When extolling the virtues of data quality, particularly to a leadership community, it pays to focus not just on the corporate gains but also the personal benefits that better quality data can offer. Improving data quality can often be a thankless task. You make changes to a resource that many
The planning and execution of enterprise information initiatives is definitely not easy. Building the business case involves identifying, documenting, verifying and refining a set of requirements that are representative of the various perspectives of the business and technical stakeholders all throughout the organization. Many such initiatives begin with the very
One of the key principles of W.Edwards Deming in his drive for greater quality in the US manufacturing industry was: “Cease dependence on inspection to achieve quality. Eliminate the need for massive inspection by building quality into the product in the first place.” Quality purists will repeatedly quote this principle
Data migration projects are really a lesson in gap management. We are effectively building a complex web of technology and understanding to bridge the gulf between the legacy world and the target world. Most of the gap management in data migration revolves around solving data quality and mapping issues. We
In my previous post, I discussed sampling error (i.e., when a randomly chosen sample doesn’t reflect the underlying population, aka margin of error) and sampling bias (i.e., when the sample isn’t randomly chosen at all), both of which big data advocates often claim can, and should, be overcome by using all the data. In this
A data quality culture is one of those elusive outcomes that can so often make or break your long-term data quality vision. Whilst many project leaders are capable of fostering collaboration and communication in their immediate team, some leaders find it hard to build on this to grow greater data
In his recent Financial Times article, Tim Harford explained the big data that interests many companies is what we might call found data – the digital exhaust from our web searches, our status updates on social networks, our credit card purchases and our mobile devices pinging the nearest cellular or WiFi network.
As an unabashed lover of data, I am thrilled to be living and working in our increasingly data-constructed world. One new type of data analysis eliciting strong emotional reactions these days is the sentiment analysis of the directly digitized feedback from customers provided via their online reviews, emails, voicemails, text messages and social networking
Data profiling is a core technique of data quality management and often the starting point for so many projects these days. Because it’s such a relatively simple technique to apply, it’s easy to overlook some of the more advanced techniques that can take your data profiling to the next level.
In his book Where Good Ideas Come From: The Natural History of Innovation, Steven Johnson explained that “error is not simply a phase you have to suffer through on the way to genius. Error often creates a path that leads you out of your comfortable assumptions. Being right keeps you in
