Here is an interview with one of the chief evangelists to data quality in the field of Business Intelligence, Jim Harris who has a renowned blog at http://www.ocdqblog.com/. I asked Jim about his experiences in the field on data quality messing up big budget BI projects, and some tips and methodologies to avoid them.
No one likes to feel blamed for causing or failing to fix the data quality problems- Jim Harris, Data Quality Expert.
Ajay- Why the name OCDQ? What drives your passion for data quality? Name any anecdotes where bad data quality really messed up a big BI project.
Jim Harris – Ever since I was a child, I have had an obsessive-compulsive personality. If you asked my professional colleagues to describe my work ethic, many would immediately respond: “Jim is obsessive-compulsive about data quality…but in a good way!” Therefore, when evaluating the short list of what to name my blog, it was not surprising to anyone that Obsessive-Compulsive Data Quality (OCDQ) was what I chose.
On a project for a financial services company, a critical data source was applications received by mail or phone for a variety of insurance products. These applications were manually entered by data entry clerks. Social security number was a required field and the data entry application had been designed to only allow valid values. Therefore, no one was concerned about the data quality of this field – it had to be populated and only valid values were accepted.
When a report was generated to estimate how many customers were interested in multiple insurance products by looking at the count of applications per social security number, it appeared as if a small number of customers were interested in not only every insurance product the company offered, but also thousands of policies within the same product type. More confusion was introduced when the report added the customer name field, which showed that this small number of highly interested customers had hundreds of different names. The problem was finally traced back to data entry.
Many insurance applications were received without a social security number. The data entry clerks were compensated, in part, based on the number of applications they entered per hour. In order to process the incomplete applications, the data entry clerks entered their own social security number.
On a project for a telecommunications company, multiple data sources were being consolidated into a new billing system. Concerns about postal address quality required the use of validation software to cleanse the billing address. No one was concerned about the telephone number field – after all, how could a telecommunications company have a data quality problem with telephone number?
However, when reports were run against the new billing system, a high percentage of records had a missing telephone number. The problem was that many of the data sources originated from legacy systems that only recently added a telephone number field. Previously, the telephone number was entered into the last line of the billing address.
New records entered into these legacy systems did start using the telephone number field, but the older records already in the system were not updated. During the consolidation process, the telephone number field was mapped directly from source to target and the postal validation software deleted the telephone number from the cleansed billing address.
Ajay- Data Quality – Garbage in, Garbage out for a project. What percentage of a BI project do you think gets allocated to input data quality? What percentage of final output is affected by the normalized errors?
Jim Harris- I know that Gartner has reported that 25% of critical data within large businesses is somehow inaccurate or incomplete and that 50% of implementations fail due to a lack of attention to data quality issues.
The most common reason is that people doubt that data quality problems could be prevalent in their systems. This “data denial” is not necessarily a matter of blissful ignorance, but is often a natural self-defense mechanism from the data owners on the business side and/or the application owners on the technical side.
No one likes to feel blamed for causing or failing to fix the data quality problems.
All projects should allocate time and resources for performing a data quality assessment, which provides a much needed reality check for the perceptions and assumptions about the quality of the data. A data quality assessment can help with many tasks including verifying metadata, preparing meaningful questions for subject matter experts, understanding how data is being used, and most importantly – evaluating the ROI of data quality improvements. Building data quality monitoring functionality into the applications that support business processes provides the ability to measure the effect that poor data quality can have on decision-critical information.
Ajay- Companies talk of paradigms like Kaizen, Six Sigma and LEAN for eliminating waste and defects. What technique would you recommend for a company just about to start a major BI project for a standard ETL and reporting project to keep data aligned and clean?
Jim Harris- I am a big advocate for methodology and best practices and the paradigms you mentioned do provide excellent frameworks that can be helpful. However, I freely admit that I have never been formally trained or certified in any of them. I have worked on projects where they have been attempted and have seen varying degrees of success in their implementation. Six Sigma is the one that I am most familiar with, especially the DMAIC framework.
However, a general problem that I have with most frameworks is their tendency to adopt a one-size-fits-all strategy, which I believe is an approach that is doomed to fail. Any implemented framework must be customized to adapt to an organization’s unique culture. In part, this is necessary because implementing changes of any kind will be met with initial resistance, but an attempt at forcing a one-size-fits-all approach almost sends a message to the organization that everything they are currently doing is wrong, which will of course only increase the resistance to change.
Starting with a framework as a reference provides best practices and recommended options of what has worked for other organizations. The framework should be reviewed to determine what can best be learned from it and to select what will work in the current environment and what simply won’t. This doesn’t mean that the selected components of the framework will be implemented simultaneously. All change comes gradually and the selected components will most likely be implemented in phases.
Fundamentally, all change starts with changing people’s minds. And to do that effectively, the starting point has to be improving communication and encouraging open dialogue. This means more of listening to what people throughout the organization have to say and less of just telling them what to do. Keeping data aligned and clean requires getting people aligned and communicating.
Ajay- What methods and habits would you recommend to young analysts starting in the BI field for a quality checklist?
Jim Harris- I always make two recommendations.
First, never make assumptions about the data. I don’t care how well the business requirements document is written or how pretty the data model looks or how narrowly your particular role on the project has been defined. There is simply no substitute for looking at the data.
Second, don’t be afraid to ask questions or admit when you don’t know the answers. The only difference between a young analyst just starting out and an expert is that the expert has already made and learned from all the mistakes caused by being afraid to ask questions or admitting when you don’t know the answers.
Ajay- What does Jim Harris do to have quality time when not at work?
Jim- Since I enjoy what I do for a living so much, it sometimes seems impossible to disengage from work and make quality time for myself. I have also become hopelessly addicted to social media and spend far too much time on Twitter and Facebook. I have also always spent too much of my free time watching television and movies. I do try to read as much as I can, but I have so many stacks of unread books in my house that I could probably open my own book store. True quality time typically requires the elimination of all technology by going walking, hiking or mountain biking. I do bring my mobile phone in case of emergencies, but I turn it off before I leave.
Jim Harris is the Blogger-in-Chief here at Obsessive-Compulsive Data Quality (OCDQ), which is an independent blog offering a vendor-neutral perspective on data quality.
He is an independent consultant, speaker, writer and blogger with over 15 years of professional services and application development experience in data quality (DQ), and business intelligence (BI),
Jim has worked with Global 500 companies in finance, brokerage, banking, insurance, healthcare, pharmaceuticals, manufacturing, retail, telecommunications, and utilities. Jim also has a long history with the product that is now known as IBM InfoSphere QualityStage. Additionally, he has some experience with Informatica Data Quality and DataFlux dfPower Studio.