It’s been said that the lines between work and personal life are wearing thin or even non-existent. We’re always “on” – and that means we can be even more productive.
It’s what Steven Sinofsky (@stevesi) calls “continuous productivity.”
His long-form piece on this topic is a fascinating read and inspired us to take a look at this from the perspective of big data analytics.
First, let’s define continuous productivity.
“Continuous productivity manifests itself as an environment where the tools and culture make it possible to innovate more and faster than ever, with significantly improved execution,” says Sinofsky, the former President of the Windows Division at Microsoft.
He says the shift from “episodic work” or “start/stop” to work that is “continuous” happens because of technologies that allow us to:
- Access the knowledge and experts of an entire industry in a few clicks
- Collaborate without boundaries of bricks and mortar
- Equally access data, knowledge, analysis and opinion of a team
- Access information and deal with situations without the boundaries of time at work
One of the advantages of this always-on productivity is that we can be more productive instead of just waiting on information or people. But Sinofsky says that this is not exactly the reality our business organizations experience.
“The vast majority of organizations are facing challenges or even struggling right now with how the changes in technology landscape will impact their efforts,” he notes.
If we apply this advantage and the reality to data analytics, Sinofsky couldn’t be more correct. The data is there to analyze. The technology is capable, but the organizations are still struggling with getting the data to the people. Silos, communication breakdowns and a number of other factors are to blame.
However, it’s a problem that can be overcome with improving access to data.
Some recommendations based on Sinofsky’s review:
- Get the data to the people. “Every member of an organization should have access to the raw ‘feeds’ that could be material to their role,” he says.
- Empower teams through collaboration. Data powers organizations today. Give teams the tools to “create, analyze, synthesize and share information.” Self-service data analytics feed this need.
- Focus on the outcomes instead of focusing on “review.” A topic we discuss frequently on the Spotfire blog is being armed with questions for the data. When your management style shifts from “specific decision making” or “reviewing work” to “framing the outcome, the characteristics of decisions and the culture of the organization,” productivity and morale improve. Things get done.
- Recognize that the tools are changing – fast. Tablets and smartphones are here – in workplaces everywhere. In fact, many tech developers think “mobile first” before heading back to the PCs in the office. It’s time to embrace these devices as more employees are moving to them.
So, what applies directly to data analytics? Here are a few recommendations from Sinofsky and our take on them:
- Keep sharing that data. Continuous data sharing allows us to “know how products are used, understand the impact of small changes and to try out alternative approaches.” There’s no better place for this mindset than in data analytics.
- Keep data real-time. The state of “right now” business means that we need access to data in its truest state. We should be able to answer the question: “What is happening now?” And take it a step further: “What do we expect to happen in the days to come?” Live data also contributes to collaboration rather than a discussion on the “validity or timeframe of those numbers.”
- Self-service is a key factor. Sinofsky focuses on usage metrics – movies watched, bank accounts used, products browsed, etc. We know that there’s power in self-service analytics. When your teams are using the data, insights and innovations happen.
Next Steps:
- Subscribe to our blog to stay up to date on the latest insights and trends in big data analytics.
Amanda Brandon
Spotfire Blogging Team
