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An archive of Looker blog posts
This subcategory contains all of the technical content from the deprecated Looker Blog (https://looker.com/blog).We will not be updating Blog Archive content, nor do we guarantee that everything is up-to-date. If you’re looking for officially supported resources, you can always visit our Help Center and review our Docs.
This content, written by Joel McKelvey, was initially posted in Looker Blog on Feb 27, 2018. The content is subject to limited support.At Looker, we are honored to debut in the 2018 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms. Gartner is a leading IT research and advisory firm that helps businesses of all sizes evaluate technology and make informed decisions. Being included in the report at all is a pretty big deal and being the only new entrant this year is something we’re really proud of. Personally, I’m excited that Looker is now recognized as one of the biggest names in BI and analytics... I believe this is a clear validation of Looker’s unique capabilities and our innovative approach to flexible, agile analytics built on a data platform that takes advantage of the power of today’s technology. Analytics evolved It’s important to note that Looker is more than just a business intelligence tool – we’re focused on bringing people together and connecting
This content, written by Shohei Narron, was initially posted in Looker Blog on May 24, 2016. The content is subject to limited support.You’ve built a great product that resonates with the market. Your customers are enthusiastic, and word of mouth is overwhelmingly positive. The in-house picture is cloudier. Your end users can’t run their own queries, which means they have to wait for someone else to generate the information they need. Because your solution requires moving data out of your database, analysis is complicated and time-consuming, placing your data analysts in an uncomfortable position: just by doing their jobs, they’re creating a bottleneck. Your engineers would love to help, but wouldn’t focusing on your core offering be a better use of their time? You’re now considering adding value to your customers by giving back the data you’ve collected through a new analytics offering, and you wonder whether to build the tool yourselves or buy an existing tool. Enter Powered By Looke
This content, written by Matt Ryan, was initially posted in Looker Blog on Dec 12, 2018. The content is subject to limited support. is vital for every company. Additionally, data privacy has become increasingly mission critical due to GDPR and other global privacy regulations. We see evidence of this everywhere -- from headlines in the press to changes in budgets and priorities. And according to : “Data analytics and security will dominate CIO spending in 2018 and 2019.” As the demand for data privacy and security has increased, so too has the demand for access to the data necessary for continued business innovation. It is the responsibility of organizations today to bridge the gap between data supply and demand in a way that keeps with privacy laws. The architecture of the Looker data platform simplifies database security by leveraging world-class database technologies, providing comprehensive data governance, and a robust audit trail. The benefit of a centralized database Cloud dat
This content, written by Haarthi Sadasivam, was initially posted in Looker Blog on Nov 6, 2017. The content is subject to limited support.The customers have spoken and, as featured in the , have selected Looker as the Leader in the Embedded Analytics space. So, what exactly does embedding with Looker look like? Powered by Looker Looker’s embedded analytics platform, is all about accessing data within your daily workflow. That could mean public static reports on your website, a private authenticated dashboard in your day-to-day tools, or a full embedded experience within your product for your customers and suppliers. The great thing about all these different use cases is that they all leverage the Looker platform without having you reinvent the wheel. Our powerful modern data modeling layer makes it easy to define your metrics or reports just once for all your different users. Also, your stakeholders can make use of Looker’s reliable scheduling, alerting, and data delivery capabilities
This content, written by Dean Wenstrand, was initially posted in Looker Blog on Aug 22, 2017. The content is subject to limited support.Someone told me that marketing attribution was a ‘sexy’ thing to talk about in the analytics community. Despite all the content on the topic, and there is a ton, one important fact gets overlooked almost all the time and the tips below will make sure you don’t miss it. It’s a bit of a secret but I think savvy digital marketers and their analyst counterparts deserve to know. Multi-touch, last-in, w-shaped, time decay, first-touch, etc.… none of it, by itself, actually matters. This is what matters: do the numbers that come out of your attribution model actually correlate with functional success and do the people who use it, in all their human glory, actually believe in and utilize those numbers to increase the success of your business? That’s it, that’s the secret. For all the beautiful theory, analytical purity and inflexible logic of specious attribut
