<![CDATA[Looker | Blog]]> https://looker.com/blog Looker blog discover.looker.com Copyright 2018 2018-07-17T04:58:08-07:00 <![CDATA[Women of Data: Cara Baestlein, Data Scientist at Snowplow]]> https://looker.com/blog/women-of-data-cara-baestlein-snowplow https://looker.com/blog/women-of-data-cara-baestlein-snowplow Cara Baestlein is a Data Scientist at London-based Snowplow Analytics. Before getting her MSc in Economics from University College London, Cara received her undergraduate degree from Edinburgh University, where she realized her interest in Data Science while spending a year abroad at Columbia University.

After university, Cara worked in finance, consulting, and startups, gaining the experience she now brings to her work helping companies of all sizes design and implement the data collection they need to build data-driven cultures at their organization.

Hi, Cara! Can you tell us a bit about your background and how it lead you to get into a career in data?

I studied economics at university, first in Edinburgh and then at UCL. During an exchange year at Columbia University I took an introductory course in Computer Science, and that really awakened my interest in applying all the statistical techniques I was learning in Econometrics classes to real world data. For my Masters thesis at UCL I applied Vector Autoregressive models to estimate the effect of Quantitative Easing during the recent financial crisis. And after graduating, I participated in research project using machine learning to determine the sentiment in the German population towards refugees based on Twitter feeds. What I enjoyed most was putting the economic theories I was learning to the test.

But while in the economic setting you mostly have to rely on data sets prepared by others, with large lags and imperfect assumptions, in the digital analytics world data can easily be collected in real time. As part of Snowplow’s Professional Services team, I get to help clients around the world not only design and implement what data they want to collect, but also to use the data most effectively to answer the questions that make the difference to their business.

What advice would you give to other women who are interested in pursuing a similar career path to yours?

Find out what you are really passionate about, what you would be happy spending every day doing, as opposed to a job that you like the idea of more than the actual day-to-day work. And keep an open mind about what your ideal job might look like, whether it be what industry you will work in, what location, or what size of company. You might surprise yourself and end up enjoying a job you never set out to get.

What has been the biggest surprise in your career?

A real surprise to me was that business culture seems to be a bigger blocker in using data effectively than a lack of sophisticated tools to do so. The technologies available today really enable even the smallest of companies to use data effectively to drive their decision making. Yet a lack of understanding and trust in these technologies often means they are not adopted as widely as you would expect.

“Especially at the beginning of a career I think it is really important to feel like you are growing through your work. It can be scary always reaching slightly further than what you feel comfortable with, but in my experience that simply is the fastest way to learn.”
What are some of the biggest challenges in leading today?

Technology is evolving rapidly, and while this provides wonderful new opportunities for growth, it also presents new challenges. Leadership needs keep up with the progress of technology, adapting and responding to new ways of working and doing business to continually be able to attract happy customers and highly skilled and enthusiastic employees.

What can women do today to help build the foundation for successful careers?

I think it can be tremendously valuable to continually identify new areas you are curious about and invest some time into learning new skills. Especially at the beginning of a career I think it is really important to feel like you are growing through your work. It can be scary always reaching slightly further than what you feel comfortable with, but in my experience that simply is the fastest way to learn.

Do you think that data can help build a more diverse and equal workplace?

Definitely, I can think of two reasons in particular: firstly, data allows companies to evaluate strategies and performance more objectively, free from human biases or stereotypes. Secondly, because the field of data analytics is a relatively new industry, particularly with regards to digital data, the expected persona and career path of a great data scientist haven’t been solidified yet. And so it’s really more about your passion and aptitude for the job as opposed what university you went to or where you worked before.

How do you think individuals can use data to advance their ideas or careers?

I see data and the insights derived from it as another tool under our belt to help us make better decisions. It allows us to understand the problems we face agnostically, and tackle them more efficiently. I feel like it brings this whole new dimension to the way we work today: we can test and evaluate our strategies, practices and assumptions in a way that was previously only accessible to scientists, running experiments in labs. We can now run experiments in the real world, whether its A/B testing new website features or evaluating the performance of an algorithm recommending products to customers in real time.

<![CDATA[Announcing the Looker User Guide: Your resource for getting the most out of Looker]]> https://looker.com/blog/announcing-the-looker-user-guide https://looker.com/blog/announcing-the-looker-user-guide Whether you are brand new to Looker, or an expert in LookML, Looker now has a single destination to find all of the content, training and resources you need...the Looker User Guide!

Here is a breakdown of everything you have access to.

User Guide Homepage

If you are new to Looker, start here.

The User Guide homepage is broken into three goal based pathways. Collectively, they will guide you through all you need to know to get started with Looker.

The Pathways

View: Learn how to use Looker to view dashboards, reports and more.

Build: Learn how to pivot and filter data, create custom visualizations, and share dashboards with your stakeholders.

Develop: Learn how to connect to your database and get started writing LookML, our SQL-derived development language.


Once you’ve selected your pathway, you're free to browse through Looker's curated content and learn more about the aspects of Looker that interest you most.


Help Center

This is where you’ll find answers to the most commonly asked Looker questions. Our Help Center is full of troubleshooting advice, how-to guides and assorted best practices. Type in your question for thorough answers courtesy of Looker's team of service experts: The Department of Customer Love.



Your one-stop authoritative reference for using Looker and understanding the technical details.

Closely aligned with releases and product updates, you’ll find articles, in-depth video tutorials and useful information on everything from administration functions, writing LookML and organizing content.


User Forums

Looker User Forums are your direct connection to the ever-growing Looker community of data lovers. Always an excellent resource for users of all skill levels, join conversations with other users and ask questions or share your learnings. A User Forum is a great place to learn with your peers and help others solve data problems.



Our new online learning platform is the place to go for accessing live and self-paced training courses. Register for any of the free eLearning courses for on-demand videos and self-service learning. Or, if you prefer, join one of our weekly live webinars or in-person classroom trainings.


Not sure where to look? Type your question into the User Guide search box and see where you can find applicable content across resources.


Go ahead, dive in.

Find what you need or learn something new with the Looker User Guide.

If you have thoughts on ways we can improve this experience, don’t hesitate to reach out. Email me directly at Shanann.Monaghan@looker.com and we can chat.

<![CDATA[Women of Data: Liz Hartmann, Data Analytics Lead at Segment]]> https://looker.com/blog/women-of-data-liz-hartmann-segment https://looker.com/blog/women-of-data-liz-hartmann-segment Liz Hartmann is the Data Analytics Lead at Segment. She was born and raised on the East Coast but has been living in California since graduating from Cornell University.

Liz previously worked at Dropbox, Freeman, Sullivan & Company (acquired by Nexant) and Acumen, LLC. When she isn’t nerding out over data, she volunteers at the local YMCA teaching a group fitness class.

Hi Liz! Can you share a bit about your background? How did it lead you to a career in data?

I was a bit of a math nerd growing up but I studied public policy in college. I was interested in working in non-profit business, but in the end, I couldn’t resist the pull of data analytics. It was actually in the process of writing my honors thesis (exploring factors that affected the progression of relationships among low-income couples) that I figured out how much I liked working with data. I used Stata (a statistical programming software) to clean up, analyze and summarize the data I needed for my thesis. Once I graduated, I decided to dive into data analysis as a career and I’ve never looked back.

What advice would you give to other women who are interested in pursuing a similar career path to yours?

Go for it! Data is only becoming more and more abundant in the age of all things tech, and it’s not going to analyze itself!

What has been the biggest surprise in your career?

Honestly, it’s been getting into tech! I spent the first four years of my career working in consulting – one year analyzing Medicare and Medicaid data for the federal government and three years analyzing residential and commercial electricity usage data for utility companies like PG&E. I learned a ton about all facets of data analysis in both of those jobs and, by switching industries, I also realized that the skills I had acquired were useful across many different types of data.

When I was ready for a change, I just happened upon the listing to start up HR analytics at Dropbox. Focusing on internal employee data seemed like a great role for me given my experience in data analysis and my undergraduate background studying social policy and demographics. Luckily for me, Dropbox agreed, and I’ve been in analytics for tech companies ever since.

