest, biggest, baddest buzzword to the technology stage – Big Data. The acceleration of data created through social media, tracking devices and the internet is giving companies a unique opportunity to learn more about their customers, target their marketing and sales campaigns more effectively and become more efficient. Read more

Who in the world needs a data warehouse with bad data in it?

I just read a recent article in Information Management entitled “Who in the World Needs a Data Warehouse?” On seeing the title, I immediately thought this would be an article about how companies can simply load their data in memory to avoid building a data warehouse entirely. As I suspected, that is the author’s proposed option. The article listed off a variety of issues with building a data warehouse, including capturing requirements and dealing with scope creep, ETL design complexity, data integration and data quality. In several prior posts, I discussed ways to handle capturing requirements and the ETL through business modeling and automation. But in this post I want to discuss data quality. The article says “data that is housed in the data warehouse is often either incorrect or inconsistent.” If in-memory analytics is the answer to this problem, I ask: what’s the difference if you load incorrect and inconsistent data into memory? How does simply moving it off disk fix this problem? The answer is: it doesn’t! Read more

United States Constitution and Data Governance

In 1787, there was such a thing called “The United States of America”, but it didn’t have a lot of power. The real power was held by the 13 former colonies, or states, each of which is a sovereign nation with its own laws, currency, and taxes. The United States of America was formed 6 years before that, driven by the need to fight a common enemy: Great Britain. So it had an army. Plus, it could negotiate treaties with other countries. But that was about the extent of its power. Read more

Much Ado About Loading

Recent chatter in the data warehousing industry has again raised the topic of data loading performance. With each new version of a database used for warehousing, the vendor trumpets their performance, trying to one-up their database competitor. There can be no doubt that physical movement of data from file to database is a critical component of the load, but is it really where the bulk of the time is spent on the nightly refresh? This raises the question: what does it mean to load the warehouse? Read more

Driving Data Governance Past Cultural Roadblocks

Recently, I wrote about getting caught in such data governance traps as pursuing quality for the sake of quality and imposing change on an organization without considering the cultural impact. In this post, I’ll explore the issue of corporate culture and what it takes to overcome barriers to change. Read more