We live in an era in which it's not terribly difficult for companies to ape many of their competitors' products and services, especially digital ones. For relatively small amounts of money (compared to years past), an organization can more or less mimic another's raison d'être and even specific functionality. As for design,
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 general paradigm for a master data management solution incorporates three operational components: An identity resolution engine. A master index. A master entity data repository. Conceptually, the identity resolution engine satisfies two core capabilities: the creation and management of unique identifiers associated with uniquely identified entities, and a matching capability
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
We have entered the era of big data, but many questions remain unanswered. For instance, who owns all of this information, anyway? If you take a photo and post it on Facebook or Twitter, does it still belong to you? If you create a presentation with Google Docs, does Google
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.
If you have been following this thread for a while, you may notice a theme that I keep bringing up: data virtualization. I'm trying to rectify a potential gap in the integration plan involving understanding the performance requirements for data access (especially when the application and database services are expected
Business intelligence is now accessible to companies of all sizes and sectors. I run a small business and we have incredible reporting and analytical capabilities that allow us to model every aspect of our operation. We can create dashboards of website traffic, article distribution, revenue performance, social media analytics and
"Most” organizations are embracing big data. For instance, a 2013 Gartner survey found that 64 percent of enterprises were deploying or planning big data projects, up from 58% the year before. Those numbers simply don't fit with what I’m seeing, and I suspect that I'm hardly alone. (By way of
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