If you had told me when I graduated with a degree in public policy that one day I would be working for Silicon Valley tech companies, I would have laughed at you - but here I am! I think it's a good reminder that your degree doesn’t necessarily have to dictate your career for the rest of your life.

What advice would you give women about building the foundation for a successful career?

Speak up for yourself. There is nothing wrong with or arrogant about advocating for yourself, and I think most women (and men!) should be doing it more.

Don’t forget to take care of yourself. Yes, it might seem like working 100-hour weeks will pay off in the long run, but that isn’t sustainable for anyone and you will eventually burn out. You can work hard but still take time to recharge.

What are some of the biggest challenges in leading today and how are you thinking about dealing with those challenges?

As someone who is fairly new to leading at a startup, I would say one of my biggest challenges is learning to let go and delegate things that I used to do myself. I think Molly Graham (most recently the VP of Operations at the Chan Zuckerberg Initiative) put it best when she talked about needing to “give away your Legos.” Instinctively, you may want to hold on to the all the balls you’ve been keeping in the air as your company grows, but in order to make a bigger impact and zoom out a little bit, you need to share your Legos.

Do you think that data can help build a more diverse and equal workplace?

Yes, I do! A great example is workplace diversity statistics. In recent years a growing number of public and private companies have been releasing diversity stats about their workforces. I think this transparency is amazing because it holds companies accountable, not just to their employees or their stakeholders but to the greater public. Segment hasn’t released our data yet because we are a bit small for that, but we track diversity data internally and use it to inform decisions around hiring initiatives.

How do you think individuals can use data to advance their ideas or careers?

As the mousepad I’m using at this exact moment says, “Without data, you’re just another person with an opinion.” Regardless of your team or role in a company, you can use data to better understand your baseline, set quantitative goals and work towards those goals. Along the way you can keep tabs on the important metrics so you can keep doing the things that are helping and iterate and change the things that aren’t.

<![CDATA[Why centralized data access is key for your organization becoming 'GDPR ready']]> https://looker.com/blog/why-centralized-data-access-is-key-for-gdpr https://looker.com/blog/why-centralized-data-access-is-key-for-gdpr 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 disconnected from the central source, people begin to rely on the types of decentralized storage “systems” mentioned above. “Oh, I have that list of email addresses on my laptop.” They’re then left with disparate data ‘swamps’ that are impossible to search and even harder to manage and protect.

From the perspective of IT, it’s one thing to control one highly guarded fortress. It’s another challenge entirely when you don’t know how many fortresses exist, what data is inside, how it’s used, you have no record of how many keys have been copied and you don’t know who has access to them. This is the challenge Chief Privacy and Data Protection Officers are being presented with. It’s a problem we need to tackle as an industry – or many will fall victim to GDPR and its potentially severe punishments, or a loss of customer trust.

This is an issue that requires a long-term solution – and cannot be solved by a one-time, CIO-led data swamp cleanup. Because if the data analysis tools encourage “data sprawl” -- extracting data and moving it to ‘data workbooks’ for analysis -- the problem will reoccur. So even after CIOs and IT teams have transformed their data swamps into clean and organized data lakes, their analysis tools go and start the problem all over again – creating a never-ending spiral of pain.

Why you need a single access point for your data

That’s why any long-term solution has to address the root of the problem. Businesses need a single access point for their data. They need to see who has accessed it and what they’ve done, all in one centralized, managed, secure place.

Introducing this kind of system immediately cuts down the number of steps required to start examining data and delving into whether it’s actually useful or not. Analytics can happen faster, and without encouraging data sprawl. Additionally, platforms leverage the world-class security of today’s most advanced databases, giving administrators control over and insight into who’s accessing data and how long it’s cached for.

The role of centralization

Looker is a centralized flexible data platform that leaves your data in your database. This means that employees no longer need to extract the data to analyze it. They can interpret it and act on it directly, accessing only the data they need to answer their immediate questions, while still retaining the ability to ask more.

This means the development of a long-term data governance and analysis strategy, in which analysts can still provide their organization with game-changing business insights - while maintaining compliance with regulation - becomes possible. An easier process. Cleaner data. And GDPR ready. That’s the modern approach to analytics your data-led business should consider.

<![CDATA[I can see your Halo: Player Behavior and Map Metrics in Halo 5: Guardians]]> https://looker.com/blog/halo-5-player-behavior-map-analysis https://looker.com/blog/halo-5-player-behavior-map-analysis I’ve been playing Halo for a really, really long time.

I started with Halo: Combat Evolved, then Halo 2, but things really clicked when I started playing Halo 3 online. I never reached Rank 50, but I’ve spent a lot of time driving Warthogs off cliffs, getting stuck by plasma grenades, and playing games of Big Team Battle way past midnight.

As a new member of Looker’s Department of Customer Love, I was given the opportunity to build a data model from scratch off a dataset of my choosing. When I found out that 343 Industries had a public API for Halo 5 multiplayer data, it really didn’t take long for me to decide that this needed to be my dataset, no question.

Like a lot of competitive multiplayer games out there, Halo 5 is chock full of player performance metrics and statistics, and by digging into this dataset, we can draw a lot of cool insights regarding how players play Halo 5 and what makes some players really good (and others not so much).

The Data

My dataset contains information on 1,068 unique players and 28,565 recorded matches, and is mostly comprised of matches from the Fall-Winter 2017 Season. In regards to my ETL process, I used a Ruby script to generate my list of gamertags and create my player table. Using those gamertags, I then pulled the 25 most recent matches for all my players to create my match table. Additional metadata on weapons, maps and gametypes were all stored as their own individual tables, as well.

The following analysis is not an end-all-be-all conclusions surrounding competitive Halo, but rather, an attempt to paint some very broad (but interesting) strokes about Halo 5 multiplayer data based on the sample data I was able to extract.

So without further ado, let’s dive into the data!

Weapon Usage in Slayer


Among all my ranked Slayer* players, by far the most used weapon is the Magnum. Based on my dataset, 100% of players at the Diamond, Onyx and Champion rank classes have more kills racked up with their Magnums compared to any other weapon. Note that as you go up each rank class, the percentage of players who primarily use the Assault Rifle or Melee/Spartan gets smaller and smaller.

While it’s no surprise that the Magnum is by far the most popular weapon among high ranking Halo players, it’s pretty crazy to see Magnum usage dominate to this extent. Bunching Magnum, Assault Rifle, and Melee/Spartan users up, let’s see how these groups stack up to each other using a variety of player metrics.


Looking at average win percentage (i.e. total matches won divided by total matches played), we can see that Magnum tend to lose a whole lot of their matches at the Bronze rank. As we climb the ladder, however, we see win percentages for Magnum users dwarf both our Assault Rifle and Melee/Spartan users.


We see very similar trend lines when comparing the average overall accuracy of each weapon group. Bronze-ranked Magnum users tend to struggle with landing shots, but at higher ranks of play, Magnum users prove to be the most accurate compared to their counterparts.


And in looking at average Kill Death ratios (i.e. total number of kills divided by total number of deaths), Magnum users again outclass the opposition. Note that player rank class and average Kill Death ratio seem to have an almost linear correlation in this case.

All in all, it’s apparent that Bronze-ranked Magnum users tend to struggle, but Magnum users at every other rank hit their targets more often, get more kills per death, and win matches more often than the competition. What this ultimately means is that while there may be a place for switching to your Assault Rifle or using your melee button, getting really good with the Magnum pistol and landing clean shots may be your best bet for climbing the ladder!

Map Analysis


Halo 5 is no slouch when it comes to map variety. Taking a look at average Kill Death ratios across different maps, you’ll find some pretty interesting facts about how players of varying ranks perform on various map.

One observation here is that our Champion-ranked players show some absolutely dominant numbers on a handful of maps. Looking at the data, Champions tend to have their best matches on Stasis, Coliseum, Plaza and The Rig, averaging a Kill-Death ratio of 1.90 or better! This stands in stark contrast to how tough these maps seem to be for Bronze-ranked players, averaging Kill Death ratios right around 0.73 or less.

