What happens when everyone can connect the dots

Everyone loves data, so it's no surprise that we've been innovating by orders of magnitude in data storage. But has analytics innovation kept up?

By Timothy McGovern
December 3, 2015
The BC Parliament building at night. The BC Parliament building at night. (source: By Alan Levine on Flickr)

In a webcast last week, Carl Anderson, director of data science at Warby Parker (and author of Creating a Data-Driven Organization), and Jen Grant, CMO at Looker, discussed the technical and human requirements for a successful deployment of organization-wide analytics, and some strategies for making your analytics strong and your organization data driven. This doesn’t come naturally with big-data architecture: native formats and varieties of pipelines tend to feed data into silos, frustrating users who want data to power their jobs.

Grafting analytics solutions onto a variety of data pipelines often results in solutions that are complicated and inaccessible: data teams are bogged down doing repetitive analysis tasks for the rest of the organization, when they would rather be conducting strategically important data science. A technical solution to unite these streams is key: Looker, for example, provides not only a canonical data pipeline and analytics dashboards, but also enables drill-down into its data sources and sharing and saving of new analysis.

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Putting this analytical horsepower to use requires working the technical solutions into human organizations. In the webcast presentation, Anderson and Grant introduced strategies that were implemented — and the rewards that came from them — at organizations ranging from DonorsChoose and Sprig (a gourmet food-delivery service) to Warby Parker.

If there is one key to successful innovation in analytics, it is ensuring that data is used across all levels of the organization. This sounds like a goal as much as a method, but there are solutions! As Anderson pointed out, the single most data-driven person in any organization is likely the CFO, monitoring and planning asset flows, values, and stores. As there are already teams gathering and provisioning this information, Anderson offered a few nuggets of advice:

  • Start with finance and work outward.
  • Build a data dictionary and iterate on it to define terms based on your business logic and values.
  • Encourage knowledge sharing within the organization, so individuals with skills can help colleagues level up.

In every case, the theme is clear: broadening access to data and the skills to make use of it fosters an objective, inquisitive culture.

When chefs are making use of data to invent new dishes — as happened in the case of Sprig — then you see a data-driven approach at work. The payoff from a data culture is clear: not just profitability, but agility and innovation.

To learn more, view the recorded webcast, “Essential Elements of a Data-Driven Culture.”

This post is a collaboration between O’Reilly Media and Looker. View our statement of editorial independence.

Post topics: Big Data Tools and Pipelines