S1: The Data Provocateurs’ Boot Camp
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  Thomas Redman   Thomas Redman
The Data Doc
Data Quality Solutions


Thursday, April 6, 2017
01:15 PM - 04:30 PM

Level:  Business/Strategic

Most companies and government agencies address data quality reactively after errors are made, and as a direct result, suffer from bad data. To date, it has taken a special person to challenge the status quo within his or her work team, address data quality by “getting in front” of the issues, making a huge improvement, and, in effect, showing the rest of the company what is possible. I call this person the “data provocateur.”

I recently introduced the term publicly (see for example, “Data Quality Should Be Everyone’s Responsibility” in HBR), and many people have asked “How can I become a data provocateur?” This mini-boot camp provides the answer. It aims:

  • To provide you with the skinniest possible set of materials needed to become an effective data provocateur and
  • To empower and embolden you to move forward.

1. An exemplary provocateur: Bob Pautke at AT&T

  • I’ll go into detail about Bob’s thinking, his first steps, how AT&T built on his efforts, and the results he and AT&T obtained.

2. What is a “data provocateur?”

  • Why data quality problems arise? (Ans: The rising middle manager)
  • The hidden data factory
  • Provocateur defined, with a special emphasis on soft skills
  • Discussion: Can you fill the role?

3. A Four-Step Process for Becoming a Provocateur

  • Step 1: Answer “Do I (we) have a data quality problem?”
    • The Friday Afternoon Measurement
    • Other impacts of bad data
    • Answering the question

  • Step 2: Clarify, document, and communicate customer needs.
    • Step a: Name the most important customers (including required documentation)
    • Step b: Learn how they use the data (including required documentation)
      • Conduct a customer needs workshop
    • Step c: Determine required features and quality requirements (including required documentation)
    • Step d: Prepare a Customer Requirements document
    • Discussion: Put yourself in the customer role. What would it take to complete such an analysis for yourself and your work team and communicate your requirements to a data creator?

  • Step 3: Make improvements to close the gaps.
    • Quality Improvement and the Scientific Method
    • The Quality Improvement Cycle
    • Discussion: Can you do this work?

  • Step 4: Summarize the results.
    • What success looks like
    • Presenting results
    • Building support to “get to the next level”
    • Discussion: Any questions about what is required
Wrap Up and Next Steps
  • A Promise to Yourself: Make a list of the steps you propose to take in the next ten, 30 and 60 days.

Tom Redman, the “Data Doc," helps companies, including many of the Fortune 100, improve data quality. Those that follow his innovative approaches enjoy the many benefits of far better data, including far lower costs. His recent article, “Data's Credibility Problem," (Harvard Business Review, December 2013) showcases what's possible. Tom's "Data Driven: Profiting from Your Most Important Business Asset" (Harvard Business Press, 2008) is the guiding light for companies seeking to build their futures in data. Tom started his career at Bell Labs, where he led the Data Quality Lab. He has a Ph.D. in Statistics and two patents.

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