3 Takeaways from a New Looker Developer

In this blog post, read about this consultant’s experience with Looker, in their own words.

As a data management and analytics consultant, I have developed dashboards in a majority of the popular BI tools such as Tableau and Power BI, as well as their backend data structures. The opportunity to develop dashboards in Looker arose when a new client, Byrider, needed 2nd Watch to help them model their data and develop Looker dashboards for their sales team (more details here).

Based on my limited experience with Looker, I knew that it makes creating quality visuals simple and that coding in LookML is unavoidable. I worried that LookML would be extremely nuanced and I would lose time troubleshooting simple tasks. I could not have been more wrong on that front. Along with this realization, below are my top takeaways from my first Looker project.

Takeaway 1: LookML is easy to learn and ensures consistent metrics across reports.

Given the vast amount of documentation provided by Looker and the straightforward format of LookML code, I quickly caught on. This learning curve may be slightly different for report developers who have minimal experience with SQL. LookML adds transparency into what happens with data presented in visuals by directly showing how the code translates into the SQL queries that run against the source data. This makes it much easier to trust the results of dashboards and QA as you develop.

More importantly, LookML allows users to ensure their metric definitions are consistent across dashboards and reports. Establishing this single source of truth is key for the success of any reporting efforts. Within the semantic layer of the reporting tool, users can create SQL queries or harness LookML functions to develop custom measures and include descriptions to define them. Transforming the source data into predefined measures in the back end of the reporting tool ensures that report developers access the same metrics for every dashboard business users will see. This is a clear contrast from tools like Power BI and Tableau where the custom measures are created in each workbook and can vary. Furthermore, by using roles, administrators can limit who has access to change this code.

Takeaway 2: Creating dashboards and visuals is super intuitive for about 95% of use cases.

After setting up your data connections and LookML, developing a visual (“Look”) in Looker only requires a simple point and click process. Once you select the filters, measures, and dimensions to include in a visual, you can click through the visualization options to determine the best possible way to present the data. From there, you can easily adjust colors and stylistic options in settings using drop-down menus. Compared to other BI tools, these visuals are fairly standard across the board. That being said, Looker greatly stands out when it comes to table visualizations. It allows for conditional formatting similar to that in Excel and a wide range of visual options in comparison to other BI tools. This makes Looker a great selection for companies that often require tables to meet reporting requirements.

Although detailed documentation and the simple interface meet most reporting needs, there are limitations when it comes to easy customization in visuals. This includes the inability to set drill-ins by a visual rather than a field. In Looker, any demographic used across reports has to drill into the same fields (unlike those set per visual in a Tableau Tool Tip, for example). Additionally, you cannot format visuals based on customized metrics (e.g., color bands, conditional formatting for Field A based on the value of Field B, etc.). The caveat here is that you can unlock many customized visuals by writing custom code, a skill not always handy for report developers.

Looker Development Environment

Takeaway 3: Looker is extremely collaborative, something not often seen in BI tools.

With most BI tools, developers are forced to work independently because two people cannot easily contribute to a single workbook at the same time. Looker’s web-based format seems to have been built with collaborative development in mind, making this tool stand out when it comes to teamwork. Business users can also easily contribute because the web-based tool makes sharing dashboards and embedding them within websites easy. While this may seem minor to some, it significantly enhances productivity and yields a better result.

The following features ensure that your team can iterate on each other’s work, edit the same dashboards, and develop LookML without accidentally overwriting work or creating multiple versions of the same report:

  • Version control and deployment processes built into the “Development” window where users can modify and add LookML code
  • Ability to duplicate Looks developed by others and iterate on them, and Looks can then be added to dashboards
  • Shared folders where Looks and Dashboards used by multiple people can be stored and reused (if needed)
  • Ability to “Explore” a Look created by someone else to investigate underlying data
  • Ability to edit a dashboard at the same time others can make changes
  • Sharing dashboards using a link and the ease of embedding dashboards, which allows for seamless collaboration with business users as well

With a properly modeled data source, Looker impressed in terms of its performance and ability to provide highly drillable dashboards. This enabled us to dramatically reduce the number of reports needed to address the wide range of detail that business users within a department required. While the visuals were not as flashy as other BI tools, Looker’s highly customizable table visualizations, row-level security, and drill-in options were a perfect fit for Byrider’s use cases.

2nd Watch specializes in advising companies on how to gain the most business value possible from their analytics tools. We assist organizations with everything from selecting which tool best suits your needs to developing dashboards for various departments or structuring data to enable quick reporting results. Contact us if you need help determining if Looker is the tool you need or if want guidance on how to get started.


Private Equity Operational Dashboards for Uncovering Trends and Making Data-Driven Decisions – Part 2: Inventory Management Dashboards

In part one of this blog, we showcased private equity sales performance dashboards for a private equity-backed machinery dealership chain. In that piece, we outlined the insights the dashboards provided and the actions/decisions they impacted. In this blog, we’ll continue our look at operational dashboards, focusing on inventory management dashboards.

Inventory management dashboards tell you more than simply how much inventory you have on hand, what needs to be restocked, and what needs to be pushed. Inventory management dashboards help operations teams and leaders make decisions in areas like:

Production: What items are moving quickly? Where do we need to increase production? Where do we need to slow down production?

Ordering: What items are most popular? Which are the least? What is the seasonality? How long does it take to get an item from a supplier? What do we need to order and when?

Marketing: What do we order or produce based on customer demand? How do we forecast future demand? What items are sitting on the shelves the longest? How do we market these to sell?

Finance: What are the storage and operating costs associated with our inventory? Where can we reduce costs? What is the depreciation rate of inventory?

Planning: How does our current inventory storage plan align with our goals? Does it make sense to have a centralized warehouse or partner with a third-party logistics company? How can we model the potential costs? How could current supply chain issues affect future inventory?

This post will focus on two types of inventory dashboards: aged inventory dashboards and inventory logistics dashboards.

Aged Inventory Dashboards

Inventory management is a delicate balance between having enough inventory in stock to meet customer demand vs. not having items sitting on shelves, depreciating, and taking up valuable space. Operations teams, executives, and private equity owners need the ability to quickly see what stock they have, how long it’s been there, and the costs associated with storing it.

The dashboard below provides the ability to monitor stock and track its depreciation over time. The stock is also clustered within age groups to give a better idea to managers about when they have to replace their assets. This aged inventory dashboard also provides instant access to:

  • What inventory is at which location
  • How long inventory has been on the shelf
  • The original acquisition cost of the inventory
  • The current value of the inventory
  • The rate of depreciation
  • Trends by location and product type

Aged Inventory Dashboards

Inventory Logistics Dashboards

This inventory logistics dashboard goes a step further and provides more specific details on how the inventory is moved over time. In the case of this machinery dealership chain, they had several ways their products could be moved out of inventory, such as:

  • Direct sale to a customer
  • Rental to a customer
  • Loan to another location or partner
  • Use as a demo model

This dashboard analyzes how various products were moved in and out of inventory based on multiple traffic types, branch locations, and time periods. It provides managers and private equity owners with a logistics insight into how their stock touches every part of their business operations.

Inventory Logistics Dashboard

We hope you’ve enjoyed this series on operational dashboards for uncovering trends and making data-driven decisions. If you have any questions or want to see more dashboards, please contact us to connect with a private equity data analytics dashboard specialist.