On October 19th, Blue Hill had the pleasure of attending Join 2016, Looker’s first multiple-day conference, focused on Looker’s strategy, technical roadmap, peer networking, analytical best practices, top case studies, and thought leadership.
Looker is a company that Blue Hill has tracked since our inception in 2013. Looker’s theses for product development were presented by Colin Zima, Looker VP of Product and Abby West, Looker Product Manager, as follows.
As a result, Looker’s approach to data exploration and commitment to integrating analytics into all aspects of corporate data operations meshes with our current view of the key requirements for business analytics in the enterprise:
– Scale, high-performance, data discovery, and visualization are table stakes.
– Must be capable to support all employees and customers.
– Accommodates data growth: Must avoid excessive follow-on investments, long-term customization, or staffing to support increases in analytic usage or adoption.
– Removes process bottlenecks to analytic guidance.
– Supports analytics for any accessible internal or external data.
– Provides more context and insight as data environments become more complex.
– Can make just-in-time adjustments when business demands it.
– Offers a user interface and experience that matches both consumer-grade ease of use and appropriate business context for each analytic role.
Although no platform does all of this perfectly, Blue Hill believes that Looker’s initial focus on ELT (Extract Load Transfer), the LookML markup language, and rapid implementation and integration with business processes and data sources are all important traits in empowering the current expectations to which business analytics should be held. In particular, Blue Hill notes the following presentations and announcements that caught our attention both at the Join 2016 event and with the launch of Looker 4.
Blue Hill Highlights from Join 2016
At Join 2016, Blue Hill focused on the end user stories, product updates, and some of the workshops used to introduce new and interesting capabilities as Looker entertained and educated over 300 data analysts at the Hudson Mercantile in New York City. Behind the curtain, here are some of the backstage topics that got our attention.
First, who doesn’t love a good web quiz? The mother of all quiz sites is Buzzfeed, a media company that has successfully shifted and expanded its media offerings over the past several years. Buzzfeed data scientist (and English major) Lyle Smith provided fascinating insights on how to integrate quantitative metrics and success into the world of creative writers and artists. One of the most interesting aspects she shared was the concept of bucketing success metrics in categories and having a portfolio of content based on the expected outcomes. For instance, a successful quiz may lead to other writers’ desires to create 10 copycat quizzes, but the portfolio mix may only ask for a certain level of specific quiz volume compared to articles, videos, and other content. By bringing the data analyst and editor together, Buzzfeed makes smarter decisions on how they publish content.
Join 2016 also focused on the product as well. I enjoyed seeing the “how-to” workshop on bringing Looker and Slack together on the rooftop of the Hudson Mercantile.
This capability presents an opportunity for any analytic change to now become an alert. This is especially useful for potential business signals that may be based on multiple departmental data changes such as sales and service or marketing and manufacturing. By using Slack as a centralized real-time collaboration channel, this functionality can help provide companies with real-time alerts that have traditionally required dedicated applications. The combination of ease of implementation and the potential for real-time analytic collaboration made this an interesting capability.
More broadly, Looker also is making a more dedicated effort to show the best practices of using LookML, its analytic markup language, to provide a better starting point for new users. LookML is YAML-based and similar to SQL, which makes the syntax and language fairly transparent for a trained data analyst or database administrator.
However, the openness of the capabilities in LookML can also provide analysts with the ability to potentially create very inefficient methods of extracting analytic outputs. Blue Hill believes this education effort is core to Looker’s ability to empower data departments with one of Looker’s core strategic advantages: quickly allowing data analysts to define queries and joins independently from the data. For non-data people, this approach allows data-driven business rules to be consistent across a wide variety of data (such as all of your product catalogs or all of your financial sources or all of your sales data sources) based on a “write once, use many” logic that is typically missing in the analytics world.
Key Highlights of Looker 4
– An improved RESTful API to pull Looker outputs into web services.
– An in-browser IDE (Integrated Development Environment) to support its LookML markup language.
– Content Delivery to both search and recommend analytic reports, outputs, and presentations that are interesting.
– More personalized analytic delivery and visualization.
– Expansion of Looker Blocks, business templates built both by Looker and Looker partners to support business tasks such as Marketing Affinity, Amazon Redshift administration, or Sales Cohorts.
In conjunction with the v4 launch, Looker has also introduced a data developer certification program. Looker is building a nascent community of LookML-certified developers, but expect to see new Looker block and Looker app contributions from that community grow.
The net-net of this is in empowering data analysts to implement new business logic more quickly, getting analytics out to a broad audience more quickly through the API, and empowering data users and browsers to provide intelligent and qualitative ways to display and prioritize analytics that matter in the context of daily work.
Blue Hill believes that Looker’s differentiation is still emerging in the broader BI and analytics marketplace at large. Ironically, the unique nature of Looker’s differentiation may be a challenge, as Looker’s approach to abstracting data and analytics has not been copied by the market-at-large despite Looker doubling its client base to over 750 companies and nearly tripling its revenue year-over-year. As a result, Looker is a well-kept secret for companies seeking a way to quickly structure, adjust, and deliver analytics at scale. Blue Hill expects that over the next year, Looker should be well-positioned to continue growing as enterprise BI, analytics, and DataOps markets shift towards active acquisition for products that represent the current state of analytics.