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Blog Archives

An archive of Looker blog posts

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LanLooker Staff

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About "Blog Archives"

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.

Department of Customer LoveLooker Staff

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You're not normal

This content, written by Daniel Mintz, was initially posted in Looker Blog on May 11, 2016. The content is subject to limited support.One of the trickiest things about being human is that you only get to see the world from one perspective. This is relevant not just for the reasons that , but also because it makes putting yourself in someone else’s shoes really hard. And it’s not getting easier. As we’ve gained tools to reach larger and more diverse audiences, putting yourself in their shoes has simultaneously gotten much harder and much more critical. After all, if you’re only fashioning tools for members of your village, you understand intuitively how they’ll be used. But when your potential audience is global—living in contexts and locations you can’t even dream of—you’re far from an average user. And yet, while potential audiences are growing, the world each of us inhabits online has only shrunk, thanks to what my friend Eli Pariser calls the . When I was writing emails for MoveOn

Department of Customer LoveLooker Staff

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Empowering Yieldify with data: what has Looker done for us?

This content, written by Demis Bhojoo, was initially posted in Looker Blog on Apr 4, 2016. The content is subject to limited support.I recently stumbled upon Justin Kan’s old post This post reminded me of my time at - where my colleague and friend, Charles Dupont, wrote an excellent article on why learning SQL is important for any business team. The article describes how, through the use of data, a small team managed to automate 90% of repeatable tasks (obviously a huge time saver) and enabled individuals to delve deeper into the reasons why specific actions were successful or not. Having taken the leap early on, any new hire was able to use data to learn and develop effectively - and that’s how I got started in Business Intelligence. This short blog post reflects on how Looker has changed the way we work at Yieldify by empowering employees with easily accessible data - here we go. Business Intelligence or Intelligent Business? Since being deployed, the Business Intelligence (BI) te

Department of Customer LoveLooker Staff

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Will the last BI vendor please turn out the lights

This content, written by Nick Caldwell, was initially posted in Looker Blog on Jun 5, 2019. The content is subject to limited support.As someone who believes strongly in the potential for (BI) to empower people and transform organizations, I have an important concern to share with every like-minded BI pro: We aren't being disrupted, it's already happened. Despite increasingly powerful dashboards and data exploration tools, traditional approaches to business intelligence are struggling to meet the expectations of the modern data-driven workforce. Fortunately the core tenets of BI — that combining data makes it more valuable and that people are more powerful with data — are alive and well. What has changed is both the volume and diversity of data, as well as the expectations of end-users who depend on it. The result is that data demands are increasingly being satisfied with tools that simply aren't BI. This should excite us all as it presents a huge opportunity to rethink our data strat

Department of Customer LoveLooker Staff

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Why your company needs a self-service BI tool

This content, written by Kenny Cunanan, was initially posted in Looker Blog on May 31, 2019. The content is subject to limited support.Technology is continually and rapidly changing. For organizations today, this means that in order to stay ahead of the pack and get real-time answers to pressing questions, data management is a necessity. From sales reports to company or industry trends, analyzing raw data has become central to promoting growth and improving decision-making. Big data is the future, and can give your organization data on-demand and the ability to quickly connect analysis to action. What is self-service business intelligence? Self-service business intelligence refers to a set of tools that help companies manage their data, transforming raw numbers into streamlined reports that improve company-wide decision-making. These tools allow companies to promote collaboration across multiple departments and to utilize ad hoc querying. As a branch of data analytics, self-service bu

Department of Customer LoveLooker Staff

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Why we release every four weeks

This content, written by Colin Zima, was initially posted in Looker Blog on Aug 1, 2017. The content is subject to limited support.Looker cuts a new release every four weeks. In October of last year, we cut Looker 4.0. Next week, we’ll cut Looker 4.20. We have always worked this way, and don’t foresee changing it anytime soon. Our four-week release cycle is core to our engineering philosophy for two reasons. First, we believe in giving customers the features they want as soon as they’re ready. If there is a feature, be it small or large, that a customer thinks would help them get more value out of Looker, we want to get it into their hands as soon as possible. The second reason is data. Iterating on a feature is only possible if you’re able to work from real usage data. The sooner we can get features into the hands of users, the sooner we can know if it accomplished what we hoped it would. The best part about moving so fast is that we’re able to use the data produced in the first few w

