In my previous post, I covered some recurring themes from our conversations with different user communities in the data ecosystem, which highlight common facets of data projects. But different user communities also have their own distinct inclinations when it comes to working with data. We’ve had the pleasure of talking with users from a range of communities and learning about their data projects.  Although there’s some overlap, generally, you could categorize these groups as: 1) Enterprise IT professionals, 2) Data journalists, and 3) Full-stack and back-end Web developers. Here are some insights we’ve...

In my previous post, I explored commonalities found in any given "data project." Here we'll run the first of the five gauntlets: data access. A few moons ago, we visited a customer to talk about some analytics he wanted to explore. Big company. Big ideas. Big IT infrastructure. The idea sounded great and we were happy to help 'em out. "All we need is the XYZ file," we said. Long pause. "Well, I can ask IT to pull an extract, but it will probably require an IT project. I'll call you next year." The phrase...

Like Doctor Dolittle's famed pushmi-pullyu, enterprise data projects have a serious agility problem. A recent story comes to mind. I was on a call with prospective customer to request data feeds for an analytics project.  We had performed a successful trial project, the business case was approved by management and everyone was happy. At last, we were ready to go into production. We simply needed data feeds for two well-known, standard tables from a commonly used enterprise system. We'd already worked with an extract and had everything set with the integration and analysis...

It's a story as old as time. It's a story of evolution, a story of freedom.  And it's a story of stuffing a genie back into its bottle. Yes, I speak of the ancient enterprise battle royale:  Productivity vs. Control. These battles are constantly happening throughout the enterprise, usually with very good intentions.  But, like so many good intentions, they often pave the road to, um...

Data projects come in many shapes and sizes. From big data predictive analytics to small data spreadsheet projects, from building new open data applications to reconciling a couple of ERP tables in the accounting department. There's one feature all these projects have in common: they all take far too long. Efficiency matters – and for maximizing the value of data, it matters a lot.  The math on this is simple. The more rapidly an organization can perform data projects, the greater its capacity to leverage data resources, generate real value and gain competitive advantage. According to...

In an excellent post, CIO Isaac Sacolick asks, what technologies work best for decentralized data scientists? It's a great question.  As he aptly describes it, working with data in a decentralized environment presents a challenging scenario: But what happens when these resources are scattered across multiple departments. One department may have an expert data scientist, another may have a small group doing internal reporting, and a third group might have outsourced its analytic function. If data scientists in the organization are decentralized with different goals, skills, and operating models, can IT still provide...