David Menninger's Analyst Perspectives

Looker Simplifies Business Intelligence in the Cloud

Posted by David Menninger on Mar 17, 2022 3:00:00 AM

Organizations face various challenges with analytics and business intelligence processes, including data curation and modeling across disparate sources and data warehouses, maintaining data quality and ensuring security and governance. Traditional processes are slow when transforming large and diverse datasets into something which is easily consumable in BI. And, it can take days or weeks to create reports and dashboards — maybe longer if processes change and new data sources are introduced. Our Analytics and Data Benchmark Research shows that the most time-consuming processes are preparing data, reviewing it for quality issues and preparing reports for presentation and distribution.

Looker is a business intelligence software that offers the capability to explore, analyze and share business analytics. It enables workers to connect and visualize data across various databases including Google Cloud, Azure, Amazon Web Services, on-premises databases and various cloud-based applications. The platform includes embedded analytics, workflow integrations and the ability to build and deploy custom data applications. Looker is entirely web-based and offers multi-cloud and hybrid capabilities. It supports over 50 SQL dialects. The Looker Marketplace is its central location for finding, deploying and managing various types of Looker content, such as Looker Blocks, applications, visualizations and plug-ins. It includes pre-built applications for several use cases such as web, marketing and sales analytics.

LookML is Looker’s markup language that describes dimensions, aggregates, calculations and data relationships in a SQL database. It provides predefined data types and syntax for data modeling. Data analysts can write SQL expressions once, in one place, and Looker will use the code repeatedly to generate ad hoc SQL queries. For business users, LookML offers the ability to build queries focusing on the content needed, without having to deal with complexities of the SQL structure. Looker offers more than 100 pre-built LookML modeling patterns to enable accelerated development.

Last year, Looker introduced Looker Mobile, which offers data access on both Android and iOS devices. Workers can access dashboards and filter information from within the application. Looker requires initial setup of a semantic model for storing business logic and metrics to build a single source of business truth. This simplifies the data for end users through a curated catalog and built-in transformation capabilities. Looker can also scan data and infer relationships between tables in schema to build a basic model, using the relationships already defined within the database to speed up the process.

Organizations are increasingly adopting self-service BI to enable workers across all departments to easily access required data, increasing opportunities to generate business value. But there are still some challenges with the democratization of data and analytics at scale. Our research lists a variety of concerns organizations have about analytics and BI. Common complaints include difficulty integrating data with business processes, data sources hard to access and systems not flexible or adaptable to change. Another big challenge is the time spent creating ad-hoc reports for decision-making, which also consumes too many resources. Using a software such as Looker can enable line-of-business workers to manipulate data, iterate and transform it in any way required, lowering the burden on IT.

Organizations with multiple data storage environments looking to make data more accessible throughout the organization should consider Looker. It can connect to various cloud databases including Redshift, Snowflake and BigQuery. Its Explore Layer enables workers to experiment with ad hoc queries or perform one-off calculations. Looker’s platform enables organizations to provide self-service analytics to a wide range of skilled workers, saving valuable IT time otherwise spent addressing requests from stakeholders.


David Menninger

Topics: Big Data, Analytics, Business Intelligence, Cloud

David Menninger

Written by David Menninger

David is responsible for the overall research direction of data, information and analytics technologies at Ventana Research covering major areas including Analytics, Big Data, Business Intelligence and Information Management along with the additional specific research categories including Information Applications, IT Performance Management, Location Intelligence, Operational Intelligence and IoT, and Data Science. David is also responsible for examining the role of cloud computing, collaboration and mobile technologies as they affect these areas. David brings to Ventana Research over twenty-five years of experience, through which he has marketed and brought to market some of the leading edge technologies for helping organizations analyze data to support a range of action-taking and decision-making processes. Prior to joining Ventana Research, David was the Head of Business Development & Strategy at Pivotal a division of EMC, VP of Marketing and Product Management at Vertica Systems, VP of Marketing and Product Management at Oracle, Applix, InforSense and IRI Software. David earned his MS in Business from Bentley University and a BS in Economics from University of Pennsylvania.