Your data-driven journey with Looker

The Looker suite is a set of data analytics tools within Google Cloud Platform. It helps you at any point of your journey to become more data-driven. It doesn’t matter if you are just beginning or an advanced user of data. So what are the steps you can take to become a data-driven organisation? This article aims to help you decide where your own business lies in its data maturity. Secondly it helps you identify which Looker product may be the best match for you.

1. Milestones towards becoming a data-driven company

Becoming data-driven requires a good data strategy and data foundations. We see 7 milestones each company should pass to becoming data-driven. We’ll show you which product can help you, depending on the stage you are in.

1. Start generating and collecting data and set up data foundations with Looker Studio

A data-driven company starts collecting data, implementing tools and services. How? By setting up data foundations. Start preparing the data, build a data operating model, set a data governance etc. and start to use the data to make decisions.

Looker Studio is the product that can help at this stage.

2. Using data for decisions and good explanations with Looker Studio

Milestone one is done by other methods than simply the gut or instincts of people within the company, and to seek out good explanations on why business critical events happened.  

These explanations can help influence future decisions, and is where human beings can be creative.  Once you start relying on these decisions and explanations, you may experience pushback or doubt on those conclusions.  

Looker Studio is the product that can again help here.

3. Creating trusted data sources with Looker Studio Pro

How can you demonstrate trust in your data? It may require you going back and modifying your data collection, switching vendors or showing your data lineage to back up the data’s claims. Should adoption be gained, as more people use the data, then milestone four is around the corner.

Looker Studio Pro is the product that is adequate to help with this milestone.

4. Data Governance access and consolidation with Looker Studio Pro and Looker Enterprise 

This milestone is all about user governance and data quality issues that will start to become more important.  Who and what they are doing to the data will be more business critical.  

Once you have a proper data governance structure in place, then you can look to take some of the insights and explanations from your dashboards and use them in more scenarios than dashboards. 

Looker Studio Pro and Looker Enterprise can help with this milestone.

5. Data Activation channels beyond dashboards with Looker Enterprise and Looker Studio Pro

Not needing to rely on a person to react but to have automatic APIs or data workflows setup and triggered by set conditions.  As you turn your data into information and wisdom, more advanced mechanisms to extract information from your raw data may be needed. That’s where the next milestone comes in.

Looker Studio Pro and Looker Enterprise can help with the data activation beyond dashboards.

6. Introduce more sophisticated data modelling with Looker Enterprise

To extract information from your raw data, ML and statistics may be required prompting using data science platforms. Using these models in an effective manner will require the whole organisation to embrace the data. 

Looker Enterprise is the product that can help with the more sophisticated data modelling.

7. Experimentation and a data culture

A data culture of experimentation and using data as a 1st class citizen may need to be infused across the whole company, requiring change management and training throughout the organisation.

So how can the Looker products help you solve your challenges?

2. Introduction to the Looker suite

Looker now encompasses a lot more than the cutting edge BI company Google bought in 2020.  Google has had its own data visualisation tool called Data Studio that started within the Google Marketing Suite, for visualisations of Google Analytics and other digital marketing data.  It quickly expanded to include data sources such as BigQuery, Google Sheets and other non-Google sources, enabled by its open protocol for creating new connectors and visualisations.  

For many years Data Studio was a popular choice for data visualisation tasks within an organisation; in particular for its ease of use, free price point, data connectivity and being able to share with others via a URL.  At Google Cloud Next 2022, Google announced that Data Studio was being rebranded to Looker Studio, alongside the introduction of a new paid service Looker Studio Pro.

In total then, there are three products to choose from the Looker suite: the original Looker (we shall dub Looker Enterprise in this article to avoid confusion); Looker Studio Pro and Looker Studio. 

Discover a comparison of these products below:

How Looker Suite products relate to one another

3. Looker Enterprise Helps Business Logic meet the Data

The next section of this article introduces you to Looker Enterprise – the third and probably the most advanced and most exciting product of the Looker Suite. We will look deeper into the semantic layer of Looker: the part where the raw data from a datasource meets the business logic. 

We will go through a simple example use-case of eCommerce website data, and see how this business logic can be modelled in LookML – Looker’s modelling language. 

