The Chicken Man versus the Data Scientist

In my previous post Sisyphus didn’t need a fitness tracker, I recommended that you only collect, measure and analyze big data if it helps you make a better decision or change your actions.

Unfortunately, it’s difficult to know ahead of time which data will meet that criteria. We often, therefore, collect, measure and analyze any data that might help us achieve our goals. Along the way we sometimes make dubious connections between the results we are seeing and the data we are analyzing, leading us afoul of smart thinking. Read More »

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Achieving persistent data governance, pt. 3: find a visionary

In the first two parts of my series (part 1 and part 2), I talked about common ways data governance projects can (and do) fail and offered collaboration between teams as a key to achieving success.

In this post, we’ll examine the importance of realizing the full value of your team members. With just a little thought and effort, you can leverage your team’s existing talents to ensure your project’s success.

Strategy #3: Select a data governance leader who has a long-term vision and who can execute that vision in a step-wise fashion while delivering value to the business at each increment. Read More »

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Re-thinking the design choices of application data quality

If we look at how most data quality initiatives start, they tend to follow a fairly common pattern:

  • Data quality defects are observed by the business or technical community
  • Business case for improvement is established
  • Remedial improvements implemented
  • Long-term monitoring and prevention recommended
  • Move on to the next data landscape

Ok, I know not all projects follow that path but for most projects there is a definite sense of resolving the issue many years after the application was originally conceived. Read More »

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Data lessons from Pizza Hut

Thought leaders and pundits like me espouse the virtues of big data. Although you'll get no argument from me on the potential benefits of this essential trend, it's important to remember that there is still tremendous value from using basic customer information.

Driving home from a networking event on the Vegas Strip a few weeks ago, I was feeling far too lazy to cook something at home. I asked Siri for the number of my closest Pizza Hut and, within seconds, was calling the store.

What happened next was, to say the least, a bit surprising.

Me: Hello. I'd like to place an order for pickup.

Pizza Hut Guy (ostensibly recognizing my phone number):  Hello, Phil. Would you like another personal pan pizza with pineapple and ham?

Me (bewildered): Well...actually, yes. That would be great.

Pizza Hut Guy: Great. The total is $3.49. It will be ready at 8:05 pm.

Me: Wow. That was easy.

Pizza Hut Guy: Thanks. See you soon.

The entire process took under one minute and I was back listening to Rush in no time. Read More »

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Sisyphus didn’t need a fitness tracker

In his pithy style, Seth Godin’s recent blog post Analytics without action said more in 32 words than most posts say in 320 words or most white papers say in 3200 words.

(For those counting along, my opening sentence alone used 32 words).

Godin’s blog post, in its entirety, stated:

“Don’t measure anything unless the data helps you make a better decision or change your actions. If you’re not prepared to change your diet or your workouts, don’t get on the scale.”

We are often told to collect data from wherever we find it. In the era of big data, that's is pretty much anywhere you look. Advocates of the quantified self tell us to measure every aspect of our lives on a daily basis, such as how many hours we sleep, how much we weigh, how many calories we consume with each meal, and how many calories we burn with each exercise. All that data and all those measurements must have value, right?

Being data-driven doesn’t mean you should be driven to collect data. It means you should be driven to do something with the data you collect. Otherwise, you are a modern-day Sisyphus pushing the big data boulder up the hill just to watch it roll back down again, repeatedly. Outfitting the mythological Sisyphus with a fitness tracker to measure how fast he pushes the boulder up the hill, and how many calories he burns while doing so, wouldn’t have changed the pointless tedium of his punishment. Sisyphus didn’t need a fitness tracker since no amount of data or number of measurements was going to help him make a better decision or change his actions.

My 320 word post concludes with these 32 words:

Don’t be Sisyphean in your approach to big data and analytics. Be Godinian—only collect, measure, and analyze big data if it helps you make a better decision or change your actions.

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Cracking the code to successful conversions: infrastructure management

How many projects have you worked on that forgot to test size, volume, and conduct load balancing in a newly converted environment? I have worked on a few of those types of projects. I know in a data warehousing effort, we always check any servers and databases, based on load, and query performance. Read More »

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Achieving persistent data governance, pt. 2: focus on trouble areas

In the first part of my series on ensuring data governance success, I mentioned the importance of linking different teams. Collaboration is an often overlooked, but critically important, part of having a successful project. Not only that, coordination and cooperation helps to create the right culture of data mindfulness throughout the organization.

In this week’s tip, I want to expand on that concept and apply it to teams that might be especially difficult to reach and get to work together. Read More »

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Lack of knowledge and the root-cause myth

A lot of data quality projects kick off in the quest for root-cause discovery.

Sometimes they’ll get lucky and find a coding error or some data entry ‘finger flubs’ that are the culprit. Of course, data quality tools can help a great deal in speeding up this process by automating the data assessment phase and putting in place routine monitoring for future detection.

However, are we using these tools to their full capacity?

In most cases, when you take a much higher ‘birds-eye view’ of the problem, the real cause is often due to a lack of knowledge about the underlying data and processes that support the services of the business. Read More »

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