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 →
Over the past year I’ve reviewed what seem like countless plans for enterprise data warehouses. The plans address real problems in the organizations involved: the organization needs better data to recognize trends and react faster to opportunities and challenges; business measures and analyses are unavailable because data in source systems is inconsistent, incomplete, erroneous, or contains current values but no history; and so on.
The plans detail source system data and its integration into a central data hub. But the ones I’m referring to don’t tell how the data will be delivered, or portray a specific vision of how the data is to drive business value. Instead, their business case rests on what I’ll call the “railroad hypothesis”. No one could have predicted how the railroads enabled development of the West, so the improved data infrastructure will create order of magnitude improvements in ability to access, share, and utilize data, from which order of magnitude business benefits will follow.* All too often these plans just build bridges to nowhere. Continue reading →
As you’ve read on this site and many others, the database world is well into a transition from a relational focus to a focus on non-relational tools. While the relational approach underpins most organizations’ data management cycles, I’d venture to say that all have a big chunk of big data, NoSQL, unstructured data, and more in their five-year plans, and that chunk is what’s getting most of the executive “mind share”, to use the vernacular.
Some are well along the way in their big data learning adventure, but others haven’t started yet. One thing about this IT revolution is that there’s no shortage of highly accessible training options. But several people have complained to me about the sheer quantity of options, not to mention the sheer number of new words the novice needs to learn in order to figure out what the heck big data is.
So here’s a very short list of training options accessible to the IT professional who is a rank big data beginner, starting with a very brief classification of the tools that I hope provides a some context. Continue reading →
His point is, since big data applications are often off the beaten IT path, big data professionals must solve “problems that companies don’t even know they have – as their insights highlight bottlenecks or inefficiencies in the production, marketing or delivery processes,” often with “data which does not fit comfortably into tables and charts, such as human speech and writing.” Continue reading →
At the very first TDWI Conference, Duane Hufford described a phenomenon he called “embedded data”, now more commonly called “overloaded data”, where two or more concepts are stuffed into a single data field (“Metadata Repositories,” TDWI Conference 1995). He described and portrayed in graphics three types of overloaded data. Almost 20 years later, overloaded data remains rampant but Mr Hufford’s ideas, presented below with updated examples, are unfortunately not widely discussed.
Overloaded data breeds in areas not exposed to sound data management techniques for one reason or the other. Big data acquisition typically loads data uncleansed, shifting the burden of unpacking overloaded fields to the receiver (pity the poor data scientist spending 70% of her time acquiring and cleaning data!)
One might refer to non-overloaded data as “atomic”. Beyond making data harder to use, overloaded data requires more code to manage than atomic data (see why in the sections below) so by extension it increases IT costs.
Here’s a field guide to three different types of overloaded data, associated risks, and how to avoid them: Continue reading →
Recently, I posted “Interview with a Data Scientist” at my company’s blog site. In it, my friend and colleague Yan Li answers four questions about being a data scientist and what it takes to become one. In my view Yan’s responses provide a bracing reminder that data science is something truly new, but that it rests on universal principles of application development. Continue reading →
Recently there was a great post at Dzone recounting how one “tech savvy startup” moved away from its NoSQL database management system to a relational one. The writer, Matt Butcher, plays out the reasons under these main points:
I hold a strong prejudice that IT paradigms are useful for about 30 years. The PC was dominant from 1980 to 2010, “online” mainframe systems from 1970 to 2000, and so on. If that’s the case then time’s up for Bill Inmon’s data warehousing framework. So far no widely held pattern has emerged to help us envision data management in today’s big data, mobile BI, end-user visualization, predictive analytics world, but at their recent Business Technology conference, Forrester Research took a swing at it by presenting their 2009 “hub and spoke” organizational strategy as a data management vision. Continue reading →
Recently I read a thoughtful post
at the PASS Business Analytics Conference site discussing how different the world is now for database professionals. Author Chris Webb focuses on the data science side in this post. His analysis made me think of the challenges and opportunities “big data” serves up to relational database designers.
To me these challenges are fundamental. Big Data and NoSQL bring lots of what we know about data elements, inherent data design, and data management into question. I think considering these elements closely leads to a sensible to-do list for relational database professionals. Continue reading →
One common theme in recent tectonic shifts in information technology is data management. Analyzing customer responses may require combing through unstructured emails and tweets. Timely analysis of web interactions may demand a big data solution. Deployment of data visualization tools to users may dictate redesign of warehouses and marts. The data architect is a key player in harnessing and capitalizing on new data technologies. Continue reading →