While it might be a stretch, personal experience tells me that these maps see a lot of long range skirmishes (note that three out of the four maps I mentioned have Sniper Rifle pick-up locations on them).

Ultimately, players who can advantage of the larger map sizes with well placed shots and map control over power weapon spawns can really take over in maps like these.

Diving into the nitty-gritty, let’s take a deeper dive into one of Halo’s most iconic maps, and one of my personal favorites: Truth (aka Midship, for any Halo 2 players out there).


Taking a look at average Slayer match duration by map, Truth tends to have some of the shortest matches among all of Halo 5’s multiplayers maps. Considering that you’re pretty much within eyesight of the opposing team seconds after spawning in, it makes sense that games tend to go shorter on Truth in particular.


Truth also tends to see an above-average percentage of players leaving Slayer matches mid-game. 8.41% of my recorded Slayer matches saw at least one player drop midway through the game, and while that’s not as high as the drop rates on Stasis and Empire (11.31% and 9.77%, respectively), it’s significantly higher than the numbers on Slag, Riptide and Eden where percentage of mid-game exits hover around 5%.


As a closing note, the average difference in team scores at the end of a Slayer match is larger on Truth than on any other map. In other words, winning teams tend to win Slayer matches by higher margins on Truth than every other map on average. However, based on my sample data, most Slayer matches tend to see winning teams about 11 to 12 kills ahead of losing teams by the end of a match, meaning most games don’t end up being particularly close, regardless of the map you’re on.

Closing Thoughts

343 Industries has done a good job of making this all very accessible, and I definitely recommend checking the Halo 5 API out if you’re at all curious!

Thanks for taking the time to dive into the data with me, and I hope you enjoyed it. And to all you Halo players out there, “Good luck, and have fun!”

* Slayer - One of the hallmark modes of Halo multiplayer. Two teams compete against each other by killing opposing players. The first team to 50 kills wins.

<![CDATA[Businesses create more impact with more efficient data science workflows]]> https://looker.com/blog/create-more-impact-with-efficient-data-science-workflows https://looker.com/blog/create-more-impact-with-efficient-data-science-workflows Looker is all about helping businesses get more value through the smarter use of data. In a lot of cases, that’s about putting the right data in the right decision-maker’s hands at the right time.

But in addition to business users who need access to actionable intel, many companies have invested in data scientists who use cutting edge algorithms to extract meaning from large amounts of data. And these people need access to the right data, too.

You’d think they’d have a stable full of amazing tools for their work, but the reality is that while the tools for building machine learning models are fantastic (and always getting better), the tools for the other parts of a data scientist’s work -- preparing the data and then operationalizing their findings -- are typically lacking.

So that’s why we’re announcing a slew of new capabilities in Looker to make those parts of a data scientist’s job--data prep, data wrangling, data cleansing, data visualization, data presentation, and data action--easier.

If you’re a data scientist, this should make you happy. After all, wouldn’t you rather spend your time doing the interesting work of building and tuning models rather than recleaning the same data set?

And if you’re somebody who employs data scientists, this should make you really happy. Would you rather your data scientists’ (expensive) hours go to doing data custodial work? Or would you rather they be able to access clean, correct data from across your organization quickly and easily?

Data scientists at places like Avant and Stack Overflow are already getting huge value from Looker, as their stories below illustrate.

Helping developers go further

Julia Silge is the lead data scientist at Stack Overflow, the most popular developer knowledge-share in the world. The platform hosts 50 million visitors a month, and these visitors ask a question on the platform every 12 seconds. It’s Julia’s responsibility, as lead Data Scientist, to surface insights from this massive amount of information and help her team to better prioritize where to spend their energy.

Concretely, one challenge Stack Overflow faced was building a sustainable Talent Solutions business on top of their “free” community platform. “We needed to understand what makes clients more likely to have a good experience, come back, and spend with us again,” she explained. To do this, Julia had to gather and stitch together multiple types of data from various sources, test out machine learning statistical models on that dataset, and put the predictive model output in relevant context for her stakeholders to better make their business decisions.

The work was hugely impactful: Stack Overflow nearly doubled their renewal rate in only one year. And Looker’s scalable and efficient architecture played a key role by helping Julia shrink the most time-consuming and manual part of her job from many hours of work down to minutes, allowing her to focus more on solving high-value data science problems.

Enabling opportunity through better access to capital

Avant is constantly striving to better match its customers with the best offering. And as a company that has democratized analytics for every employee, Avant has become one of our go-to examples for what an instrumented workplace and universal data access can achieve.

But the next step in their data culture’s evolution was to democratize access to advanced analytic techniques. To do this, they introduced the machine-learning-as-a-service rocketship known as DataRobot. And the combination of Looker and DataRobot has driven tangible and exciting outcomes for Avant and their data culture:

Our advanced analytics workflows are at least 5x more efficient with Looker. Before, it would take a data scientist a whole week to get the answer they need... preparing the data, feeding it into the predictive model, and outputting the results for stakeholders. Now, thanks to Looker, that process takes less than a day. When we combine Looker with DataRobot, our analysts can easily run 10 models and choose the winner all in one day and quickly move on to the next problem.
~ Charles W, VP Product, Avant

New capabilities to make data science more efficient and valuable

One of the biggest goals of our new tools aimed at data scientists is making sure that it’s dead simple for them to integrate Looker into their existing workflows. That’s why we’re focused on integrating into their existing best-of-breed data science tools with some exciting new technology partnerships. Our TensorFlow integration allows data scientist professionals to more easily take advantage of Google-scale machine learning on their governed and reliable Looker datasets. For companies that need more support, our partnership with Big Squid helps the “citizen data scientist” run their Looker data through the sophisticated analytical techniques of a pro, in only a few clicks.

We’re also rolling out four new capabilities that help data teams augment Looker analytics with artificial intelligence and machine learning on large datasets: Streaming Results, an R SDK , Python Connections and Merged Results. Now, you can more seamlessly send your entire set of data directly to your data science tool of choice (e.g. RStudio, Jupyter Notebooks, etc.) for advanced statistical analysis. Then, as you iterate and optimize your statistical model, you can operationalize outputs in relevant, actionable context alongside other business metrics, federating results from any connected database all in the front-end of Looker.

With these developments, both professional and citizen data scientists can focus their time on higher value thinking for greater impact.

As organizations build out and evolve their use of data, Looker is focused on helping more people make more impact in more ways. Efficient, high value data workflows mean tighter feedback loops for testing, learning and iterating on models, accelerating the value organizations derive. Data cultures like those at Stack Overflow and Avant are at the leading edge, and we’re excited to see more customers take full advantage of Looker in their data science workflows going forward.

<![CDATA[The Marketer’s Guide to Building a Reporting Stack that Drives Growth]]> https://looker.com/blog/building-a-reporting-stack-that-drives-growth https://looker.com/blog/building-a-reporting-stack-that-drives-growth Many companies are unsure about the exact effect of their marketing.

As a result, they view marketing more as a cost center than a revenue center and tend to underinvest. This perspective restricts growth and makes them vulnerable to more aggressive and data driven competitors.

At Funnel, we work with thousands of marketers worldwide. Lately, we have started observing a new trend: marketers are increasingly building a sophisticated reporting stack to replace siloed reporting tools and spreadsheets.

This trend started in 2017 with larger organizations. But now we are seeing companies of all sizes invest in a much more sophisticated marketing stack.

There are a number of drivers behind this:

  • Marketing is increasingly becoming measurable with 50% of all marketing being digital by 2021 as reported by eMarketer. If you measure correctly, you can make much better decisions, acquire more customers, and drive significant growth in your business. However, you need to have the right reporting tools in place to do this. Smart marketers have come to this realization and are taking action.

  • Marketers are increasingly focused on having a good marketing setup, which includes things like tagging campaigns and using third party analytics tools as the source of truth for advertising performance.

  • Automation of data import and mapping has been made much easier with new breeds of companies providing it as a service or offering greatly simplified tools for building data imports.

