Interest in "data" is at an all-time high. The popularity of search terms like "big data," "Hadoop" and the "Internet of Things" spiked dramatically in the past year. The fact is, organizations are more interested in the potential of big data platforms and data management solutions than ever before. That’s
Lately, there's been lots of buzz around the logical data warehouse (LDW). In fact, Gartner is hearing LDW mentions as part of data warehouse (DW) inquiries almost 20% of the time and considers it a "megatrend." The definition usually includes some use of data virtualization or data federation capabilities to complement
One of the benefits of running an online data quality and data governance community is that over the course of many interviews, you start to see common threads and patterns emerging in the way practitioners create success in their data-driven programs. Data governance is a relatively new discipline, so it’s
In the past few weeks I have presented training sessions on data governance, master data management, data quality and analytics at three different venues. At each one of these events, during one of the breaks a variety of people in my course noted that the technical concepts of implementing programs
In my previous post, I outlined the main components needed for a phased approach to MDM. Now, let's talk about some of the other issues around approaching MDM: data governance and the move to enterprise MDM. Where does governance come in? Throughout your MDM program, it's important that deep expertise
In my last post I introduced the term “behavior architecture,” and this time I would like to explore what that concept means. One approach is to start with the basics: given a business process with a set of decision points and a number of participants, the behavior architecture is the
For decades, data quality experts have been telling us poor quality is bad for our data, bad for our decisions, bad for our business and just plain all around bad, bad, bad – did I already mention it’s bad? So why does poor data quality continue to exist and persist?
Instituting an analytics program in which actionable insight is delivered to a business consumer will be successful if those consumers are aware of what they need to do to improve their processes and reap the benefits. As we have explored over the past few posts, success in the use of
The data quality and data governance community has a somewhat disconcerting habit to want to append the word “quality” to every phrase that has the word “data” in it. So it is no surprise that the growing use of the phrase “big data” has been duly followed by claims of
Each year, I'm excited to see the awards nominations for Data Steward of the Year come in. It's not just because we enjoy seeing the program grow each year (which is true, based on the number of nominations we receive). It's also because of the variety of the nominations –
Loraine Lawson recently used the Eight-Fold Path of Buddhism, in which practitioners are encouraged to pursue right views, intentions, speech, actions, livelihood, efforts, mindfulness and concentration, as inspiration for her blog post The Five-Fold Path for Ensuring Data = Information. The post offered five recommendations for ensuring that data is transformed into
Last time we explored consumption and usability as an alternative approach to data governance. In that framework, data stewards can measure the quality of the data and alert users about potential risks of using the results, but are prevented from changing the data. In this post we can look at
Since tomorrow is How-long-has-it-been-since-you-used-this-data-ween, it’s time to review your organization’s preparedness for preventing the zombie data-pocalypse. (Please Note: This should not be confused with your organization’s preparedness for preventing the zombie apocalypse, for which check out the resources provided by the Centers for Disease Control and Prevention by ever-so-carefully clicking on
The third part of my data governance primer series addresses data quality analysis. Don’t even start a data quality analysis until you have completed the first two steps of your root cause analysis: investigate and prioritize any potential causative factors, then start your metadata assessment. Otherwise, you may be misled
In my last post I discussed one of the important traits that I feel truly great data stewards possess – the ability to effect change. Today I want to talk about how you actually identify, train and nurture everyday workers into the role of data stewards. Most organisations don’t have
In the era of big data, Kenneth Cukier and Viktor Mayer-Schonberger noted in their book Big Data: A Revolution That Will Transform How We Live, Work, and Think, “we are in the midst of a great infrastructure project that in some ways rivals those of the past, from the Roman aqueducts
With Data Stewards Day fast approaching I started to reminisce about the many data stewards I’ve had the pleasure of working with in the past. What struck me was just how many people take on the role of data steward - but under the guise of conventional roles. For example,
The second part of my data governance primer series addresses ways to "mind your metadata." I can just hear the collective groans, and perhaps a stifled yawn. Sorry, but metadata collection is one of those necessary evils that may not be fun, but having it available as a resource to
I've written, talked and thought about data management for an entire decade now. In that time, I've collected examples of how data – and the processes for managing that data – can affect our everyday life. For years I used the "Have you ever gotten multiple pieces of mail from
What's the Fifth Law of Data Quality? Jim Harris explains.