Looker Enterprise: a platform for enabling data experiences

Looker can be called a BI solution. However, one would also like to deviate from this definition and call it rather a platform for enabling data experiences. Looker can be compared to other similar solutions, such as PowerBI, Alteryx and Tableau. 

However, it has its own advantages, which make it one of the best choices in some of the most common data analytics challenges one could have. 

The next section introduces exactly what is so special about Looker: its semantic layer together with a simple use-case of how the semantic layer could be represented, or, in Looker terms, modelled. 

Looker’s Semantic Layer & LookML

When talking about Looker, one special feature of it should take the first priority in discussions about why one should consider Looker. That is, its semantic layer. 

Semantic Layer is an abstraction that sits between a datasource and the presentation layer, which Looker also has of its own, however is not limited to. Semantic layer is built in LookML – Looker Modelling Language – and is specific to the business logic and business rules it should provide. 

In more technical terms, Looker’s Semantic Layer is a collection of git-versioned LookML files, where each component of your data – a table, a column and an aggregation of a column value – is abstracted into a collection, or a single file. 

Explores in Looker

Besides the tables, dimensions (columns) and measures (aggregations), the semantic layer also includes Looker-specific model definitions, data access rules, dashboard definitions, and last, but not least, explores. An explore is another Looker concept, which one should be familiar with, if one is to build data analytics solutions in Looker or one is to explore the data as an end user. 

An explore is an analytical representation of the relationships of your data, that is, an explore is built by joining your tables together in LookML code, where the introduced joins give a logical relationship of your data tables, which then can be explored – selected, recalculated, if needed, and then presented – for example, in Looker’s native dashboards and looks.  

Example of a semantic Layer

Let’s have a look at an example. The below figure shows how a semantic layer could look like for a sample eCommerce use-case:

In the above figure, we have 3 tables in our datasource – products, orders and users. The datasource could be a SQL-based database, such as MySQL, Snowflake or BigQuery; it can also be a Google Sheets file. Before the data is presented in the presentation layer, the business logic is modelled in the semantic layer: in this case, we define the 3 tables (called views in Looker) with their corresponding columns, which are represented as dimensions and measures. 

Then we define a couple of explores, which then are related to a number of models, that in this case may represent 2 departments – “finance” and “marketing”. Why would you have 2 separate models, represented by two different departments? The reason could be that different data should be accessed by different departments. We will not go into details regarding data access here, but it is also possible to model in the semantic layer.

What is also important to note in the above figure, is the relationship between the 3 layers – presentation layer, semantic layer and the data layer. From the semantic layer, Looker writes the most performant SQL to the datasource and retrieves the results back. And then, the semantic layer brings the results up to the presentation layer, where the looker dashboards, looks and explores are presented for the end user.     

How Looker differs from other BI Tools

The current landscape of available BI tools and platforms in the market is quite diverse, however, also quite fragmented. Each technology is almost ready to provide the BI tool best fit for their use – Microsoft offers PowerBI and SAP platform has a corresponding Business Objects solution. And then there are other solutions which cover data analytics needs, however with different technological flavours: such as Alteryx and Tableau. 

As mentioned before, Looker is strongest if one considers its semantic layer – and one should choose Looker, if that is where the weight of the decision lies. To add on top of it, the semantic layer is where the Looker roadmap is directed to. In the future, one will be able to develop the semantic layer of the business logic and business needs, and then use other solutions, such as PowerBI or Tableau, for their strong data visualisation capabilities. 

Additionally, in other tools you need to create worksheets and write scripts to load data, but Looker allows the users to directly query the database. Any user without SQL knowledge can therefore query the company’s data thanks to this semantic layer. 

Another advantage of Looker is that the tool is better at drilling down. In other tools, data engineers might use some aggregations so that data is not too large, but in Looker you can always drill down to see what’s inside the data.

Looker as a BI Solution – Lessons Learned

At Devoteam, we have implemented or helped implement a number of projects that were focused on Looker as a data analytics platform. The companies embracing Looker made the right choice – their use-cases were quite broad in terms of where in the company the analytics had to be consumed. 

They were also quite democratic technology-wise, and they were mature in their journey of becoming data-driven companies. So the fundamental need of data analytics for better business decision making in the company was already there and already being satisfied with quite well-established data analytics solutions. 