  • Data warehouse technology is increasingly becoming available to a mass market. These products were typically sold as expensive software products to big enterprises. Now they are readily available as cloud based pay-as-you-go solutions from Google and Amazon.

  • General purpose visualization tools are becoming available to a mass market. Over the past couple of years, business intelligence companies have moved to cloud offerings with per seat pricing at a more accessible price point.

What does this look like in practice?


The three layers of the modern market reporting stack

Data import platform

Getting the data into the data warehouse has been the most challenging part of building the marketing reporting stack, as historically the process has been extremely manual and time consuming.

Most marketing data lives in third party advertising and marketing platforms. Getting the data in into a data warehouse involves integrating with and maintaining a large number of APIs.

Different types of solutions have emerged to solve this problem. Sometimes one tool cansolve the entire problem for you and sometimes a combination of tools are necessary to get all the data needed for analysis into a data warehouse. Broadly speaking, there are two types of tools that have emerged:

  • “Data as a service” platforms focused on marketing data: These platforms pull in data for all or most of you marketing data. Because they only operate in the marketing domain, these tools have the domain data expertise and provide those data extracts in the most consumable way. Funnel is one example of such a platform.

  • General purpose ETL (Extract Transform Load) tools: ETL tools have evolved a lot over the past couple of years. They now provide ready integrations to a number of the larger platforms. They are relatively easy to use, but they often require technical knowledge and responsibility for maintaining connections to the platform.

It is important to note that not all connections are equal. Just because a tool has a connection to a platform you need does not mean that you can get exactly the data you need or that the data is accurate. Marketing platforms change often and integrations with these tools need to stay up to date with those changes.

Data warehouse

A data warehouse is a highly scalable data repository used for reporting and data analysis.The market for data warehouses has traditionally been dominated by companies like Oracle, IBM and SAP selling large installations to enterprises. However, new cloud based offerings from Google and Amazon have made data warehouses available to the mass market with self service offerings and affordable pay-as-you-go pricing.

What’s more, your organization might already have a data warehouse where other data is stored. By collecting all of your company’s data in one place, you open up new possibilities for even broader understanding of your marketing efforts throughout the funnel.

Data Access & Visualization

The final piece is finding a tool to access and analyze all of this data. That’s where a data platform, like Looker, comes in.

What you can do with a reporting stack

With a robust marketing reporting stack in place, you now have the ability to not only automate much of your marketing reporting but also do new analysis you simply could not support with silos and spreadsheets.

Tracking overall marketing spend according to budget

Next time you meet a marketer, ask them (or ask yourself if you have a marketing budget) the following question: As of today, what percent of your marketing budget have you spent and is your overall spend tracking above or below budget?

More often than not, it’s hard to get a straight answer. The spend for marketing channels is typically tracked in different tools and siloed reports. However, with a consolidated marketing stack, all the data needed to visualize your overall marketing spend is collected automatically.

For example, the cumulative spend for last month's and this month's advertising spend can easily be visualized. The comparison forecasts where the spend for this month will likely end up.

A budget line can be added to show how you are tracking towards your budget and help stay in control of your advertising spend.

Customer acquisition reporting based on mapped data

In siloed reports, the performance of each advertising platform is reported on separately.

In order to get a full understanding of your marketing spend, you need to see cost data from the advertising platforms and transaction and revenue data in the same view.


Table with mapped customer acquisition data

With this view, you know exactly how each advertising platform is performing in an objective manner. If you want to you can look at the data based on marketing strategy such as display, search, retargeting, email, SEO instead. Or you can take a look at how each product category or vertical is performing.

Rollup of markets, websites, brands or customers

Most analysis is done for a specific advertising channel or website. But it is even more important to get the complete picture of your advertising rolled up across all advertising platforms, markets, websites, brands, customers and other entities that matter for your business.

As long as the data has been properly tagged and mapped, all this data can be easily available in the data warehouse for you to slice and dice in every way imaginable.


Great organizations use accurate marketing reporting to drive growth

With all the new technology developments, building a marketing reporting stack is easier than you think. It will help you measure the true impact of your marketing and allow you to invest with confidence which can drive substantial growth in your business.

If you want to try this, Funnel offers an easy to use, out-of-the-box data import platform for marketing which is pre integrated with Looker. You can be up and running pulling in data from your different marketing platforms and visualizing them in Looker in minutes.

Learn more about the Looker Funnel integration here and here.

<![CDATA[Women of Data: Oana Tararache, Data Team Tech Lead at Namely]]> https://looker.com/blog/women-of-data-oana-tararache-namely https://looker.com/blog/women-of-data-oana-tararache-namely Oana Tararache is the Data Team Tech Lead at Namely. She received her Master in Computer Science from Columbia University and is a mother to three young boys (including twins).

Hi Oana, thanks for joining us! Can you tell us a bit about your background and how it lead you to get into a career in data?

I come from a family of software engineers. Growing up hearing engineer-talk at the dinner table and seeing boxes of computer punch cards around the house, it wasn’t surprising that both my brother and I followed the same career path. I have been a software engineer for 16 years now.

My first job involved working with data (before it was cool), but as I moved along in my career I spent some time in application development. I joined Namely in October 2016, and I was assigned to the Data team for a project, thinking it would be temporary. In the end, this role felt like coming home, and now over a year later I am still deep in the data world and loving it.

What has been the biggest surprise in your career?

I can’t count the number of times I have been the only woman in a room full of engineers, but it still takes me by surprise sometimes. I am lucky enough that our current team is very diverse, and half of the engineers are women. But in my experience that typically hasn’t been the case.

Do you think that data can help increase diversity in tech?

Data has no prejudice. Companies can analyze the data to uncover inequalities or areas that lack in diversity, and work to correct them. Namely strives for that internally, and some of the most important work I do is to build software that helps HR professionals dig into their own workforce data.

“Imposter syndrome is a real thing, and it’s especially prevalent in women in technology... But whatever you do, fight it.”
What do you find are some of the biggest challenges in leading today?

One of the things I often struggle with is accepting that I will make mistakes sometimes. Even with all my best intentions, I will make bad decisions or give bad advice. Our company mission is accepting that we’re all human, and my team always gives me the opportunity recognize my mistakes and learn from them.

What advice would you give to someone who is starting out in their career?

Stay true to yourself and your values. Remember that you don’t need to act a certain part in order or fit into a preconceived mold to be successful. Spend time discovering and identifying your strengths and build on them.

What advice would you give to other women who are interested in pursuing a similar career path to yours?

In a field where you are the minority, it can be really challenging to recognize your value and take credit for your work. Imposter syndrome is a real thing, and it’s especially prevalent in women in technology. But whatever you do, fight it. Whether that means finding a mentor that can guide you when you’re feeling overwhelmed, or simply surrounding yourself with people that appreciate you, make sure you don’t let the fear of inadequacy win. Then, pay it forward and help someone else.

Do you think data can help support this foundation?

A successful career is based on accountability, and data is your best friend when it comes to planning or measuring the success of a project. Ask thoughtful questions, and the data will help you make well informed decisions. Data-driven thinking increases credibility and trust within a team.

<![CDATA[Looker + Workplace: A smarter, faster, and more powerful way to collaborate with fresh data]]> https://looker.com/blog/announcing-looker-for-workplace-by-facebook https://looker.com/blog/announcing-looker-for-workplace-by-facebook Looker and Workplace by Facebook share the belief that people can achieve more by working together. That’s why Looker is excited to announce our new integration with Workplace.

Where teamwork comes to play

Workplace is a communications platform built to transform how businesses and teams work together. By combining the familiar, reliable Facebook features you’ve come to know with Looker’s powerful data platform, we’re able to deliver a smarter, faster, and more powerful way to collaborate with fresh data.

Looker + Workplace makes it easy to:
  • Share an interesting insight and start a conversation
  • Send reports on a periodic basis to keep data at the top of mind for everyone in the organization
  • Set custom triggers to send alerts to teams directly in Workplace

It’s never been easier to get the answers you need to make informed decisions, share insights with your team, and build a truly data driven culture.

