DATA ANALYTICS

Why Looker Leaves Teams Lagging and How Sigma Empowers Ad Hoc Data Exploration

Julian Alvarado

Sr. Content Marketing Manager, Sigma

Because Looker has been around since 2012 and is now part of the Google Cloud Platform, it’s one of the most well-known data analytics solutions — and its extensive visualization template library is impressive.

But while Looker looks good, it often leaves business teams with more questions than answers, prompting them to rely on spreadsheets and data extracts that pose a security risk. Let’s explore these limitations and see how Sigma enables teams to easily harness the full power and potential of their data, without any data ever leaving the warehouse.

Why Ad Hoc Data Exploration is Foundational to Data-Driven Decision Making

Guesswork doesn’t result in good decisions. Today’s companies know that they must be data-driven to compete, and decision-makers need the ability to get answers from their data quickly. One reason that making data-driven decisions is so challenging is that ad hoc analysis is bottlenecked. Too many organizations are dependent on their data teams for next-level insights beyond static dashboards.

Because business teams must operate with speed to act on opportunities and identify or address problems, they can’t wait hours or days for the data team to deliver vital information. Ad hoc analysis allows decision-makers to get the insights they need for decision-making when they need them. With an ad hoc analytics tool, decision-makers can explore data on their own, rather than having to wait on scheduled reports or additional analysis from the BI team. For a company to be data-driven, its business teams must be able to conduct ad hoc analyses on their own to find specific answers to specific questions.

But What About Data Chaos?

Many organizations are concerned about user access to raw data. With Sigma, admins can control exactly who has access to the connection’s data at each level of the connection tree. Organizations can grant users full access to a connection or individual schemas, or limit their access to only certain warehouse tables.

Sigma vs. Looker: 5 Ways Sigma Enables Ad Hoc Decision Making

  No need to learn proprietary code

Looker requires users to learn LookML, a proprietary coding language. LookML then generates SQL “under the hood” to query the data warehouse. Data experts skilled in SQL, the most widely-used language for interacting with databases, can’t use SQL directly. As a result, Looker customers must force all new and existing data and BI team members to learn LookML, or hire from the extremely small pool of analysts with LookML experience.

In contrast, Sigma allows users to interact with a spreadsheet-based interface and use spreadsheet-like formulas and functions. Sigma also generates SQL “under the hood,” but it allows data experts to work directly in SQL if they prefer. Sigma is easy for both non-technical and technical users to operate, which translates in faster time to value. Sigma also saves critical time and resources by making it faster and easier to recruit qualified analysts.

  No data modeling necessary

While Looker promises to enable non-technical business teams to independently analyze data and get the answers they need without having to ask the BI team for help (as long as they learn LookML), there’s a catch. Data must be extensively modeled in LookML before it can be used by domain experts. For this reason, data analysts must anticipate and clearly define each metric the business needs in advance, and business users must know the name of the metric they want to use and request for the BI team to model it for them if it’s missing.

Sigma offers more flexible data modeling functionality. Users can query warehouse tables directly with no modeling required, join tables on the fly, and perform expression-based joins (for example, joining a date to a timestamp.) They can also visually curate datasets, define spreadsheet-like calculations, and link sources to give others an endorsed path for limitless exploration. Models are fast and easy to update as data sources, types, and processes evolve, and they can also be written back to the cloud data warehouse and leveraged across other platforms.

  Provides access to underlying data for further exploration

The most impactful and timely insights are often the ones you least expect. For this reason, limitless, on-demand data exploration is a must-have for any data-driven organization. For example, the data may be modeled in Looker for a marketer to analyze the average cost per lead by month. But if the marketer notices a spike in a particular month and wants to dig into the purchase behaviors of the most expensive cohort, they will have to go back to the BI team to model this data for them.

This requirement creates delays and ultimately defeats the purpose of a self-service approach. Additionally, it often results in business teams turning to siloed, ungoverned, and outdated data extracts so they can move faster. Working with data extracts creates security risks, as users typically use Excel or Google Sheets.

However, Sigma makes it possible for anyone to explore real-time data in the cloud data warehouse at row-level detail by giving them the full power of SQL in a spreadsheet-like UI. Sigma users are always working with high-quality, up-to-date, fully governed data. And with Sigma, data never leaves the cloud data warehouse, so there are never any extracts created.

  No technical skills required

Most marketers, sales reps, and finance professionals are not data experts. And data teams are not experts in marketing, sales, finance, etc. However, Looker requires data analysts to clearly define metrics and model data using LookML before business teams can get involved, assuming that data experts are familiar with Salesforce field definitions, marketing campaign measurements, financial reporting criteria, and much more. The result is a lot of trial, error, and frustration as BI teams struggle to give line-of-business teams what they need.

The best data models and analyses allow data and line-of-business colleagues to efficiently work together and combine their expertise in real time. Because Sigma doesn’t require technical skills to explore raw data, join sources, or create definitions and calculations, business teams have much more flexibility.