The only remaining necessary component was the data technology they needed to choose going forward, either from the very start with no existing starting-point, or from the aspect of improvement, where the data analytics in question were implemented in more rigid solutions, such as for example Metabase. 

Based on their use-cases, some themes appeared to be quite common for all of them – that is:

1. Being able to present data real-time without considerable time delays 
2. Being able to provide a sufficient data access model in the data platform without implementing PII, user and group access directly in their original datasource
3. Being able to provide a sufficient and easy to implement data consumption model, where self-service (which means, business users can explore their data to create their dashboards and reports) was a key element for the new data-platform success. 

Last but not least, moving from SQL-based queries in the data analytics platform (such as Metabase) to a more comprehensive semantic layer, made the overall reporting solution much easier for the data team to consume, maintain and expand.  

4. Get Better Insights Faster: How Looker Jumpstart Speeds Up Looker Deployments

The Added Value of Looker Jumpstart for Your Business

Investing in Looker Jumpstart provides a wealth of tangible benefits that contribute directly to your business success. You can do that with Devoteam, a premier Google Cloud partner with a proven track record in data and analytics solutions, Devoteam brings the deep expertise necessary to navigate your initial Looker implementation, provide architectural support, and empower your teams through targeted training. Our co-development approach ensures that you not only gain proficiency but also build the internal confidence to harness Looker for your ongoing analytics needs.

1. Drastically Reduced Time to Value

Traditional BI implementations can be lengthy, involving extensive planning, complex configurations, and significant time before users can start generating meaningful insights. 

Looker Jumpstart directly tackles this challenge by providing focused training, expert guidance, and architectural support right from the beginning. 

• The “Planning & Kickoff” services offered ensure a swift initiation of the project, defining key stakeholders, success criteria, and project governance to keep things on track. 
• The Looker Architecture Workshop provides an immediate introduction to the platform and helps gather technical requirements for quick architectural decisions. 

This accelerated approach means your teams can start exploring data and generating value much sooner, leading to a faster return on your investment in Looker.

2. Clearly Defined Objectives and a Strategic Roadmap 

Before starting any data analytics initiative, establish clear goals and a well-defined roadmap. Looker Jumpstart prioritises this crucial step. By working collaboratively to review project objectives, document key performance indicators (KPIs), and map data to initial use cases, the program ensures that your Looker implementation is strategically aligned with your overarching business goals. Our experts reinforce this by focusing on the roll-out plan and identifying key initial use cases.

3. Empowering Your Teams with Essential Looker Skills 

A successful Looker implementation hinges on having skilled users who can effectively leverage the platform. Looker Jumpstart provides comprehensive training offerings tailored to different user roles. 

• With a Looker Developer Training, we equip your technical teams with the foundational knowledge of LookML, enabling them to build and maintain your data models. Concepts such as Views, Dimensions, Measures, and Explores are explained in detail, along with the Looker development workflow. 
• Simultaneously, the “Business User Training” empowers your non-technical users to navigate the developed Explores, dashboards, and Looks, showing them how to extract the maximum value through features like scheduling, alerting, exporting, and activating data.

This dual focus on both technical and business users ensures widespread adoption and self-sufficiency.

4. Hands-On Experience Through Co-Development 

Theoretical knowledge alone is often insufficient. Looker Jumpstart incorporates Looker co-development sessions where Devoteam experts provide online support as your teams build out use cases in Looker.

These sessions offer opportunities to ask questions, address challenges on the spot and learn best practices. This hands-on approach has boosted confidence and accelerated the learning curve, ensuring your teams can independently develop solutions moving forward. 

5. Robust and Scalable Architectural Foundations 

A well-designed architecture is critical for the long-term success and scalability of your Looker environment. With the Looker Architecture Workshop, you get an introduction to the Looker platform and its architecture, helping to gather technical requirements and guide crucial architectural decisions.

You can even take on architectural advisory services with ongoing support from a Data Architect to assist your Data Engineers with complex or architectural topics. This ensures a solid and scalable foundation that can adapt to your evolving data needs.

