What is Data Quality anyway? If you are a data professional, I’m sure someone from outside our field has asked you that question, and if you’re like me you’ve fallen into the trap of answering in data-speak.
To my listener, I’d guess that the experience was similar to having a customer service rep who has just turned down his simple request justify it by describing byzantine company policies.
There’s a ton of great writing available on data quality, and I in no way mean to disparage it or its value in the field. But in that writing I’ve yet to find a concise and compelling definition that’s useful to non-data professionals. I’ll review one or two prevailing definitions and then offer one that could help us unlock real data quality improvements. Continue reading →
Modern data architectures, by enabling data analytics insights, promise to drive order of magnitude value gains across many business sectors (here, here, and here). Not so long ago, big data presented a daunting challenge. Although tools were plentiful, we struggled to conceptualize the architecture and organization within which to capitalize on those tools. Now solid frameworks have emerged. This post reviews two promising models for modern data architecture, and discusses two key cultural values critical to their successful adoption: drive to solve business challenges and drive for universal data correctness. Continue reading →
A year ago I recounted proceedings from the 2017 EDW World conference, which included release of the Leader’s Data Manifesto (LDM). Last week’s EDW World 2018 served as a one-year status report on the Manifesto. The verdict: there’s still a long way to go, but speakers and attendees report dramatic progress and emergence of shared values supporting data management’s role in enabling success and reducing risk.
To me the most compelling example of progress was the story of the Lopez women, told by Tommie Lawrence, who leads patient data quality efforts at Sharp Healthcare, a major San Diego, Ca, healthcare network. Ms. Lawrence’s team is responsible for data quality related to about six million patient records in the 40 highest priority of Sharp’s ~400 systems containing Patient Health Information (PHI).
A few years ago, Sharp Healthcare had two patients named Maria Lopez*, with birthdays one day apart. One suffered from kidney disease, the other had cancer. After a long wait a kidney was found, and the hospital called the Maria with kidney disease and asked her to come to the hospital for a transplant immediately. During operation prep, an assistant noticed that Maria had cancer, and put a halt to proceedings – it didn’t make sense to give the kidney to someone with cancer. Continue reading →
In response to findings that many “safety-related events were caused by or related to incorrect patient identification”, the Office of the National Coordinator for Health Information Technology (ONC) worked with CMMI to develop the PDDQ Framework in order help organizations implement effective, sustainable data management practices around patient data management.
Groundbreaking? Yes. As a lean framework appropriate for small business the PDDQ shows how you can rightsize the Data Management Maturity Model to match your situation. That it is freely available demonstrates CMMI’s commitment to improving data quality in healthcare. Continue reading →
Of course, any discussion of Agile values starts with the Agile Manifesto. The first sentence declares that Agile development is about seeking better ways and helping others. Then, as if espousing self-evident truths, the founders present four relative value statements. Finally, they emphasize appropriate balance, saying that the relatively less valued items aren’t worthless: implying that they are to be maintained inasmuch as they support the relatively more valued items.
While there is value in the four relative value statements, I believe most successful Agilists value the first and last statements more. So to me, the core Agile values are continuous improvement, helping others, and balance.
There’s a lot written about Agile behaviors, but as I read most is geared toward scrummasters or managers, and most is about transitioning from the waterfall world. Starting from the premise that Agile methods are established, focusing on participants rather than managers, and based on the assumption that behaviors are grounded in values, this post details the values and behaviors I’ve observed of those who succeed as Agile team members.
Even now the business case for a metadata tool seems unclear and difficult to quantify, but it isn’t impossible.
We in the data management business tend to devalue solutions that don’t clearly derive from a coherent top-level view. We seek applications defined from an enterprise architecture, database designs from an enterprise data model, and data elements consistent with the enterprise business glossary.
However, sometimes tactical gains make sense even when the big picture is missing, and tactical successes of metadata for analytics teams can raise consciousness that helps set the stage for evolving data management improvements. Continue reading →
I recently found myself in a series of conversations in which I needed to make a case for dimensional data modeling. The discussions involved a group of highly skilled data architects who were surely familiar with dimensional techniques but didn’t see them as the best solution in the case at hand.
I thought it would be easy to find a quick, jargon free summary of best reporting database design principles aimed at a technical audience. There were a number of good summaries (cited at the end of this post), but none pitched just right for this highly-technical-but-outside-the-data-warehouse-world crowd.
I wanted to raise the dimensional model because, for most business reporting scenarios, it not only delivers on reporting needs, but also helps report developers handle changes to those needs as a side effect of the design.
So these are the notes I prepared for the conversation. They helped us all get on the same page, hopefully they will be useful to others: Continue reading →
Data quality doesn’t have to be a train wreck. Increased regulatory scrutiny, NoSQL performance gains, and the needs of data scientists are quietly changing views and approaches toward data quality. The result: a pathway to optimism and data quality improvement.
Here’s how you can get on the new and improved data quality train in each of those three areas: Continue reading →
Obviously, data management is important. Unfortunately, it is not prioritized in most organizations. Those that effectively manage data perform far better than organizations that don’t. Everyone who needs data to do his/her job must drive change to improve data management.
That was the theme of the recent Enterprise Data World (EDWorld) conference this week. This year’s EDWorld event might be the start of a new vitality and influence for the field, marked by introduction of a Leader’s Data Manifesto.
Over the years, data practitioners struggled for recognition and resources within their organizations. In reaction, they often focused on data “train wrecks” that this neglect causes. This year’s conference was no exception. For example: Continue reading →
Even though it happens annually, teams building new visualizations often forget to think about the effects of turning over from one year to another.
In today’s fast paced, Agile world, requirements for even the most critical dashboards and visualizations tend to evolve, and development often proceeds iteratively from a scratchpad sketch through successively more detailed versions to release of a “1.0” production version. Organized analytics teams evolve dashboards within a process framework that include checkpoints ensuring standards are met for security, reliability, usability, and so on.
A reporting team can build a revolutionary analytics capability enabling unprecedented visibility into operations, and then, if year turnover isn’t included in requirements, experience embarrassing errors and usability challenges in the January after initial deployment. In effect, the system experiences its own Y2.xK crisis, not too different from the expected Y2K crisis 16 years ago. Continue reading →