Looker is one of several leading business intelligence (BI tools) that can help your organization harness the power of your data and glean impactful insights that allow you to make the best decisions for your business.
In this post, we’ll provide a high-level overview of Looker, including a description of the tool, why you should use it, pros and cons, and easily integrated tools and technologies to augment your Looker reporting.
Overview of Looker
Looker is a powerful BI tool that can help a business develop insightful visualizations. Among other benefits, users can create interactive and dynamic dashboards, schedule and automate the distribution of reports, set custom parameters to receive alerts, and utilize embedded analytics.
Why Use Looker
If you’re looking for a single source of truth, customized visuals, collaborative dashboards, and top-of-the-line customer support, Looker might be the best BI platform for you. Being fully browser-based cuts down on confusion as your team gets going, and customized pricing means you get exactly what you need.
Pros of Looker
Looker offers performant and scalable analytics on a near-real-time basis.
Because you need to define logic before creating visuals, it enforces a single-source-of-truth semantic layer.
Looker is completely browser-based, eliminating the need for desktop software.
It facilitates dashboard collaboration, allowing parallel development and publishing with out-of-the-box git integration.
Cons of Looker
Looker can be more expensive than competitors like Microsoft Power BI; so while adding Looker to an existing BI ecosystem can be beneficial, you will need to take costs into consideration.
Compared to Tableau, visuals aren’t as elegant and the platform isn’t as intuitive.
Coding in LookML is unavoidable, which may present a roadblock for report developers who have minimal experience with SQL.
Select Complementary Tools and Technologies for Looker
Any SQL database
Was this high-level overview of Looker helpful? If you’d like to learn more about Looker reporting or discuss how other leading BI tools, like Tableau and Power BI, may best fit your organization, contact us to learn more.
So, you’ve been tasked with building an analytics dashboard. It’s tempting to jump into development straight away, but hold on a minute! There are numerous pitfalls that are easy to fall into and can ruin your plans for an attractive, useful dashboard. Here are five important principles for dashboard development to keep in mind every time you open up Power BI, Tableau, Looker, or any other BI tool.
1. Keep it focused and defined.
Before you start answering questions, you need to know exactly what you’re trying to find out. The starting point of most any dashboarding project should be a whiteboarding session with the end users; the dashboard becomes a collection of visuals that hold the ability to answer their questions.
For every single visual you create, make sure you’re answering a specific question. Each graph needs to be intentional and purposeful, and it’s very important to have your KPIs clearly defined well before you start building. If you don’t include your stakeholders from the very beginning, you’ll almost certainly have a lot more reworking to do after initial production is complete.
Courtesy of discourse.looker.com
2. A good data foundation is key.
Generating meaningful visualizations is nearly impossible without a good data foundation. Unclean data means holes and problems will need to be patched and fixed further down the pipeline. Many BI tools have functions that can format/prepare your data and generate some level of relational modeling for building your visualizations. However, too much modeling and logic in the tool itself will lead to large performance issues, and most BI tools aren’t specifically built with data wrangling in mind. A well-modeled semantic layer in a separate tool that handles all the necessary business logic is often essential for performance and governance.
Don’t undervalue the semantic layer!
The semantic layer is the step in preparation where the business logic is performed, joins are defined, and data is formatted from its raw form so it’s understandable and logical for users going forward. For Power BI users, for example, you would likely generate tabular models within SSAS. With a strong semantic layer in place before you even get to the BI tool, there will be little to no data management to be done in the tool itself. This means there is less processing the BI tool needs to handle and a much cleaner governance system.
In many BI tools, you can load in a raw dataset and have a functional dashboard in 10 minutes. However, building a semantic layer forces you to slow down and put some time in upfront for definition, development, and reflection about what the data is and what insights you’re trying to get for your business. This ensures you’re actually answering the right questions.
This is one of the many strengths of Looker, which is built specifically to handle the semantic layer as well as create visualizations. It forces you to define the logic in the tool itself before you start creating visuals.
It’s often tempting to skip the data prep steps in favor of putting out a finished product quickly, but remember: Your dashboard is only as good as the data underneath it.