This content, written by Erin Franz, was initially posted in Looker Blog on Mar 21, 2018. The content is subject to limited support.Even though most product and engineering organizations today are collecting tons of data, most only use a fraction of what is available. This is often due to competing priorities or lack of implementation experience in data strategy. Looker and are working together to make data-driven product development workflows a reality. There’s no silver bullet when it comes to making data-backed decisions the norm, but there are steps you can take to shift the culture of your team, or even your company. 1: Expand your definition of data Data from instrumented products is increasingly valuable to product teams because it shows how users interact with the product directly. But data from the product itself isn’t the only source of information you can use to improve your product and workstreams. Data from issue tracking tools can provide valuable insight into historical
This content, written by Denise Duffy, was initially posted in Looker Blog on Aug 6, 2018. The content is subject to limited support.Competition as an eCommerce or marketplaces business is fierce. Consumer expectations increase a little with every new website or app that launches: more options, the ability to customize, lower pricing, and seamlessly speedy service. How do you keep up with the competition let alone exceed expectations to stand out? Here are 7 questions the best companies, like Amazon, Deliveroo, and Etsy ask themselves: 1. Where are your customers coming from? There are a few things you should know when paying to acquire customers. You need to know where they’re coming from and how much it costs to bring them to your store. To which channel can you attribute these acquisitions? Exactly how many customers were acquired, and at what cost? With the right tools, you can build your own cross-programme metrics that empower you to take control of customer attribution claims, a
This content, written by Daniel Mintz, was initially posted in Looker Blog on Jan 10, 2018. The content is subject to limited support.To many people, analytics tools all look the same. Some dashboards, a few reports, a way to slice data. That’s because the real differentiators lie behind the scenes, where the great tools are separated from the merely good ones (and the not so good ones). Behind Looker’s pretty face is something quite revolutionary: LookML. And even though the vast majority of users will never see a line of LookML, this new language for data is what makes Looker uniquely powerful, agile, and trustworthy--for everyone. LookML has plenty of fans (), but not everyone is immediately sold. When skeptics hear about LookML, they usually have one of two reactions: “Why invent a new data language when SQL already exists?” or “I have to write code (😱😩😭)? Can’t I use a graphical user interface (GUI) instead?” And if you’re not familiar with LookML, these questions are totally r
This content, written by Joel McKelvey, was initially posted in Looker Blog on Dec 2, 2019. The content is subject to limited support.Visit the Looker team at AWS re:Invent in Las Vegas, December 2-8th at booth #1405 and ask us about our integrations with AWS. As an AWS Advanced Technology Partner, we are excited to talk about our continued integration with AWS — something many of our customers have come to value and rely upon. To kick off what will be another exciting re:Invent conference in Las Vegas this week, we put together a “top 7” list of facts to highlight all the ways Looker and AWS are better together. Fact one Over 50% of all Looker customers are using Looker with Amazon , , or another AWS database. Using an AWS database has a number of benefits, but one of the cooler things you can access is geospatial data via Athena (check out our to learn how that works). Fact two More than 90% of . And for Looker customers who choose to host their own instances, many of them use AWS a
This content, written by Sooji Kim, was initially posted in Looker Blog on Aug 16, 2017. The content is subject to limited support.If you’re anything like me, as soon as your manager came to you with the novel idea of testing the company’s website to improve conversion, you might have done a few (or all) of these things. Stare blankly into the space between their eyes and nod. Say you’ll have something for them in a week. Try to figure out where to start. Google “how to A/B test.” And, if you do actually search “how to A/B test,” you’ll get a ton of results—62,400,000 to be exact-ish. From beginner’s guides to “proven” tactics and ideas, it can get pretty overwhelming to figure out how to get your testing strategy and process started. So when it came to A/B testing looker.com, I started where any employee of a data-obsessed company would: with our web analytics data. With that came a starting point for testing ideas, strategies, and processes that we continue to optimize and fine-tune,
This content, written by Daniel Mintz, was initially posted in Looker Blog on Jun 15, 2016. The content is subject to limited support.Hi. My name is Daniel, and I love SQL. Ever since I first started learning how to write queries on MySQL a decade ago, I've loved the discipline that SQL imposed on me. It forced me to think through my analytic questions in a, well, structured way. And using SQL, I could answer just about any question I could come up with. It's an immensely satisfying and empowering feeling, and it's made me better at whatever job I held. With SQL and the right data, whenever I had to make a decision I could make it better using data. Having professed my love for SQL, I should also point out that I'm fully aware of its warts. SQL is, for all intents and purposes, a write-only language. Even when I'm looking at a query I wrote, if it's more than a couple weeks old, it's generally easier to start from scratch than try to figure out what the heck I was doing. And collaborat