Connected by data

Introducing new data directly into a workflow allows teams to seamlessly apply real-time analytics to current tasks; ensuring that everyone has a voice and is working together from a single source of truth.

The Looker and Workplace integration allows users to send a snapshot of a Dashboard or visualization directly from Looker to a Workplace group, along with commentary. Users then have a place to discuss, share, and react to the information while keeping the context of the information itself.


Build new routines with automation

Want everyone to check out the weekend report on Monday morning? Schedule it to appear in the company group first thing.


Lowering the barrier to entry is the first step to changing behavior. Make data a regular part of the conversation by simply putting it there.

A Learn fast, act faster

Keeping a constant pulse on what’s happening in your business is extremely important but having to log into separate tools can slow this process down.

Instead, have this information come to you. Set up a custom trigger in Looker to send a report directly to Workplace.


By setting up an alert, you can be sure the right people will see the right information at the right time.

We’re excited to usher in this next wave of organizational communication. Learn more about Looker’s integration with Workplace here.

<![CDATA[Announcing Looker Support for Dremio]]> https://looker.com/blog/announcing-looker-support-for-dremio https://looker.com/blog/announcing-looker-support-for-dremio Looker is excited to announce our support for Dremio as a data source with Dremio 2.0, which was announced on Wednesday, April 25.

Dremio is a self-service data platform that enables high performance data analysis from data lakes, NoSQL sources, and relational databases for data analysts and data scientists. Using Dremio with Looker will enable users to accomplish some historically challenging use cases with ease.

Why We’re Excited

Ever since Dremio formally launched last year, our team was buzzing with how much they were looking forward to a Looker-Dremio joint solution. Dremio’s platform offers plenty to be excited about for the data analyst and data scientist community in general, but we’ll cover three points in this post that are particularly close to our hearts:

Direct and performant querying for NoSQL data sources

With Dremio it’s now possible to query common NoSQL data sources like MongoDB and Elasticsearch directly via SQL. And they’ve made queries even faster through its query acceleration architecture, based on Apache Arrow.

Ability to query across sources without physical consolidation

Dremio makes it possible to query across both NoSQL and relational data sources without physically consolidating them - allowing you to connect Looker to both types of data sources in a seamless and elegant way.

Designed with the data analyst and data scientist in mind

Dremio makes it possible to complete complex data workloads without having to involve engineering, so Analysts and Data Scientists can quickly and easily onboard new data sources and transformations to be analyzed and consumed by their users.

Contact or download Dremio to learn more about using Looker and Dremio today!

<![CDATA[Women of Data: Rachel Bradley-Haas, Sr Manager of Business Analytics & Data Engineering at Heroku]]> https://looker.com/blog/women-of-data-rachel-bradley-haas https://looker.com/blog/women-of-data-rachel-bradley-haas We were very excited this week to sit down with Rachel Bradley-Hass. Rachel is the Senior Manager of Business Analytics & Data Engineering at Heroku. Prior to joining Heroku, Rachel worked at Cisco after getting her B.S.E in Industrial and Operations Engineering at the University of Michigan.

Hi, Rachel! Can you tell us a bit about your background, and how it lead you to a career in data?

I studied Industrial and Operations Engineering at University of Michigan, which was a combination of supply chain, statistics, and business. While in school, I fell in love with how applying math, especially statistics, to processes to optimize systems and increase customer experience, profit, and efficiency. Initially, I was resistant to investing time on developing computer programming skills, but during an internship I realized that automated systems and scripts would allow me to scale my efforts to a magnitude I could have never imagined. I haven’t looked back since. My coding skills combined with my analytics skills have enabled me to support a wider range of customers, products, and systems all while continuing to work on passion projects.

What has been the biggest surprise in your career?

When you remove self-doubt from the equation, you can accomplish anything you set your mind to. Sometimes you just need to jump into the deep end and hope you can swim. Worst case scenario, your team will throw you some adorable floaties to help you get along until you get the hang of it. When you stop making excuses for why you can’t do something, the world is your oyster.

How do you think women can help build the foundation for successful careers?

Similar to what I mentioned above, it is so important to never doubt yourself. There will always be people that doubt you, don’t let yourself be one of them. Pushing yourself and putting yourself in challenging environments allows you to grow at a rate you didn’t think possible.

Do you think data can help support this foundation??

Data helps tell a story. Data supports arguments. Data is a universal language. When pushing for funding on a new project idea or asking for a raise/promotion, data allows you to tell a compelling story supported by facts that all individuals are able to interpret. Similar to what Cady Heron says in Mean Girls, I love math (and data) because it is “the same in every country.”

“When you remove self-doubt from the equation, you can accomplish anything you set your mind to.”
What advice would you give to other women who are interested in pursuing a similar career path to yours?

Don’t be afraid to get your hands dirty and never stop learning! There are so many paths that lead to the same destination and there is no “right” path. There were 3 major areas of development in my career that have gotten me to where I am; math/analytics, computer programming, and business acumen. Without each of those development areas, I wouldn’t be able to apply my skills and build creative solutions for real world problems.

What are some of the biggest challenges in leading today?

One of the biggest challenges in leading today is creating a healthy balanced team. In order to tackle difficult problems in a fast moving environment, it is critical that you have a diverse set of skills, mindsets, personalities, and passions. Similar to darwinism, the more diversity you have on a team the more likely you will be able to survive and solve whatever problem gets thrown at you. The reason I view this a a challenge is because surrounding yourself with individuals similar to you comes naturally. This can lead to redundancy and leave major gaps unseen. To address this challenge, it is important to take the time to recognize the value in attributes different from yours and how those differences can positively impact the dynamic on your team.

Do you think that data can help build this kind of diverse workplace?

I believe that data can definitely help build a more diverse and equal workplace. The most powerful part of data is it allows you to surface real information when making decisions rather than relying on assumptions. While I do not think that data is the solution to building this diverse and equal workplace, I do believe that it has the ability to ignite change. Just by showing simple workplace statistics, it causes individuals to think twice when faced with decisions that may impact diversity and equality.

<![CDATA[Looker Announces Support for Oracle’s New Autonomous Data Warehouse Cloud]]> https://looker.com/blog/looker-for-the-oracle-autonomous-data-warehouse-cloud https://looker.com/blog/looker-for-the-oracle-autonomous-data-warehouse-cloud Last week, Oracle announced the General Availability of Autonomous Data Warehouse Cloud (ADWC), which was introduced during the Keynote Speech at Oracle OpenWorld.

Looker has spent the past year working closely with Oracle to ensure that we’re able support ADWC at launch, and we are happy to report that Looker provides full support for ADWC as of our 5.10 release.

About Autonomous Data Warehouse Cloud

oracle Fill in six fields and in less than a minute you’ll have a production-ready, fully managed database.

ADWC has all of the key features that you’d expect in a modern data warehouse: it’s simple to provision, it’s fast, and it’s fully elastic for both compute and storage: you can scale your storage and compute up or down instantly, with no down time.

ADWC also provides some advanced features that set it apart. The database is fully autonomous, meaning that is self-managing. DBA’s don’t need to worry about performance optimization techniques like indexes, partitions and materialized views. ADWC uses sophisticated machine learning algorithms to optimize data storage and retrieval based on data volume and usage patterns. An administrator need only specify the required service level for the database and ADWC takes it from there, guaranteeing up to 99.995% reliability.

In addition, ADWC is built on the Exadata platform, Oracle’s highest performance engineered systems, and the performance benefits are obvious when tearing through multi-billion row data sets. It also takes advantage of the new Oracle 18c Database, which includes several significant performance improvements, particularly when it comes to analytical workloads, and there are a number of new Advanced Analytic functions as well.

Benefits for Looker Users

ADWC is fully managed, so Data Analysts can spend more time bringing value to the business and less time worrying about how queries are written, how data is organized, or if their infrastructure is properly sized for their workload.

Looker users will also be able to incorporate all of the new analytic functionality provided by 18c into their analysis and can take advantage of Oracle’s Advanced Analytical functions to provide new insights.