3. PLEASE de-clutter.
There are numerous, obvious problems with the dashboard below, but there is one lesson to learn that many developers forget: Embrace white space! White space wants to be your friend. Like in web development, trying to pack too many visuals into the same dashboard is a recipe for disaster. Edward Tufte calls it the “data to ink ratio” in his book The Visual Display of Quantitative Information, one of the first and most impactful resources on data visualization.
Basically, just remove anything that isn’t essential or move important but non-pertinent information to a different page of the dashboard/report.
4. Think before using that overly complicated visual.
About to use a tree-map to demonstrate relationships among three variables at once? What about a 3-D, three-axis representation of sales? Most of the time: don’t. Visualizing data isn’t about making something flashy – it’s about creating something simple that someone can gain insight from at a glance. For almost any complex visualization, there is a simpler solution available, like splitting up the graph into multiple, more focused graphs.
5. Keep your interface clean, understandable, and consistent.
In addition to keeping your data clean and your logic well-defined, it’s important to make sure everything is understandable from start to finish and is easy to interpret by the end users. This starts with simply defining dimensions and measures logically and uniformly, as well as hiding excess and unused columns in the end product. A selection panel with 10 well-named column options is much easier than one with 30, especially if end-users will be doing alterations and exploration themselves.
You may notice a theme with most of these principles for dashboard development: Slow down and plan. It’s tempting to jump right into creating visuals, but never underestimate the value of planning and defining your steps first. Doing that will help ensure your dashboard is clean, consistent, and most important, valuable.
If you need help planning, implementing, or finding insights in your dashboards, the 2nd Watch team can help. Our certified consultants have the knowledge, training, and experience to help you drive the most value from your dashboard tool. Contact us today to learn about our data visualization starter pack.
Healthcare executives often must quickly make informed decisions that affect the trajectory of their business. How can they best access and analyze the key performance indicators (KPIs) needed to make those decisions? By referring to well-designed healthcare dashboards.
Using tools such as Looker, Power BI, and Tableau, healthcare organizations can integrate customizable, interactive dashboards into their reporting to improve decision-making across a range of areas – from healthcare facility operations and surgeon performance to pharmaceutical sales and more. We’ve compiled several healthcare dashboard examples to illustrate a variety of use cases and a sampling of dashboard design best practices to help your organization make the most of your data.
A Healthcare Dashboard Using Data Science
A healthcare dashboard like this one demonstrates how a company could use data science to determine sales projections – in this case, pharmaceutical sales – and guide their field reps’ sales activities. To make the information easy to digest, this dashboard uses the rule of thirds (a design principle that draws a viewer’s eye to points of interest – you’ll notice variations on the rule of thirds throughout these healthcare dashboard examples):
New prescription (NRx) and total prescription (TRx) values at the top make goals and expectations easily accessible.
A visual in the middle illustrates the TRx projection over time, also broken down into specific drugs for competitive analysis.
Customer segmentation comparisons at the bottom provide broader context and more actionable information about doctors prescribing the company’s drugs. This section indicates the company’s most valuable customers and the areas where their competitors are seeing higher growth, allowing them to target accordingly.
Combining projections and customer segmentation information from a dashboard like this one, a company can adjust their sales strategy as necessary or determine they don’t need to change their sales plan to meet their goals.
A Customized Healthcare Dashboard in Tableau
This dashboard provides quick and easy insight into the financials and patient visits specific to this organization’s California healthcare facilities. It shows the most valuable metrics at the top of the dashboard and enables executives to drill into those that warrant further investigation below.
Using a custom map feature in Tableau, company executives can quickly see which zip codes generate the most revenue and, even more specifically, which individual facilities contribute the most patient encounters. Users are able to uncover areas of improvement across the business such as facilities with lower levels of visits than expected or a revenue-to-encounter ratio not meeting profitability expectations. Conversely, this dashboard also highlights successful facilities that less-successful facilities can model themselves after.
A Regional Healthcare Finance Dashboard
This dashboard showing clinical practice financial data and patient visit information would be referenced by two audiences: the clinical practice’s financial team and the practice owner. In this case, the owner is a private equity firm. Including overall financial performance and that of individual practices in one healthcare finance dashboard allows the private equity firm to understand practice financial data in context.