6. Proactive Mitigation of Implementation Risks 

Implementing a new analytics platform can present various challenges. Looker Jumpstart proactively addresses these risks by providing best-practice guidance on technical setup, data connectivity, and overall implementation strategy. 

The “Technical Setup Review” specifically focuses on providing best-practice advice on Looker technical setup, with an emphasis on data security. This expert oversight helps avoid common pitfalls and ensures a smoother and more successful deployment.

7. Fostering a Data-Driven Culture and Driving Adoption 

Technology adoption is not solely about the tools; it’s also about the people and processes. Looker Jumpstart includes a focus on change management to help embed data-driven practices within your organisation. 

By training and enabling business users, the program empowers individuals across different departments to access and analyse data independently, fostering a culture of data-informed decision-making and driving Looker adoption. Offerings like our Business User training and strategies to “Grow the Looker Love” further support this cultural shift.

5. Interesting Looker Use Cases

The Looker Enterprise suite can help you with the more advanced use cases for data.  Your data engineers can craft the business metrics your users will use in downstream data applications including Looker Studio via its LookML language. You have fine grain control about how data is defined and to ensure everyone is applying the same business rules to your data.  

You will also be able to create data applications beyond dashboards, and make your dashboards interactive e.g. A dashboard indicates a campaign is over performing – so the end user can press a button on the ashbord itself and increase the campaign budget without leaving its interface.  This helps with the 4th and 5th milestones given above.

Once you are leveraging data well, you are in a good position to try and extract more information out of your data via machine learning solutions.  Looker is able to interact with BigQueryML, a managed machine learning system created via SQL, for instance, but will also interface with all other popular machine learning pipelines and endpoints, covering your for the 6th milestone.

Besides modelling eCommerce website analytics data, in this section we look into other business use cases, which Looker can help bring into easy to understand, easy to build, and easy to maintain analytics solutions.

User Conversion Funnel

For quite some business use-cases, it is crucial to know how the user conversion funnel for a website or an app looks like. A user Conversion Funnel can be explained as a flow which users go through while using an app or a website. 

If we ignore the website/app visitors for a moment (those users, who visit the website and leave), we can look at the registered users, acquired users and verified users. Registered users would be those users that have registered on the website/app. 

Acquired users would be those that have carried out a transaction/purchase on the website/app. And then verified users would be those that have performed n-th transaction/purchase on the website/app. 

What is interesting to see from a marketing perspective, is what is the fraction of users that get acquired and converted. Based on the results, the company’s marketing department then can take specific actions to increase the fraction of the users that keep constantly making transactions on the website/app.

Top to Bottom Analysis of Marketing Metrics

Depending on what one is following in the data, such as top 20 countries with the most users, top 30 brands with the most sales or bottom 15 categories with the least average sale price; in Looker one can display these types of analyses visually in a very easy and consumable way. 

Looker would also take care of any displacement/change in the ranking of your data, therefore minimising the effort to keep visualisation up to date.

Comparing data of Two Different Periods

It is very common in any sort of analysis that one would like to compare data in different periods of time to one another, for example how many transactions have been created in this month, compared to the last month. 

In Looker it is possible to implement period over period analysis, where one can compare different time periods that the data is represented in, therefore allowing one to save time by disregarding any complex spreadsheets one has for that purpose. 

By allowing selection of different periods in the drop downs, in an instance Looker will show the comparison of the data.

6. Why Devoteam Is Your Expert Consulting Partner for Looker

Choosing Devoteam for your Looker Jumpstart program provides access to a team of experienced professionals with a proven track record of helping businesses succeed with Looker. Our expertise encompasses:

• Deep Looker Expertise: Benefit from our in-depth understanding of the Looker platform, its capabilities, and best practices for implementation and adoption.
• Proven Delivery Methodology: Devoteam employs a focused delivery methodology centred around achieving tangible business outcomes, ensuring effective technology delivery. We understand the importance of aligning technology delivery with business outcomes and building a data culture for long-term success.
• Tailored Approach: We prioritise understanding your specific Looker project success criteria and tailoring the Jumpstart program to help you achieve your desired results.
• Comprehensive Support: From initial planning and architectural design to hands-on training and ongoing advisory services, Devoteam provides end-to-end support throughout your Looker journey.