And of course, everyone wants to analyze more data and get their results faster, which is where ADWC really shines, providing a great experience for Looker users. Because Looker connects directly to ADWC, users can take full advantage of its impressive performance. Would you like a dashboard that includes the number of unique visitors to your website over the last week from millions of web events? Oracle’s approx_count_distinct function can provide that information so quickly that it will not impact dashboard rendering times.

We’re very excited to be partnering with Oracle on this release and preliminary feedback has been fantastic! We can’t wait to see the amazing things that our customers can do by combining Looker with the speed, performance, reliability and simplicity of Oracle’s new Autonomous Data Warehouse Cloud.

Learn more about how to get started with Oracle’s ADWC here.

<![CDATA[Women of Data: Mico Yuk, Co-Founder of BI Brainz and the Analytics on Fire Podcast]]> https://looker.com/blog/women-of-data-mico-yuk https://looker.com/blog/women-of-data-mico-yuk This week, we were very excited to learn from speaker, author, and data visualization expert, Mico Yuk. An active proponent of humanizing analytics, Mico is the Co-Founder of BI Brianz and co-host of the Analytics on Fire Podcast.

Mico, can you tell us a bit about your background and how it lead to a career in data?

At age 12 I was fascinated with the two things: Computers and Architectural Design. I graduated from high school at 15 wanting to attend art school, but instead graduated as a Computer Engineer as the thought of becoming a starving artist was not an option with Caribbean parents. My first job was as a Sr. Research Analyst, known today as a data scientist, building models in SAAS (not sexy!). A year later, while seeking new opportunities, I was hired to use a tool called Xcelsius to design supply chain dashboards. The rest is history. I admit, exploiting data was not my focus, creating compelling data visualizations was.

What has been the biggest surprise in your career?

The failure rate of BI projects for the last 10-15 years is staggering! Per Gartner, seven out of every ten BI projects are deemed as failures. Every year companies spend millions of dollars on technology hoping to solve this problem. If they took a fraction of that money and invested it on the softer elements of BI projects such as the user experience, innovative user engagement techniques, and upskilling their existing teams, the failure rate could be extinguished.

What are some of the biggest challenges in leading today? How are you thinking about dealing with those challenges?

Leadership can be hard, but it’s even more challenging when you’re a double minority (black & female) running a global analytics consulting firm. Last year at the Black Tech Mecca event hosted by Google I presented data to show that less than 1% of blacks occupy tech jobs in the US. In the business intelligence and analytics world, there are no role models that look like me in this industry and as a result I’ve had to find great mentors from different walks of life. In addition, I continue to use my influence and social visibility to champion the #WIT (women in technology) cause at all times. I support and mentor a few executive women in the field. It’s important that us women support each other.

What advice would you give to other women who are interested in pursuing a similar career path to yours?

Do It. Data is the new oil. Data is a currency of its own. In this field the sky is the limit, as the uses and applications of data are infinite. Data like the internet, will eventually drive every facet of our lives. Think of it as getting a ride to space on NASA’s first space shuttle, and getting to choose any planet to explore!

What can women in the workplace do today to help build the foundation for successful careers?

Defy gravity. That is what I strive to do. I was blessed to grow up in the Caribbean where my parents never told me there was a box. I saw no barriers. I always encourage the women I mentor to look at the opportunities, not the challenges. Give the benefit of the doubt to those ignorant of the challenges we face. Seek out those around you who may need help. Join external networking groups like Women 2.0 or She++ to keep close tabs with like-minded women. We know that as women we have to work harder, so instill it into your mindset. Don’t be average. Exceed expectations and take on new challenges. I’m tired of hearing about the lack of opportunities. After working with 50+ large global companies you’d be surprised to see how many men will help to champion your cause. We’re in an era where many men in this field have daughters, and have finally realized that unless they help to create the change, their daughters will face the same challenges we do. Don’t limit your support channels.

“Study, learn, copy and then pave the way for someone else.”
Do you think that data can help build a more diverse and equal workplace? How so?

Absolutely! As Peter Drucker said ‘you can't manage what you can't measure’. This is also true when it comes to tackling the diversity problem in tech. It’s not enough to talk about the problem, once you quantify it, the numbers speak for themselves. Less than 25% of tech jobs are filled by females. Not only is this disappointing, it doesn’t reflect the world we live in. Thanks to social media, anemic stats like those embarrass big company executives who would otherwise continue to turn a blind eye to the matter. They realize that eventually their brand could be affected. The threat of being seen as a racist brand is enough to cause most companies to prioritize and address the problem quickly and openly before it is reflected on wall street.

How do you think individuals can use data to advance their ideas or careers?

There is an old saying, if you don’t know where you’ve been, you won’t know where you’re going. The same applies to diversity and the workplace. Research and understand the stats, but don’t allow them to affect your goals. Instead, use them to educate those around you and create awareness. Again, thanks to social media it’s easy to find women who are doing amazing things in our field. Study, learn, copy and then pave the way for someone else.

Thank you for sharing your stories with us, Mico!

<![CDATA[6 ways to integrate a data strategy into your product workflow]]> https://looker.com/blog/6-ways-to-integrate-a-data-strategy-into-your-product-workflow https://looker.com/blog/6-ways-to-integrate-a-data-strategy-into-your-product-workflow 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 Atlassian 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 development performance and can help you plan for future development cycles. Our ETL partners, like Fivetran, make it easy to move data from applications you use to run your development team.

Check out the new JIRA by Fivetran block that makes it easy to get up and running with Jira analysis.

2: Broaden your focus with data from other departments

The most successful product analytics often incorporates a wide range of sources outside of traditional product data to uncover insights into areas of the product effectiveness and broader customer experience.

Marketing, finance, and sales data can provide valuable insight to a product team. Not only does this new data provide a fuller picture of how the product is received, purchased, and used, but it can also determine the larger impact of product development choices. For example, you can see the impact of a new feature on revenue, the impact of a bug on customer retention or the impact of a marketing campaign on the types of new users.

Services like Fivetran make data consolidation with Jira easy and scalable.

Your data should be representative of your company as a whole, not just your department or function because at the end of the day, that is how your customer experiences your product and brand. By looking at the full picture, you can get a much richer and more accurate understanding of your product and your users.

3: Reveal your metrics that matter

Once you have the data you need, keep your product team focused on the task of building great products by developing clear, team-wide definitions of key performance indicators you can all agree upon. This includes not only product performance but customer engagement, as well.

Find your metrics that matter, then break those larger metrics down into levers that each individual on the team can begin to influence. Monthly active users is a great North Star metric but is difficult for an individual to influence directly. Increasing usage of a sticky feature, on the other hand, is a measurable goal an individual can work towards.

4: Apply potent metrics to every conversation

Once you have actionable metrics, make them a conscious priority for your team. Talk about these metrics in meetings and bring them to the forefront of group conversations.

In addition to in-person conversations, your product team likely uses a wide range of tools in your day-to-day workflows. Email, chat, and project management software all play a role in how we coordinate, track, and communicate. Set up alerts in these tools so the whole team knows when certain thresholds are passed or when unexpected events occur so you can act quickly.

Check out the new Looker integration with Hipchat which allows you to push data directly to the place where your team is talking.

Just having metrics isn’t enough. To make truly data-driven decisions, metrics need to be routinely baked into every conversation.

5: Shrink the distance between insight and action

Data ensures you’re informed when making decisions, but the larger the distance between the information and the action, the harder it is to keep the two connected.

Ideally, this distance would be almost invisible. If you have product issues to file, make sure you can do so right from your data tools without delay. If you are describing an insight in a Confluence page, insert that visualization directly so the reader can see the live data in context.

Making sure the data is available when and where it’s needed is crucial to building a data-driven organization. See the new Looker Actions that allow product managers to update Jira tickets directly from where they are analyzing data in Looker.

6: Treat data analytics development as if it were your own product

Data is only powerful when metrics remain relevant. Which is why it’s very important to enable reporting on a single version of the truth.