Department of Customer LoveLooker Staff

published in Blog Archives

Why we built a data culture at Fivetran

This content, written by Garegin Ordyan, was initially posted in Looker Blog on Aug 9, 2019. The content is subject to limited support. At , we build technology that centralizes data from different applications into data warehouses, so enabling organizations to be data-driven is an essential part of our mission. But what does it mean to have a data-driven culture? My teammates think of this in a few ways: "A data-driven culture means that individuals are tuned in to start thinking about problems and their solutions with a data-focused mindset. Enabled by a data-driven culture, users across the organization can ask questions like: What can the data tell me about this problem?, What hypotheses can I test with the data we have available? and How will the changes I’m going to implement affect the data we’re collecting and other people who are leveraging this data?" — Christine Ndege, Solutions Engineer/Data Analyst at Fivetran "Creating a data-driven culture is something that requires bu

Department of Customer LoveLooker Staff

published in Blog Archives

Why nesting is so cool

This content, written by Daniel Mintz, was initially posted in Looker Blog on May 24, 2017. The content is subject to limited support.When you're setting up a data warehouse, one of the key questions is how to structure your data. And one of the trickiest types of data to deal with is hierarchically structured data (like orders and the order_items that make them up; or the pageloads that make up sessions). Do you completely normalize the data into a snowflake schema? Completely denormalize it into a very wide table with lots of repeated values? Or do something in the middle like a star schema? Historically, that's a decision you had to make as you were designing your warehouse. And it always involved trade offs. Denormalization (one big wide table with lots of repeated values) takes up more space. But by eliminating joins, it's faster for some queries. Normalization, on the other hand, avoids repeated values and yields more compact tables. So it's more space efficient and makes other t

Department of Customer LoveLooker Staff

published in Blog Archives

Why you should get involved in the data revolution

This content, written by Carrie Browde, was initially posted in Looker Blog on Jul 31, 2020. The content is subject to limited support.The anxiety of change is real. We’re all yearning to “get back to normal,” but how can we know what “normal” will look like — or when we may get there? While our mentors and peers encourage us to turn our fear of the unknown into excitement with a “go get ‘em” attitude, the truth is that we are in the midst of a global shift, and it’s hard to know what it is exactly we’re supposed to go and get. The answer is data. With data, you can equip yourself to understand what's happening, analyze the world, and make informed decisions. Even prior to COVID-19 shaking up our day-to-day activities, we saw organizations across the globe joining the data revolution by incorporating data into everyday business operations. And this observation wasn’t limited to a single sector: in the , enterprises like implemented data solutions to empower individual providers with

Department of Customer LoveLooker Staff

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Why centralized data access is key for your organization becoming 'GDPR ready'

This content, written by Daniel Mintz, was initially posted in Looker Blog on May 25, 2018. The content is subject to limited support.After the better part of two years of preparation, debate and conjecture across the technology industry, today, the general data protection regulation (GDPR) is finally upon us. In the past, the impact of this type of regulatory change would have been confined to the IT and data teams. Nowadays, however, nearly everyone handles data. From customer communications to employee records and beyond, much of this information will qualify as personal data. This means, according to the GDPR, that data must be controlled, used based on published commitments, secured, and ‘deletable’. Yet, for many companies, allowing access to data has typically required copying, exporting, and extracting data – which leaves a trail of personal data across any number of laptops, servers and systems, both inside the company and with third parties. Tackling data sprawl Once data is

Department of Customer LoveLooker Staff

published in Blog Archives

Why a visualization is worth a thousand data points

This content, written by Jill Hardy, was initially posted in Looker Blog on Jul 8, 2019. The content is subject to limited support.We talk about data visualization (or “viz”) a lot at Looker. We believe in its power to tell stories, to help people see patterns and take action accordingly. In other words, visualization matters—a lot. In the age of big data, visualization is one of the most powerful tools in your arsenal as an analyst or data enthusiast. It can so drastically affect the way we interpret and understand information that it may seem like magic; when, in fact, it works so well because of the way the brain processes large amounts of information. Get it right, and you supercharge your efficiency, your ability to draw connections, and your communication. Data visualization makes you more efficient Understanding the scope of data available today illuminates why visualization makes you more efficient, and how necessary it is for modern business. For example, this picture, taken o

