Looker Blog : Data Matters

BI for the Under-40 Crowd.

Frank Bien, CEO

Jul 30, 2013

Why are so many young data analysts hand-coding in SQL?

BI is stale and a new approach is long overdue. Recent financial success and IPOs aside, BI’s innovation cycle simply hasn’t kept up with the rest of tech. Where are the tools for the new generation of tech-savvy data experts? Why has an innovation cycle been missed?

The reason is straightforward.

Several years ago, when new BI tools started to see success, Big Data emerged. Driven by Web 2.0 and 9/11, the need to capture vast amounts of machine-generated data became paramount.

The brightest teams in computer science created a new era in data management infrastructure—primarily around MapReduce and Hadoop. New tool vendors focused on Hadoop, primarily in the advanced analytics space. The newer, successful BI vendors retreated to departmental solutions. The core of BI, with its long-entrenched set of technology players, was left to go stale.

That’s not to say that the “new” BI players haven’t been innovating in their own ways. Keep in mind that when they were formed—Tableau 10 years ago, Qlik 20 years ago—technologies like smartphones, MPP databases, JavaScript, Hadoop, and social networks were just ideas… and .NET developers were using Dreamweaver on Windows XP. But the innovation was evolutionary, not revolutionary.

Somewhere along the way, BI got stuck in “group-think”: It needed to be brain-dead simple, analysts needed to be replaced with dashboards, data modeling was passé, and the user interface—above all else—was king. There wasn’t BI 2.0 like there was Web 2.0. Instead, we had the “dumbing down” of BI.

Meanwhile, the new generation of analytic databases was solidifying. From Greenplum to Vertica, HANA to Redshift (and just MySQL on cheap multi-core machines), processing capabilities around large datasets were growing at incredible speed. These systems used fresh techniques—such as column-oriented data storage, in-memory processing, and MPP scale-out—to bring new power to the analytic ecosystem. But, the tools to leverage these systems never emerged. Hand-coded SQL became the interface.

Enter Looker.

Looker is a new kind of data tool. It grew up out of the web—the world of Google and Facebook, Ruby and JavaScript, and the emerging class of modern data analysts and scientists. It grew out of database and languages experts, not the enterprise software BI crowd. And it grew up in a hybrid world of MySQL, MPP, Columnar, and Hadoop/Hive data management infrastructures.

Looker empowers data analysts to build web-based exploratory data applications—lightweight and agile—and through this, to delight their users with the ability to explore data, driven by selfish curiosity. Looker operates within the new era of databases, leverages the power of SQL to universally connect, and provides a modern and agile data modeling language that enables a new kind usability.

We’re not about dumbing down BI “so the CEO can use it”; it’s about creating such compelling value that any user can and will become immersed directly in the data. Let’s face it, the great tools aren’t always brain-dead simple. I’d argue Excel is one of the most complex tools ever let loose on the business world, but its value is in the value it creates for its users. The best tools do provide real business value. Value drives adoption, and vice versa.

In Looker, data scientists see code and an in-browser IDE. Analysts develop models collaboratively. Users leverage the effort of the data scientists and analysts, driven by their own curiosity and business needs. The results speak for themselves: Looker customers are among the most passionate of any tech I’ve seen. From business users in Marketing to PhD-level data scientists, Looker is providing value by being valuable—empowering folks to get information out of data. Looker is addressing the age-old problem of BI, but in a fresh, new (contrarian) way. Something the under 40 crowd would use (…and those who like to feel under 40, myself included).

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