The heat map provides an easy-to-digest visual to communicate location-specific patient encounter information. Each clinical practice can reference the map to see how the number of patient encounters correlates to their individual financial information, and the private equity firm can understand how location impacts operating costs and revenue across multiple nearby facilities. They’re able to see which clinics and which zip codes bring in the greatest net revenue, allowing them to strategically approach potential new healthcare practice acquisitions.
An Interactive Surgeon Performance Dashboard
Hospital administrators and department heads would use a dashboard like this to evaluate surgeons’ performance. This could be the average time spent in the operating room for a specific surgery, patients’ average time in the hospital recovering after surgery, or how patients fare in emergency surgeries vs. planned surgeries. Alternatively, surgeons could evaluate themselves and learn about areas for improvement.
Using multiple spreadsheets to dig into the factors relevant to a surgical department evaluation can be time-consuming and confusing. Instead, this healthcare dashboard example provides a high-level view of a neurology department and also details regarding each surgeon. The dashboard is interactive, so users can filter in many ways to evaluate one surgeon or multiple surgeons against each other.
A user can easily identify potential issues, such as a surgeon spending a significant amount of time in the operating room for very few surgeries. Department heads can then drill down further into the average age of patients, the surgeries performed, and how many surgeries were planned vs. emergency to see if those factors explain the amount of time spent, all without having to reference multiple spreadsheets or dashboards.
A High-Level Sales KPI Dashboard
Similar to the dashboard using data science shared at the beginning of this post, this highly visual dashboard gives the user a quick overview of KPIs. The user would likely be high-level sales employees at a pharmaceutical company who don’t want to be bogged down with details upfront. However, they can click through this “nine-box stoplight” to get more information as needed.
A user could click on the box showing the change in NBRx (prescriptions for patients who are new to the brand) to see related trends or details about specific sales reps that contributed to this “green light,” a positive indicator. They could then investigate the negative change in TRx by clicking on that box. Pulling in only the details the user needs, they are better able to explain these numbers without the distraction of unrelated details. Perhaps the brand saw great growth in the number of distinct HCPs (healthcare providers) prescribing NBRx but not prescribing high enough volume of RRx (returning patients) to positively affect TRx. Or maybe a high-performing sales rep was on maternity leave and therefore not selling this month, causing a temporary drop in TRx.
A Dialysis Clinic Dashboard in Looker
Dialysis facility owners, administrators, and directors can use a dashboard like this one, created using Looker, to track dialysis machine utilization and allocate machine staffing needs based on location. This dashboard tells a full story with data, moving from a broad picture of total patient encounters, through dialysis-specific encounters, and drilling into details at the facility level.
Like the previous healthcare dashboard examples, this dashboard is driven by the rule of thirds. It first provides KPIs, then highly visual representations of location-specific information (both individual facilities and another zip code heat map), and finally a broader data point broken down based on time. Within one dashboard, a user can understand their data from multiple viewpoints to draw varied, in-depth insights.
These healthcare dashboard examples demonstrate how a well-designed dashboard can uniquely meet the needs of a range of healthcare-related organizations. Healthcare executives can make the most of their data and empower their business users with customizable, interactive dashboards. To learn how 2nd Watch could help your healthcare organization develop dashboards that best suit your needs, set up a complimentary healthcare analytics whiteboard session.
When working with JD Edwards, you’ll likely spend the majority of your development time defining business logic and source-to-target mapping required to create an analyzable business layer. In other words, you’ll transform the confusing and cryptic JDE metadata into something usable. So, rather than working with columns like F03012.[AIAN8] or F0101.[ABALPH], the SQL code will transform the columns into business-friendly descriptions of the data. For example, here is a small subset of the customer pull from the unified JDE schema:
Furthermore, you can add information from other sources. For example, if a business wanted to include new customer information only stored in Salesforce, you can build the information into the new [Customer] table that exists as a subject area rather than a store of data from a specific source. Moreover, the new business layer can act as a “single source of the truth” or “operational data store” for each subject area of the organization’s structured data.
Looking for Pre-built Modules?
