Dream for inspiration

“Abandon all hope, ye who enter here.” 

In Dante’s Inferno, these words are inscribed above the entrance into hell.  The Roman poet Virgil was Dante’s guide through its nine circles, each an allegory for unrepentant sins beyond forgiveness.

The Very Model of a Modern DQ General will be your guide on this journey through nine of the most common mistakes that can doom your data quality project:

1. Thinking data quality is an IT issue (or a business issue) - Data quality is not an IT issue.  Data quality is also not a business issue.  Data quality is everyone's issue.  Successful data quality projects are driven by an executive management mandate for the business and IT to forge an ongoing and iterative collaboration throughout the entire project.  The business usually owns the data and understands its meaning and use in the day to day operation of the enterprise and must partner with IT in defining the necessary data quality standards and processes.

• This common mistake was the theme of my post: You're So Vain, You Probably Think Data Quality Is About You.

2. Waiting for poor data quality to affect you - Data quality projects are often launched in the aftermath of an event when poor data quality negatively impacted decision-critical enterprise information.  Some examples include a customer service nightmare, a regulatory compliance failure or a financial reporting scandal.  Whatever the triggering event, a common response is data quality suddenly becomes prioritized as a critical issue.

• This common mistake was the theme of my post: Hyperactive Data Quality.

3. Believing technology alone is the solution - Although incredible advancements continue, technology alone cannot provide the solution.  Data quality requires a holistic approach involving people, process and technology.  Your project can only be successful when people take on the challenge united by collaboration, guided by an effective methodology, and of course, implemented with amazing technology.

• This common mistake was the theme of my post: There are no Magic Beans for Data Quality.

4. Listening only to the expert - An expert can be an invaluable member of the data quality project team.  However, sometimes an expert can dominate the decision making process.  The expert's perspective needs to be combined with the diversity of the entire project team in order for success to be possible.

• This common mistake was the theme of my post: A Portrait of the Data Quality Expert as a Young Idiot.

5. Losing focus on the data - The complexity of your data quality project can sometimes work against your best intentions.  It is easy to get pulled into the mechanics of documenting the business requirements and functional specifications and then charging ahead with application development.  Once the project achieves some momentum, it can take on a life of its own and the focus becomes more and more about making progress against the tasks in the project plan, and less and less on the project's actual goal, which is to improve the quality of your data.

• This common mistake was the theme of my post: Data Gazers.

6. Chasing perfection - An obsessive-compulsive quest to find and fix every data quality problem is a laudable pursuit but ultimately a self-defeating cause.  Data quality problems can be very insidious and even the best data quality process will still produce exceptions.  Although this is easy to accept in theory, it is notoriously difficult to accept in practice.  Do not let the pursuit of perfection undermine your data quality project.

• This common mistake was the theme of my post: The Data Quality Goldilocks Zone.

7. Viewing your data quality assessment as a one-time event - Your data quality project should begin with a data quality assessment to assist with aligning perception with reality and to get the project off to a good start by providing a clear direction and a working definition of success.  However, the data quality assessment is not a one-time event that ends when development begins.  You should perform iterative data quality assessments throughout the entire development lifecycle.

• This common mistake was the theme of my post: Schrödinger's Data Quality.

8. Forgetting about the people - People, process and technology.  All three are necessary for success on your data quality project.  However, I have found that the easiest one to forget about (and by far the most important of the three) is people.

• This common mistake was the theme of my post: Data Quality is People!

9. Assuming if you build it, data quality will come - There are many important considerations when planning a data quality project.  One of the most important is to realize that data quality problems cannot be permanently “fixed" by implementing a one-time "solution" that doesn't require ongoing improvements.

• This common mistake was the theme of my post: Are You Afraid Of Your Data Quality Solution?

Knowing these common mistakes is no guarantee that your data quality project couldn't still find itself lost in a dark wood.

However, knowledge could help you realize when you have strayed from the right road and light a path to find your way back.

출처 : http://www.ocdqblog.com/home/the-nine-circles-of-data-quality-hell.html

Data Quality와 관련하여 쓰여진 글인데, 한번쯤 읽고 생각해보고 넘어가는 것도 좋을법한 내용이라 옮겨봅니다. ^^

강신장 사장님께서 집필하신 "오리진이 되라"라는 책입니다.

얼마전에 구매해서 읽었는데요. 참 많은 생각을 하게 하는 책인듯 합니다.

강신장 사장님이 삼성경제연구소(SERI)에서 다년간 지식경영실장으로 있으면서 기업 CEO 대상으로 창의력 개발 프로그램을

운영한 노하우를 바탕으로 집필되었습니다.

개인적으로 이 책을 한마디로 표현해보자면, "어떤 제품을 만드는데 있어서 철학을 가지고 있는가?"라고 정리해봅니다.

요즘과 같은 시기를 흔히 Digital Convergence 시대라고들 합니다. 이런 Convergence는 단순히 기술력을 통해서만 이루어내는

것이 아니라, 다양한 분야에 대한 통찰과 융합으로 이루어지는 것이라고 설명해줄 수 있는 책입니다.

책 내용도 어렵지 않고, 문장 또한 대화체(?) 같이 쉽게 쓰여져 있으니, 가볍게 읽을 수 있습니다.

책은 가볍게 읽을 수 있지만, 독서 후의 여운은 가볍지 않음을 느낄 수 있을듯~ ^^