This content, written by Brett Sauve, was initially posted in Looker Blog on Dec 2, 2014. The content is subject to limited support.A lesser known feature of some SQL dialects is something called the "window function". While MySQL users will be left out in the cold, most other SQL dialects can take advantage of their power. They can be a little tricky to wrap your mind around at first, but certain calculations - which are very complex or impossible without window functions - can become straightforward. Intriguing ... To demonstrate the power of window functions, let's take a look at an example set of customer data:name status lifetime_spend Neil Armstrong Platinum 1000.00 Buzz Aldrin Platinum 2000.00 Yuri Gagarin Platinum 3000.00 John Glenn Gold 400.00 Alan Shepard Gold 500.00 Jim Lovell Gold 600.00 Now suppose you want to know how the customer ranks in spending against the other customers in their status. In other words, you're hoping for a result se
This content, written by Kevin Marr & Joel McKelvey, was initially posted in Looker Blog on Aug 11, 2020. The content is subject to limited support.If you depend on data to make critical business decisions, ensuring that queries are fast and efficient is paramount. To achieve high performance and efficient queries, most analytic tools use techniques that have one thing in common: limiting the size of the dataset queried. Desktop analytics tools accomplish this by using cubes or extracts of data. With other legacy tools, achieving faster queries means data must be heavily transformed, formatted, or aggregated in some way. Certain systems are designed to split data into subsets and index it, thus calling only on that subset when a query is run. While these are great strategies to protect slower databases or legacy data tools from becoming overloaded, when handling most companies' data, these approaches are also flawed. Extracts of data can become stale and are difficult to govern. Bu
This content, written by Brian Lafaille, was initially posted in Looker Blog on Apr 19, 2016. The content is subject to limited support.This is part 2 of my on using data to improve Customer Success and Account Management. The formula as a Customer Success Manager in SAAS is really quite simple. Reduce churn; drive more revenue. It’s the formula leading to the coveted SAAS goal of . That first goal can certainly be , but how can you use data to drive new business from existing accounts? For the Looker Customer Success team data is an integral part of our up-sell workflow. Data as a prospector New revenue for Looker means more people using data. But instead of spending cycles prospecting new business opportunities within existing customer accounts, our team focuses on helping customers with new use-cases, expanding current use-cases, advising on ways to ingest new data sources, and how best to onboard new teams. Happy customers help sell Looker for us (). If we do our jobs well, up-s
This content, written by Eric Carr, was initially posted in Looker Blog on Nov 26, 2018. The content is subject to limited support. have been strategic partners since shortly after Looker’s inception. Looker hosts its instances in Amazon Web Services (AWS), and over 55% of our clients are using one of the many Amazon-hosted cloud databases such as , Athena, and various Relational Database Service (RDS) flavors as their primary Looker data sources. With such compatible products and hundreds of joint customers, Looker and AWS are continuously working together to make the end-user experience more streamlined, which makes re:invent one of the annual highlights for our team and customers. This year is no exception. At AWS re:Invent 2018, we’re announcing an integration with AWS SageMaker, as well as a new trial of Amazon Redshift and Looker. We’re excited about both of these additions because we believe that the combination of Looker and Amazon is truly changing the lives of our joint custo
This content, written by Elena Rowell, was initially posted in Looker Blog on Jun 18, 2020. The content is subject to limited support.Looker’s platform offers a unified surface to access the truest, most up-to-date version of your company’s data. With this unified view into the business, you can choose or design the experience that makes the most sense for what your users need. For many companies this includes making actionable insights available to users where they’re working. In this blog I’ll talk about going beyond insights that are action-oriented and creating experiences that actually facilitate insight-driven actions. Looker’s Action Hub enables customers to magnify the impact of unified, governed data by sending data to, and taking action in, systems outside of Looker. In this blog, I’ll break down three powerful uses for the Action Hub: Securely and reliably send governed data to other systems Trigger workflows in other systems based on unified metrics in Looker Bring routine
