In my experience, data management is both a mission critical and an undervalued capability. Perhaps recent customer data losses and regulatory initiatives like GDPR tend to raise the stock of data maturity efforts, but it remains undervalued. For example, any Fortune 1000 firm building end-to-end processes finds that much of the cost goes to translating data from different systems that integrate into the process.
Today we have available stage models like CMMI’s Data Management Maturity Model (DMMM) which, as I’ve written, help organizations assess an organization’s maturity level. However, the DMM model aims to assess data maturity at a single agency. It lacks mechanisms to compare multiple agencies or business functions, and therefore can be difficult to translate to prioritized plans for improvement.
Of course no one would do that on purpose, but I as a consultant over many years I’ve often seen it. A vendor fulfills a contract to the letter, which unfortunately allows them to deliver required reports in various, sometimes changing, formats with suspect data quality. The customer company absorbs these costs, leaning on the data analyst to update PowerPoint decks on schedule before the next monthly management meeting in spite of the extra programming work.
These contracts have been for various goods and services, but almost every business contract today is also a contract for data. If a regional gas company hires a vendor to inspect residential lines, then I suspect it wants reports showing inspections conducted and results; a healthcare firm that sends nurses on house calls needs data detailing call schedules and results; and so on.
Companies that supply goods or provide services often don’t feature data management as a core competency, and the quality of their reporting often doesn’t match the quality of their goods or services. Someone in the customer organization has to code around every addition or omission of an expected Excel column, every “N/A” in a numeric field, and every unexpected change from imperial to metric units. Continue reading →
Data quality improvements follow specific, clear leadership from the top. Project leaders count data quality among project goals when senior management encourages them to do so with unequivocal incentives, a common business vocabulary, shared understanding of data quality principles, and general agreement on the objects of interest to the business and their key characteristics.
Poor data quality costs businesses about “$15 million per year in losses, according to Gartner.” AsTendü Yoğurtçuputs it, “artificial intelligence (AI) and machine learning algorithms are only as effective as the data they use.” Data scientistsunderstandthe difficulties well, as they spend over 70% of their time in data prep.
Recent studies report that data entry typos are the largest source of poor data quality (here and here). My experience says otherwise. From what I’ve seen, operational data is generally good, and data errors only appear when data changes context. In this post I’ll detail why data quality is management’s responsibility, and why data quality will remain poor until leadership makes it a priority.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 →
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 →
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 →
The term “trust” implies absolutes, and that’s a good thing for relationships and art. However, in the business of data management, framing trust in data in true or false terms puts data governance at odds with good practice. A more nuanced view that recognizes the usefulness of not-fully-trusted data can bring vitality and relevance to data governance, and help it drive rather than restrict business results.
The Wikipedia entry — for many a first introduction to data governance — cites Bob Seiner’s definition: “Data governance is the formal execution and enforcement of authority over the management of data and data related assets.” The entry is accurate and useful, but words like “trust”, “financial misstatement”, and “adverse event” lead the reader to focus on the risk management role of governance.
However, the other role of data governance is to help make data available, useful, and understood. That means sometimes making data that’s not fully trusted available and easy to use. Continue reading →