Department of Customer LoveLooker Staff

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Why a data platform

This content, written by Daniel Mintz, was initially posted in Looker Blog on Feb 19, 2019. The content is subject to limited support.The world of business data has changed radically in the past decade. That might seem obvious by now, but it is worthwhile to understand exactly why and how things have changed. On the supply side, there’s simply more business data than ever before. Not a little bit more or a lot more, either—VASTLY more. The average enterprise today , plus custom enterprise software and transactional systems. All of those applications are data applications in that they capture huge amounts of data and produce clouds of data exhaust. At the same time, today’s workforce is hungrier for data than ever before because they see the enormous value in it. Marketers want to know which campaigns bring in the biggest customers. Sales managers want to understand pipeline coverage. Warehouse directors want to optimize utilization. FP&A wants to improve their forecasts. Product Ma

Department of Customer LoveLooker Staff

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Why a data company wants more IRL (in real life)

This content, written by Jen Grant, was initially posted in Looker Blog on Mar 5, 2019. The content is subject to limited support.Many people say that marketing is both an art and a science, but lately, with all the data available and SaaS tools that deliver amazing insights into the success of each and every campaign, I think we’ve gotten a little too much into the science. From emails, routing, personalization tools, and machine learning on our websites, everything we do is now automated. We judge ourselves by the data that shows whether these interactions have successfully gotten a click or an action. And sometimes, we forget that a click doesn’t really offer a solid connection with a person who might be interested in getting to know us better. Ironically for a data company, we at Looker like to emphasize more than just data. We focus on real interactions with people. I heard the phrase “IRL” from one of my kids and I’m in love with the sentiment. In Real Life. Let’s spend a moment

Department of Customer LoveLooker Staff

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Placing BI modernization at the heart of business transformation

This content, written by Josh Parenteau, was initially posted in Looker Blog on Nov 30, 2020. The content is subject to limited support.Today’s businesses are moving faster than ever, and that means they need to evolve to better achieve their goals and remain competitive. The world of e-commerce has dramatically changed consumer behavior and is driving new expectations for much of the global workforce. Employees increasingly expect the same immediate access to data and technology in their work lives as they do in their personal lives as the line between them continues to blur. With everything and everyone becoming digitally connected, organizations have to rethink traditional management and workforce constructs. The way that a successful company operates has changed: there are more remote workers today than ever before, and CFOs expect that trend to continue indefinitely.1 Modernizing your business intelligence (BI) initiatives is key to harnessing the opportunities present in this era

Department of Customer LoveLooker Staff

published in Blog Archives

What do data people do?

This content, written by Josh Siegel, was initially posted in Looker Blog on Mar 18, 2014. The content is subject to limited support.One of today's business tragedies is when a company's smart data people spend their time pulling data to answer other people's questions, rather than on research and data science. Often this is the result of a tools gap: Until recently, most business intelligence tools have focused on "reporting" rather than allowing users to explore the data on their own (a harder problem to solve). At worst, the data team can become a cost center on the defensive. Modern data teams do four things with the data they collect: Prepare it (data warehousing) Answer anticipated questions (reporting) Answer unanticipated questions (exploration) Ask their own questions (research) Each activity has its own experts, sub-industries, tools and methodologies. 1) Preparing data (data warehousing) A well-rounded technical person understands the architecture around relational databases

Department of Customer LoveLooker Staff

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Weather and climate data for business intelligence: Q&A with Weather Source