This content, written by Kyle Coleman, was initially posted in Looker Blog on Sep 20, 2018. The content is subject to limited support.Performance optimization in the world of inside sales and call analytics often focus on vanity metrics. Vanity metrics are surface-level data points that are good for comparison, but don't necessarily lead to actionable insights. For example, reports on number of calls made or connected can give you a good sense of rep performance, but provide little insight into why the numbers may be the way they are. You may notice a lower call volume due to seasonality or tracking error. By focusing on why the number of calls seem low or high, you’re able to make better-informed decisions on how to optimize or bring awareness to the sales process. Using Looker, our inside sales team is able to view and analyze data beyond vanity metrics, and instead focus on actionable metrics – those that drive behavior, growth, and learning. What are call analytics? Call analytics
This content, written by Dean Wenstrand, was initially posted in Looker Blog on Mar 9, 2016. The content is subject to limited support.Lots of businesses make good use of recommendation engines based on affinity analysis to improve their offering. Let’s look at three examples before diving into what often can seem like black magic. Playlists & song recommendations Have you ever been listening to a playlist in a public place and thought to yourself, “this playlist is sooo good, whoever made this is a genius!” only to discover it was Pandora? That Pandora can deliver that experience is their competitive advantage and the basis of that is taking a song selection and making great recommendations based on it. Amazon product recommendations Amazon.com has gained notoriety for countless things from drones to their razor thin margins. One less talked about part of Amazon.com is their recommendation engine which suggests high-affinity products based on customers’ purchase and browsing histo
This content, written by Brett Sauve, was initially posted in Looker Blog on Jun 10, 2014. The content is subject to limited support. NOTE: When this entry was originally written, Looker users needed to use the following strategies to properly calculate certain metrics. Since that time Looker has implemented , which make much of this unnecessary and a great deal easier. However, this is still a useful entry to read for writing SQL in general, or for SQL dialects that do not support Symmetric Aggregates. If you’re using SQL some of the first things you probably learned about were joins and aggregate functions (such as COUNT and SUM). One thing that is not always taught is how these two concepts can interact, and sometimes produce incorrect results. In this entry we’ll discuss the things to look out for, the concept of a “fanout”, and why it matters to SQL writers and Looker users alike. Let's start with a friendly join Let’s start off with a simple example, where we’ll join together
This content, written by Josh Dreyfuss, was initially posted in Looker Blog on Sep 19, 2018. The content is subject to limited support.Imagine that everything you wrote had to be written on a typewriter. Any typos meant getting out the Wite-Out, and any larger edits meant retyping the whole page. Now compare that to our reality today, where we have word processors that allow us to edit and update our work instantly. By virtualizing the process of typing, we unlocked much more efficiency and solved huge editing and cleaning pain points. What word processors have done for the written word, data virtualization does for the world of data. In , we provide an introduction to data virtualization, share the key pains it alleviates for data teams and analysts, and take a look at the data virtualization landscape. While the white paper takes a deep dive into several real world examples where data virtualization alleviates problems, this post aims to focus on where data virtualization fits into
This content, written by Jesse St. Charles, was initially posted in Looker Blog on Nov 28, 2017. The content is subject to limited support.Our mission at frame.ai is to make it easy to build better relationships with your customers (internal and external) using Slack. As a small team of startup vets, we often need to work quickly and independently, so as Head of Data, I’ve typically built and configured the services I need myself. Our ability to make quick changes was recently put to the test. Responding to an increasingly common customer need, I was able to design, prototype, and safely deploy a new analytics dashboard to a large subset of Frame’s customers in eight hours. This new dashboard wasn’t just a nice-to-have, either. As a result of it, one of our partners was able to make critical budget decisions that week based on the new visibility this dashboard provided. Our agility made the difference for them. Now, I’ll readily admit that although I have strong data science background
This content, written by Kenny Cunanan, was initially posted in Looker Blog on Feb 13, 2018. The content is subject to limited support. Today, Looker is announcing the availability of new features that will make it easy for everyone in an organization to have access to the freshest data in order to get rapid, clear answers to nearly any question. Historically, businesses have struggled to get value out of their data. In a 2015 report,”Digital Insights are the new Currency of Business”, Forrester found that . How are organizations today missing out on getting the full advantage of the insights hidden in their data? There’s a general understanding that data hides information of great value, but collecting data is only half of the equation. In order to get better answers, you need to ask better questions. The best questions are asked by people with deep experience with the business -- and while these people are frequently experts in their area, they’re not usually experts in analytics. So