2nd Watch has built out data marts for several subject areas. All tables are easily joined on natural keys, provide easy-to-interpret column names, and are “load-ready” to any visualization tool (e.g., Tableau, Power BI, Looker) or data application (e.g., machine learning, data warehouse, reporting services). Modules already developed include the following:
Hosting Analyzable JDE Data
After creating the data hub, many companies prefer to warehouse their data in order to improve performance by time boxing tables, pre-aggregating important measures, and indexing based on frequently used queries. The data warehouse also provides dedicated resources to the reporting tool and splits the burden of the ETL and visualization workloads (both memory-intensive operations).
By design, because the business layer is load-ready, it’s relatively trivial to extract the dimensions and facts from the data hub and build a star-schema data warehouse. Using the case from above, the framework would simply capture the changed data from the previous run, generate any required keys, and update the corresponding dimension or fact table:
Simple Star Schema
Evolving Approaches to JDE Analytics
This approach to analyzing JD Edwards data allows businesses to vary the BI tools they use to answer their questions (not just tools specialized for JDE) and change their approach as technology advances. 2nd Watch has implemented the JDE Analytics Framework both on premise and in a public cloud (Azure and AWS), as well as connected with a variety of analysis tools, including Cognos, Power BI, Tableau, and ML Studio. We have even created API access to the different subject areas in the data hub for custom applications. In other words, this analytics platform enables your internal developers to build new business applications, reports, and visualizations with your company’s data without having to know RPG, the JDE backend, or even SQL!
Maybe you’re venturing into data visualization for the first time, or maybe you’re interested in how a different tool could better serve your business. Either way, you’re likely wondering, “What is Looker?” and, “Could it be right for us?” In this blog post, we’ll go over the benefits of Looker, how it compares to Power BI and Tableau, when you may want to use Looker, and how to get started if you decide it’s the right tool for your organization.
What is Looker?
Looker is a powerful business intelligence (BI) tool that can help a business develop insightful visualizations. It offers a user-friendly workflow, is completely browser-based (eliminating the need for desktop software) and facilitates dashboard collaboration. Among other benefits, users can create interactive and dynamic dashboards, schedule and automate the distribution of reports, set custom parameters to receive alerts, and utilize embedded analytics.
How is Looker different?
We can’t fully answer “What is Looker?” without seeing how it stacks up against competitors:
Does Looker fit into my analytics ecosystem?
When to Use Looker
If you’re looking for customized visuals, collaborative dashboards, and a single source of truth, plus top-of-the-line customer support, Looker might be the best BI platform for you. Being fully browser-based cuts down on potential confusion as your team gets up and running, and pricing customized to your company means you get exactly what you need to meet your company’s analytics goals.
When Not to Use Looker
If you’ve already bought into the Microsoft ecosystem, Power BI is your best bet. Introducing another tool will likely only create confusion and increase costs.
When someone says “Tableau,” the first thing that comes to mind is how impressive the visuals are. If you want the most elegant visuals and a platform that’s intuitive for analysts and business users alike, you may want to go with Tableau.
How do I get started using Looker?
You can get started using Looker in four basic steps:
1. Determine if your data is analytics ready
Conduct an audit of where your data is stored, what formats are used, etc. You may want to consider a data strategy project before moving forward with a BI platform implementation.
2. Understand your company’s BI needs and use cases
Partner with key stakeholders across the business to learn how they currently use analytics and how they hope to use more advanced analytics in the future. What features do they or their staff need in a BI tool?
3. Review compliance and data governance concerns
When in conversation with those key stakeholders, discuss their compliance and data governance concerns as well. Bring your technology leaders into the discussion to get their valuable perspectives. You should have an enterprise-wide stance on these topics that informs any additions to your tech stack.
4. Partner with a trusted resource to ensure a smooth implementation
Our consultants’ hands-on experience with Looker can contribute to a faster, simpler transition. Plus, 2nd Watch can transfer the necessary knowledge to make sure your team is equipped to make the most of your new BI tool. We can even help with the three previous steps, guiding the process from start to finish.
If you still have questions about if Looker is worth considering for your organization, or if you’re ready to get started with Looker, contact us here.