This content, written by Shohei Narron, was initially posted in Looker Blog on May 5, 2020. The content is subject to limited support.We recently had the opportunity to speak with Mark Gibbas, CEO of Weather Source, and learn more about how they provide weather and climate data solutions for a growing list of companies across a variety of industries. Plus, as a member of the Looker Partner Network, Weather Source now offers their as one of the newest additions to our . Hi, Mark! Could you share with us a little bit about yourself and your company? Sure! I’m a meteorologist who also has an extensive background in computer science and mathematics. This led me to found in 2015, with the mission to make hyper-local weather and climate data accessible around the globe and across industries. Weather Source helps companies quantify the impact of weather on their operations, reduce waste, increase return on investment, fine-tune logistics, deploy strategic marketing, improve resource plannin

Department of Customer LoveLooker Staff

published in Blog Archives

Looker and Vertica - geospatial analytics

This content, written by Erin Franz, was initially posted in Looker Blog on Dec 22, 2015. The content is subject to limited support.Vertica offers a suite of geospatial analytics functions with its HP Vertica Place package. This package enables storing and querying of geometric objects - translating locations into geometric points and regions into polygons. Common asks for measuring distance, length, area can be completed directly in Vertica and these requests are optimized at scale using spatial indexes and the underlying power of Vertica’s database. Because Looker performs all analytical workloads in HP Vertica, Looker can utilize these geospatial analytic functions to enable exploration and visualization of results dynamically. To demonstrate some of what’s possible, we’ll start with a table of bookings in Vertica that have associated latitude and longitude locations. When we query for a sample of the table, called locations, we see the following: select * from place.locations limi

Department of Customer LoveLooker Staff

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Looker and Vertica: flex table integration

This content, written by Erin Franz, was initially posted in Looker Blog on Oct 12, 2015. The content is subject to limited support.HP Vertica’s Flex Zone allows semi-structured data to be quickly loaded and analyzed using Vertica, without having to create schemas up front. Because Looker connects directly to Vertica to provide data exploration and visualization capabilities, Looker can take advantage of the the underlying power of Vertica as well as its analytic features such as Flex Zone. In this tutorial we’ll build out an exploration framework for a JSON Flex Table in Vertica using Looker’s LookML modeling layer. Twitter data is a great example for this since it is commonly obtained in JSON format and analysis can provide valuable insights into social presence and sentiment. Utilizing Vertica’s JSON parser, Flex Table integration, and the LookML modeling layer, we’ll examine how to quickly and easily get valuable insights from Twitter data. In order to make the data from the raw J

Department of Customer LoveLooker Staff

published in Blog Archives

Using Snowflake MVs in Looker

This content, written by Bruce Sandell, was initially posted in Looker Blog on Oct 19, 2019. The content is subject to limited support.Snowflake recently announced the general availability of materialized views (MVs). If you aren’t familiar with MVs, they are physical database objects that contain the result of a query, so they lie somewhere between a table, which is a physical object, and a view, which is based on a query but is a logical object. MVs have many uses, including enhancing query performance, encapsulating complex logic, and aggregating data. Enhancing query performance Say you frequently report on a customer’s month-to-date spend and you have a table that contains line-item information about a customer’s purchase history. Using MVs, you could create a view that rolls the purchases up by customer for the current month. And since that aggregated data would always be as current as the data in the base table, the result would be highly performant queries when accessing data o

Department of Customer LoveLooker Staff

published in Blog Archives

Marketing conversion rates: clarity, trust, and the feedback loop

This content, written by Jonathon Knapp, was initially posted in Looker Blog on Jun 15, 2017. The content is subject to limited support.Test, measure, iterate. The dialogue is similar with most SDR departments across tech: book more meetings, make more calls, adopt new approaches. There are so many different types of strategies, and while each approach has its value, I think we can all agree that these approaches are meaningless if you can’t track the effectiveness of the approach. At Looker, our SDR team takes a multifaceted data-driven approach to everything we do. Below, I’m sharing the most valuable metrics and ‘Looks’ (aka: dashboards, if you aren’t a Looker-user) that our team uses to achieve maximum efficiency. Clarity Our SDR team understands the value of Looker to our process. We rely the conversion data on our Looker dashboards to influence our team’s daily workflow. From monitoring the pipeline to something as simple as tracking phone calls made, we’re always exploring data