Your data tool must change and grow with your business, and to do this, you can pull best practices from other development processes you already know and employ. Rely on sprint cycles to build new analytics tools and use version control to keep improve analytic outcomes, encourage collaboration and pivot data development quickly when necessary. Learn more about how to use Bitbucket for version control in Looker here.

You can check out all of our apps in the Marketplace

<![CDATA[Women of Data: Interview with Margaret Rosas, Looker’s Director of the Department of Customer Love]]> https://looker.com/blog/women-of-data-margaret-rosas-looker https://looker.com/blog/women-of-data-margaret-rosas-looker This week, we are very excited to share the perspective of Looker’s very own Margaret Rosas. Since joining Looker over five years ago, Margaret has worn many hats: from release manager to chat analyst to community organizer. Today, Margaret heads up Looker’s Department of Customer Love, the global group of chat support analysts.

In addition to scaling Looker’s DCL, Margaret is a long time pillar of her local community. She connects her two passions - entrepreneurship and technology - by helping to promote burgeoning talent across the Santa Cruz Community with organizations like TechRaising and Santa Cruz Works.

Margaret, can you tell us a bit about your background and how it lead you to get into a career in data?

I’m an accidental technologist, but a native data nerd. Growing up I didn’t gravitate to technology so much, but I was always asking questions about how many people did what, what is popular in different locations, and how did the tabloids justify what their headlines claimed. I wanted to see the data long before I ever knew what the word meant.

But I didn’t connect computers to my data questions until I learned about this little thing called the internet. I was awestruck by the potential to connect people to each other —- the potential of the human network had me at hello world. I started a mad dash to learn how to code and learn every internet protocol I could because I was suddenly determined to be an internet pioneer.

What advice would you give to other women who are interested in pursuing a similar career path to yours?

Understand what drives and motivates you to contribute your best self. Be a sponge for learning new technologies. Learn SQL, it’s the language of data and you will want that foundation no matter how your career unfolds.

What can women in the workplace do today to help build the foundation for successful careers?

Confidence can be hard to come by, but it is essential to leadership. If you don’t feel confident, dig in and figure out how to build your confidence. It might mean learning something new, developing expertise or simply creating affirmations to psych yourself up!

“Leading with the data can shine a light on imbalance and inequities to build a better workplace.”
What has been the biggest surprise in your career?

The fact that I ended up in technology still surprises me. I can remember how I shunned the Computer Engineering program at GW. I didn’t think about why until much later, but I vividly remember walking through the CS buildings and not seeing a single woman student or professor. In reflection, I think I felt greater confidence in the Business school because it wasn’t lacking in gender diversity. It was my passion for the internet that made me completely disregard comfort levels and set my sights on all things internet.

What are some of the biggest challenges in leading today? How are you thinking about dealing with those challenges?

I’ve had to do a lot of work in understanding when and where to use emotion. Emotion can provide fuel to accomplish great things (both anger and passion are great instigators). However, they can also be terribly distracting and ultimately debilitating when used to lead. I know it’s cliche, but this is where data can be the great equalizer. My great challenge in the coming year is to lead with data while also staying true to my emotions.

Do you think that data can help build a more diverse and equal workplace? How so?

Science tells us that diverse ecosystems are the most successful, they flourish while less diverse ecosystems flounder. Leading with the data can shine a light on imbalance and inequities to build a better workplace.

How do you think individuals can use data to advance their ideas or careers?

Gut instincts and hunches are great things to tune into. But the become even more powerful when you are able to support them with data. Develop a strong partnership with your gut instincts and your data savvy to advance your ideas.

<![CDATA[Data of Women: Education and Literacy Around the World]]> https://looker.com/blog/data-of-women-education-and-literacy-around-the-world https://looker.com/blog/data-of-women-education-and-literacy-around-the-world In the spirit of International Women’s Day, we wanted to learn more about how women around the world are being prepared for future opportunities.

To learn more about this, we turned to the World Bank’s World Development Indicators dataset and pulled some metrics around School Enrollment and Literacy by gender, income, and location around the world.

School Enrollment
An education is key to the pursuit of upwards mobility and personal agency. To measure this, we analyzed what percent of the female population are enrolled in school, by country and region.

According to the World Bank’s definition of literacy, female literacy can predict the quality or preparedness of the future female labor force and can also be used as a proxy for the effectiveness of the education system. When looking at the effectiveness of an education system, the quality of education needs to be taken into account; simply having access to an education is not enough to ensure future success.

For both literacy and school enrollment, we obtained metrics from countries around the world over a period of 5 years from 2012-2016. Because we were not able to obtain complete data from all countries, not all countries in the world are represented in all parts of the dataset. We will make note of the missing pieces of data as we move through the findings.

In general, the dataset proved many common assumptions, but in unpacking these numbers, we found that there was more depth to be discovered in the data.

Access to Education by Gender


When looking at youth education, the data shows that developing nations - such as Sub-Saharan Africa and South Asia - have the highest percentage of youths not enrolled in school.

When looking at youth education by gender, the data shows that in the regions with a higher youth enrollment overall (on the left of the visualization above), there is a slightly greater percentage of the female population than the male population in school. However in the regions with the largest portion of the population of children out of school (to the right of the above visualization), the percentage of girls in school is much lower than the percentage of boys in school. In other words, as the overall level of youth population in school decreases, the level of girls enrolled in school decreases disproportionately more than the levels of boys enrolled in school.

Another way to look at educational access is by using the Gender Parity Index (GPI) - a metric used by UNESCO to measure the access to education to girls vs. boys in a given country. The Gender Parity Index measures the amount of girls in school compared to boys in school, with 1 being parity with equal access to education, below 1 being more skewed towards boys and above 1 being skewed towards girls.

Below, is the GPI of Gross Primary and Secondary School enrollment across the globe.


Not all countries report this data, but of those that do, the same regional problems that are highlighted above become extremely clear - the gap between boys and girls is highest in North Africa and parts of the Middle East and South Asia.

But there is good news here too - look at all that green! We took a look at this data broken-down by country to get a better idea of what this Gender Parity Index looks like across all countries:


Above, we look at a country by country comparison, which shows things are actually looking pretty equal. There are inequalities on both sides of the spectrum, but overall, it’s great to see that the availability of education is close to equal in so many places around the world.

Literacy Rate by Gender

UNESCO argues that literacy is a key factor in an individual's ability to take part in the labor market and succeed in society. With this in mind, we wanted to get a better understanding of how women fare compared to men when it comes to literacy

Please note: This dataset did not include literacy rates for North America (USA, Canada and Bermuda) so the region was not included in the following analysis.


Looking at the above visualization, we see a clear literacy drop in certain regions. We also see that as overall literacy rates drop in a region, the literacy rates or women drop much more than male literacy.

But let’s dig into this a bit more....


Another way we can slice this data is by income levels across all countries. When broken down by income tier, we can see that, overall, the literacy disparity increases substantially as income decreases.

But this pattern is not the case for all regions...


Another indicator in this data set is the the Gross National Income (GNI) per capita for each country and averaged across regions. This figure shows the wealth per person a country or region has, but is not representative of what the average person in that country or region would make.

One might assume that as GNI per capita increases, the rate of literacy would also increase, and while that is mostly true, there is a notable flip in the data.

As you can see in the visualization above, the Middle East and North Africa region is essentially tied for the second highest overall GNI per capita, but its literacy rates for women trail far behind that of other regions. And on the other hand, the Latin American and Caribbean region is far behind the first three in GNI per capita, but has high and relatively equal literacy rates.

This flip of expectations could be the sign of a priority difference between the Middle East and North Africa and Latin American and Caribbean regions. This bodes well for the future of women in the Latin American and Caribbean region, as it shows a much closer to equal investment in both male and female education.


So how are women around the world being prepared for the future?

In developing regions and the lowest income groups of all countries, women and girls are still facing a large gap in both educational availability and quality, with both school enrollment and literacy rates falling behind their male counterparts. But this issue has not gone unnoticed. Organizations like UNESCO are specifically investing in improving education around the world in order to bring the opportunity gap to a close. This data proves the need for that investment and it is encouraging to see it prioritized globally.