  Simple to implement

Thanks to the fact that all users must learn Looker’s proprietary language and data teams must model all data using LookML prior to use, Looker’s solution can take months to fully deploy. Additionally, Looker customers must either pay extra to have their instance hosted by Looker, or invest upfront money and time, as well as ongoing maintenance resources to host it themselves.

On the other hand, Sigma is a SaaS solution that connects directly to your cloud data warehouse so you can start analyzing data in minutes, not days or weeks. Sigma has no maintenance costs associated with it. And its spreadsheet-like UI and visual data modeling capabilities empower business teams to instantly uncover their own insights, freeing data experts to focus on higher-value initiatives — at a fraction of the cost of Looker.

Sigma’s Ad Hoc Analysis Capabilities in Action

Let’s take a look at two examples of how companies are using Sigma to empower their teams to quickly and easily explore data for game-changing insights and become more competitive.

How Payload uses Sigma to offer analytics to customers

Payload is an easy-to-use cloud application for logistics and supply chain management, delivering simple, accountable logistics tracking and reporting. To improve retention, generate new business, and ultimately enable customers to get the full value of their application, the Payload team realized they needed to build analytics solutions into their product. No one else in the industry was providing this type of insight to customers, so it would help make the company more competitive.

Looker required time-consuming processes

At the time, Payload was using Looker to analyze and visualize its data and using CSV exports to share reports with customers. Reports had to be created ad hoc and required significant development effort to build, deploy, and release. Because Looker requires users to learn its proprietary coding language, LookML, the number of people who could generate and manage these reports without exporting them was limited. Additionally, even a small schema change in the company’s cloud data warehouse meant hours of manual updates in Looker.

Sigma speeds the analytics product launch and delivers insights faster

After evaluating multiple BI tools, the team chose Sigma thanks to its spreadsheet-like user interface, which gives everyone the power of SQL without having to manually write code. Empowering business teams to independently explore and analyze data drastically reduces time to insight by eliminating reporting request queues and time-consuming back and forth.

Sigma doesn’t require any updates to the central data model to access or analyze data. It sits directly on top of the cloud data warehouse, so any changes in the warehouse are immediately reflected across reports. What’s more, BI and business teams can work together in Sigma to build contextual, reusable datasets and conduct complex analyses.

Sigma eliminates risky data extracts and keeps data safe inside the cloud data warehouse, providing confidence that data is always secure and compliant. This confidence made it possible for Payload to share its data externally with customers using Sigma’s Embedded Analytics functionality.

Using Sigma, Payload has launched two new data products that provide its customers with actionable insight into load utilization, field ticket visualization of events, vehicle speeds and safety, carbon footprint, and more. The Payload team reports that adding Sigma dashboards and insights to the Payload application raised the perceived value of its product, helping them to retain current customers and expand the accounts. Additionally, Sigma’s solution allowed Payload to create an entirely new revenue stream, which now plays a key role in generating and closing new business.

How Teachable uses Sigma to slash ad hoc requests 70% and increase analytics adoption

Teachable is an all-in-one platform that helps people create and sell courses online. More than 100,000 creators use Teachable to share their knowledge because they make it easy to create an online course or coaching business by handling everything from web hosting to payment processing.

Time to insight lagged with Looker

Teachable was committed to being data-driven, but their current BI tool, Looker, required knowledge of LookML, which prevented most of the company from running their own reports. Even straightforward, predictable report requests had to go through the data team. As a result, two full-time employees were spending the majority of their time answering ad-hoc requests, time to insight took weeks, and adoption of the tool was low.

Sigma delivers insight in seconds

The team at Teachable chose Sigma because of its ease of use, especially among non-technical users. The Teachable team says Sigma was especially attractive because of its ease of use for “non-data” people, as well as the fact that end users can add and connect to datasets on their own. Any user in the company can connect to and join any table they have access to in the data warehouse, without the need for the data team to set anything up.

Almost immediately, teams across the organization were able to build their own dashboards and directly ask questions of their data using Sigma’s spreadsheet-like UI. The most obvious benefit to adoption of Sigma has been the reduction in ad-hoc requests, which has freed the data team up to do more impactful work and unlock greater value for the company. Teachable has reduced the workload from two full-time employees doing virtually nothing but answering ad-hoc data requests to half of one full-time employee’s time spent on ad-hoc requests. Business users are able to make their own data pulls through Sigma. As a result, the role of data analysts at Teachable has been transformed from running rote SQL jobs to complex data modeling and analysis and other work that’s valuable to the company.

Sigma Delivers Faster Insights for Better Decision-Making

Decision-makers need the ability to get answers from their data quickly. To do that they must be able to conduct their own ad hoc analyses. Business intelligence solutions that require teams to learn a proprietary language and demand extensive data modeling prior to analysis slow down decision-making. Today’s teams must be empowered to explore their data efficiently to find the answers they need in a timely manner, and Sigma truly transforms a teams’ ability to work with data, delivering faster insights.

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