Department of Customer LoveLooker Staff

published in Blog Archives

Using innovative analytics to measure our radio advertising campaign

This content, written by Stewart Duncan, was initially posted in Looker Blog on Oct 1, 2015. The content is subject to limited support.It is considerably more difficult to measure the impact of offline marketing campaigns than online digital ones. Here we describe how we use some simple maths, some clever SQL, our open source analytics system and our data discovery tool to measure the success of regionalised radio ads and to help us decide where best to spend our money next. The objective - testing the airwaves Here at , we are working hard to make easier to understand and simpler to buy. We are keenly focused on the small business sector in the UK, which represents an incredibly diverse community of some 5 millions SMEs, each with their own individual needs. We have great success communicating with this community through digital channels like online search, display advertising and social, but in recent months we have been testing regionalised radio advertising to open up a more tra

Department of Customer LoveLooker Staff

published in Blog Archives

Using data to transform the customer experience with Looker and Braze

This content, written by The Looker Team, was initially posted in Looker Blog on Aug 29, 2020. The content is subject to limited support.When real-time data is made available across an organization, everyone becomes empowered to improve the customer experience. With dashboards and visualizations to track consumer and product metrics, teams can gauge how products are performing and build alerts to flag changes in behavior or customer spend. These insights allow consumer-focused teams to better understand their “always on” shopper and create new opportunities for serving information to them in ways that can positively affect their buying decisions. With the ability to use data this way, improving the customer experience can start to include more data-driven activities such as: Connecting with consumers where they want, when they want Collecting customer experience data faster from notifications, email, apps, and websites to inform new decisions and strategies Feeding data back to teams i

Department of Customer LoveLooker Staff

published in Blog Archives

Using Amazon Redshift Materialized Views with Looker

This content, written by Bruce Sandell, was initially posted in Looker Blog on Jul 2, 2020. The content is subject to limited support.Materialized Views (MVs) allow data analysts to store the results of a query as though it were a physical table. Amazon Redshift recently , providing a useful and valuable tool for data analysts, because they allow analysts to compute complex metrics at query time with data that has already been aggregated, which can drastically improve query performance. Historically in Redshift, users have been able to store the results of a query to a physical table using the CREATE TABLE AS SELECT (CTAS) syntax. While this still allows users to query pre-aggregated data, MVs have an important benefit over CTAS tables. MVs can be updated incrementally using the REFRESH MATERIALIZED VIEW command, whereas a table created using CTAS has to be completely recreated each time. And because tables created using CTAS can include joins across large tables and complex aggregatio

Department of Customer LoveLooker Staff

published in Blog Archives

Uplevel your customer data analytics with RudderStack and Looker

This content, written by Soumyadeb Mitra, was initially posted in Looker Blog on Sep 29, 2020. The content is subject to limited support.Offering a unique product or service experience is critical to building a strong customer base. To do this, you need a customer data model that can provide you with a detailed understanding of who your customers are. Creating such a model can be a difficult task, though. Despite the numerous tools on the market today, many companies find it challenging to address how to best bring all of their customer engagement data together. Standard analytics tools can give you an aggregate view of interactive data like site views and clicks, but true business insights come from user-level customer data across their entire journey. To help organizations overcome this challenge and derive better insights from their customer data, teamed up with Looker to build the . With this Looker Block, you can leverage customer event data persisted in your warehouse to perform

Department of Customer LoveLooker Staff

published in Blog Archives

Unleashing data insights to your entire organisation: Q&A with Appsflyer

This content, written by Áine Dundas, was initially posted in Looker Blog on Feb 24, 2020. The content is subject to limited support.In this blog, we talk with Alexandra Sudilovski, Head of BI at Appsflyer. Appsflyer is a rapidly growing SaaS company, and we wanted to learn more about how access to data has helped to fuel that growth. Hi Alexandra! Could you share a little about your background in data and your current role in Appsflyer? I am a BI expert at — a mobile attribution and marketing analytics platform that helps application marketing teams around the world make better decisions. We enable marketers to measure everything from impressions and clicks, to installs, purchase activity, ad revenue, and so much more. One of our main responsibilities as a BI team is to provide data and make it relevant, available and accessible to all our internal departments. That’s no mean feat! I’ve been working with Looker for about two years already, and I’m focused on really integrating analyt

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