Overall, we were actually pleasantly surprised by much of this data. The actual percentage of Children Out of School was much lower than we had originally expected for both boys and girls. The Gender Parity Index findings and evidence of cultural shifts like those in the Latin America and Caribbean region were also promising. A global prioritization of quality education is clear in this data, and while there is still much work to be done, the numbers show how much has been invested in this cause to date.

There are many other factors about female life that this data does not cover, and we are eager to dive into more of that data in the coming weeks. Stay tuned for our next post about women in the United States based on data from the US Census.

Thanks for reading!

<![CDATA[Women of Data: Interview with Amy Anthony, Director of Data Operations at SendGrid]]> https://looker.com/blog/women-of-data-amy-anthony-sendgrid https://looker.com/blog/women-of-data-amy-anthony-sendgrid This month we are excited to launch Looker’s Women of Data interview series, which spotlights women leaders in the data field and shares their stories and advice.

We are thrilled to kick things off with a longtime member of the Looker community, Amy Anthony. Amy is the Director of Enterprise Data Operations at SendGrid, a Denver, Colorado-based customer communication platform for transactional and marketing email.


What’s your background and how did it lead you to where you are today?

I fell into software implementation consulting, and the easiest way for me to learn the front end of the software was to learn SQL and run queries to see how the data flowed through the backend and then compare to the front end software. That led to my interest in data and ultimately my career focusing on data.

What advice would you give to other women who are interested in pursuing a similar career in data?

If you have a drive and desire you can do it, even if it's not your originally intended career. There's a lot to be said for experience and hands on learning, to grow in your desired career path, outside of traditional training. And remember you have a perspective and voice that is diverse so don't be afraid to speak up.

What you think women in the workplace can do today to help build the foundation for successful careers?

Find a mentor. Cross-train in as many areas as you can to help widen your perspective. Ask questions, that's how you'll learn. If there's someone else that you value how they manage, present themselves or interact with others, spend time with them so you can learn.

“There's a lot to be said for experience and hands on learning, to grow in your desired career path, outside of traditional training. And remember you have a perspective and voice that is diverse so don't be afraid to speak up.”
What has been the biggest surprise in your career?

I think the fact that I love data the way that I do. I love the pipeline of sourcing data, transforming data, making data accessible and presenting it in a way that people can geek out on analyzing it. I love the challenge of staying current with data frameworks and methodologies and the challenge of finding ways to get the right data in front of the right person as fast as possible. I love that when you have governed, transformed business data you really can use it to learn from the past and make improvements for the present and future.

What are some of the biggest challenges in leading today?

The investment it takes to truly be a data driven company.

How are you thinking about dealing with those challenges?

Continued investment in data governance, user adoption, educating on data best practices in providing self-service analytics, and continually finding ways to get data into the hands of our customers faster

Do you think that data can help build a more diverse and equal workplace?

Yes. Data allows for insights, which is simply someone's ability to see something in a particular way and present and display the data effectively. The more people that do this well, the greater opportunity it opens for people to excel in this space, regardless of age, gender and/or background.

How do you think individuals can use data to advance their ideas or careers?

Without data you're just another person with an opinion (W. Edwards Deming). But when you know your business and you know how to use data effectively to explain and analyze your business, then you have just equipped yourself to successfully answer questions and solve problems.

This is a part of the Women of Data, Data of Women series. Check out this post to learn more.

<![CDATA[Announcing Women of Data and the Data of Women]]> https://looker.com/blog/women-of-data-and-data-of-women https://looker.com/blog/women-of-data-and-data-of-women I love watching the change that happens in companies when everyone discovers how easy it is to access and understand their business data. And it’s not just because these people can now make better decisions, it’s also because it clears the way for everyone to contribute their ideas and participate in solutions. No longer can the HiPPO (highest paid person’s opinion) in the room or the loudest or most aggressive person in the room, dominate the decision-making. Everyone can diagnose issues they are seeing and share their ideas as well. This can often bring a discussion back to where it needs to be: how to make the business more successful.

In the end, a more data driven culture also creates a more inclusive culture allowing for many different perspectives to be heard. And since March is National Women’s History month, we are excited to announce a new project highlighting these diverse perspectives: Women of Data and the Data of Women.

First, Women of Data will be a series of interviews spot-lighting the growing group of female leaders in the data space who are building data cultures at their organizations. Second, Data of Women will be a series of posts examining data about the lives of women today - in the workplace, in society and around the world. We will be looking at trends and share interesting findings about what is actually happening and where we can work together to improve the lives of women around the world.

We are kicking this off with new posts every week in March, and we will continue to highlight the Women of Data and the Data of Women throughout the year. We hope you enjoy them!

<![CDATA[Dashboard Confessional: I’m addicted to my Demand Generation Program Dashboard]]> https://looker.com/blog/dashboard-confessional-demand-generation https://looker.com/blog/dashboard-confessional-demand-generation Companies experiencing high-growth tend to set aggressive quarterly goals, focused on generating new leads.

Ensuring your company maintains quality leads while growing their volume can be a tricky balancing act.

For many, maintaining a cost-effective lead budget while scaling programs can be a challenge. Without the ability to calculate ROI and cost-per-lead on a daily or weekly basis, it's hard to know where to invest and where to cut back.

Looker has provided an operational way to look at program performance and growth that has proven to be a real game-changer.

As a Program Manager at Looker, I regularly use the dashboard shown below. It's helped me to grow the program 5x over the last couple years, in a cost-effective way. I am strangely addicted to it and find myself checking it and gazing at it multiple times a day.

Digging into the Dashboard

From a top-level perspective, we keep a close eye on how far we are through the quarter, as we are on quarterly targets from a planning and execution standpoint.


Next, we have the aggregate volume of leads per with a line graph of the conversion rate. This is helpful to know how the program is doing on a weekly basis.

If there is a dip one week, I usually check in with our SDR manager to see if there is an operational issue in delivering the leads or extra enablement needed on our end.


The cohort analysis shows which vendors are responsible for the majority of weekly lead growth. This is helpful in knowing which vendors to push or raise PPC bids on.


Taking a step back, we have a quarterly view of which vendors are doing the heavy lifting through Donut Multiples. Naturally, the goal is to diversify the vendors we use so that if one under delivers, the program and SDR’s meeting goals don’t suffer.


Lastly, there is a quarterly accumulation of all of the metrics into one table. Here I can check the number of total leads, meetings, and velocity (average time it takes from coming into the database to becoming a booked meeting for sales). I can look at individual conversion rates and if they seem to drop I investigate why that might be happening.


Long Term Growth

Over the past couple years, this program has seen an enormous amount of growth, and I attribute all of that growth to the meticulousness of this dashboard.



We use this dashboard operationally and to review our tactics at the end of each quarter. By constantly monitoring the pulse of the data - and taking a step back when we need to - we are able to use our spend to the best of our ability, and ultimately do our part to drive the business forward.

<![CDATA[2018 Marks the Debut of Looker in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms]]> https://looker.com/blog/2018-gartner-magic-quadrant-for-analytics-and-business-intelligence-platforms https://looker.com/blog/2018-gartner-magic-quadrant-for-analytics-and-business-intelligence-platforms 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.

I encourage you to download and review the complimentary report here.

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 you to your data, so you can make more informed choices, share and explore insights, and dramatically improve your business. With Looker your analysis is flexible, extensible, reusable, sharable, and scalable – and I believe our satisfied, successful customers are a significant part of why we’re included in the Gartner Magic Quadrant this year.

Since its inception, Looker has focused on providing exceptional support for our users, enabling new users of the Looker platform to come up to speed faster and to make better informed, more authoritative decisions based on the freshest, most complete data available. The 2018 Gartner Magic Quadrant for Analytics and BI Platforms report reflects these values.

At Looker we continue to evolve our product to provider fresher, more impactful analysis you can trust. I believe this recognition from Gartner is a recognition of Looker’s technology and mission as a data platform. If you’re interested in learning more, you can download this valuable report at no cost and learn more about why Looker is so proud to be included with the top companies in BI and analytics.