It’s been a truism that data is a resource, but to prove it you just have to follow the money. As the illustration shows, the vast majority of corporate market value draws from intangible assets. Just as money is an abstraction that represents wealth, data is an abstraction that represents these intangible assets.
It’s year three after initial rollout of the Leader’s Data Manifesto (LDM). Since then, many widely publicized events have highlighted the value of data and metadata, and the importance of sound data management (here, here, and here). Recently at Enterprise Data World, John Ladley, Danette McGilvray, James Price, and Tom Redman presented this year’s LDM update. They reintroduced the Manifesto, recounted events of the past year, discussed strategy for the coming year, and issued a call to action for data professionals. Continue reading →
Data scientists spend most of their time doing data integration rather than gathering insights. In my interview with data scientist Yan Li, she said that data collection and prep takes at least 70% of her time. Obviously, there’s a lot of integration work to do on data that’s new to analytics efforts, but not every analysis uses brand new data. Organizations can improve analytics efficiency by staging commonly used data pre-integrated for data science.
For years, large organizations have supported data warehouses, but prevailing data warehousing practices often fail when faced with “big data” volume and velocity. Still, warehousing teams in large organizations can pre-prep frequently-used internal data. Examples include reference and master data, production and sales records, and so on. Continue reading →
Reading articles about data anonymization makes it clear that it is not an entirely effective security measure (here and here), but still part of a robust security capability, and required if your organization is affected by GDPR. (I use “anonymization” as a general term encompassing techniques that de-identify personal data within a given data set.)
But there’s a positive side of anonymized data that hasn’t received much press. Providing anonymous data to senior managers who don’t need access to personal data can encourage them to take a broader perspective, and thereby bring new energy to fact-based senior planning and analysis. Continue reading →
“At least 84 percent of consumers across all industries say their experiences using digital tools and services fall short of expectations.”* That quote headed a recent article by David Roe on the role of data integration in digital workplace apps. However, the opening quote reflects the pervasive dearth of integrated data among the companies most of us frequent.
We’ve all experienced the effects. Last week I was in a fender bender. Due to a mixup I didn’t have my insurance card with me, so I called the insurance company to get the info. They had no record of me associated with my car. It turned out that my car is insured under my wife’s name, hers under mine. Although I’ve been their customer for 25 years, and was driving my own car, they couldn’t give me insurance info. Sure, they were following good security practices. But I’m not letting them off the hook. Continue reading →
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 →
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 →