Navigating the current BI and analytics landscape is often an overwhelming exercise. With buzzwords galore and price points all over the map, finding the right tool for your organization is a common challenge for CIOs and decision makers. Given the pressure to become a data-driven company, the way business users analyze and interact with their data will have lasting effects throughout the organization.
Looker, a recent addition to the Gartner Magic Quadrant, has a pricing model that differs from the per-user or per-server approach. Looker does not advertise their pricing model; instead, they provide a “custom-tailored” model based on a number of factors, including total users, types of users (viewer vs. editor), database connections, and scale of deployment.
Those who have been through the first enterprise BI wave (with tools such as Business Objects and Cognos) will be familiar with this approach, but others who have become accustomed to the SaaS software pricing model of ‘per user per month’ may see an estimate higher than expected – especially when comparing to Power BI at $10 /user per month. In this article, we will walk you through the reasons why Looker’s pricing is competitive in the market, and what it offers that others do not.
Semantic and Governance Model
Unlike some of its competitors, Looker is not solely a reporting and dashboarding tool – it also acts as a data catalog across the enterprise. Looker requires users to think about their data and how they want their data defined across the enterprise.
Before you can start developing dashboards and visualizations, your organization must first define a semantic model (an abstraction of the database layer into business-friendly terms) using Looker’s native LookML scripting, which will then translate the business definitions into SQL. Centralizing the definitions of business metrics and models guarantees a single source of truth across departments. This will avoid a scenario where the finance department defines a metric differently than the sales or marketing teams – all while using the same underlying data. A common business model also eliminates the need for users to understand the relationships of tables and columns in the database, allowing for true self-service capabilities.
While it requires more upfront work, you will save yourself future headaches of debating why two different reports have different values, or needing to define the same business definitions in every dashboard you create. By putting data governance front and center, your data team will be able to make it easy for business users to create insightful dashboards in a few simple clicks.
Customization and Extensibility
At some point in the lifecycle of your analytics environment, there’s a high likelihood you will need to make some tweaks. Looker, for example, allows you to view and modify the SQL that is generated behind each visualization. While this may sound like a simple feature, a common pain point across analytics teams is in trying to validate and tie out aggregations between a dashboard and the underlying database. Access to the underlying SQL not only lets analysts quickly debug a problem, but also allows developers to tweak the auto-generated SQL to improve performance and deliver a better experience.
Another common complaint from users is the speed for IT to integrate data into the data warehouse. In the ‘old world’ of Cognos and Business Objects, if your calculations were not defined in the framework model or universe, you would be unable to proceed without IT intervention. In the ‘new world’ of Tableau, the dashboard and visualization are prioritized over the model. Looker brings the two approaches together with derived tables.
If your data warehouse does not directly support a question you need to immediately answer, you can use Looker’s derived tables feature to create your own derived calculations. Derived tables allow you to create new tables that don’t already exist in your database. While it is not recommended to rely on derived tables for long-term analysis, it allows Looker users to immediately get speed-to-insight in parallel with the data development team incorporating it into the enterprise data integration plan.
Looker takes collaboration to a new level as every analyst gets their own sandbox. While this might sound like a recipe for disaster with “too many cooks in the kitchen”, Looker’s centrally-defined, version-controlled business logic lives in the software for everyone to use, ensuring consistency across departments. Dashboards can easily be shared with colleagues by simply sending a URL, or exporting directly to Google Drive, Dropbox and S3. You can also send reports as PDFs, and even schedule email delivery of dashboards, visualizations, or their underlying raw data in a flat file.
Looker enables collaboration outside of your internal team. Suppliers, partners and customers can get value out of your data thanks to the modern approach to embedded analytics. Looker makes it easy to embed dashboards, visuals and interactive analytics to any webpage or portal because it works with your own data warehouse. You don’t have to create a new pipeline or pay for the cost of storing duplicate data in order to take advantage of embedded analytics.
So, is Looker worth the price?
Looker puts data governance front and center, which in itself is a decision your organization needs to make (govern first vs. build first). The addition of a centralized way to govern and manage your models is something that is often included as an additional cost in other tools, increasing the total investment when looking at competitors. If data governance and a centralized source of the truth is a critical feature of your analytics deployment, then the ability to manage this and avoid headaches of multiple versions of the truth make Looker worth the cost.
If you’re interested in learning more or would like to see Looker in action, Aptitive has a full team of data consultants with experience and certifications in a number of BI platforms as well as a thorough understanding of how these tools can fit your unique needs. Contact us to learn more.
Tayva is a data & technology consultant at Aptitive. Through her background in information technology and retail analytics she helps transform organizations through the use of data-driven strategies and innovative solutions.