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TechBytes with Julie Lemieux, VP of Product Design at Sigma Computing

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TechBytes with Julie Lemieux, VP of Product Design at Sigma Computing

Tell us about your role and journey into Technology.  

 I am the VP of Product Design at Sigma Computing, which means I oversee the design and user experience of our product. Right now, I am hyper-focused on gathering as much feedback from our customers as possible, so I can continue to improve how they interact with Sigma.

I majored in American History for my undergrad and spent a great deal of my free time in the Computer Science labs. After graduation, I was trying to figure out what to do and my dad encouraged me to explore “this Internet thing” that had captivated me in the lab. One summer, I ended up temping at a Satellite Communications company and was asked if I could add information to their website. I said ‘yes’ and then bolted out of there and bought a copy of HTML for Dummies because I had no idea how to actually do it.

I kept at it, and, in no time, I was hooked on the internet and opted to do a post-grad in design. After graduation, at the height of the dot-com boom, I moved to Silicon Valley and began working at Escalate Retail. From there I moved on to Yahoo! Small Business, NetSuite, BEA Systems, SAP, Databricks, and a few others, before finding my way to Sigma.

What’s your history with analytics, and how did you get into this space?

I don’t have a great love for Applied Math—I don’t have the head for it—but I do have an affinity for seeing the numbers in front of me and thinking about how those numbers could reveal other things. One of the challenges that my father gave me, after I got my first job was to prepare my own tax return. While the calculations weren’t very complicated, it was exciting to be 16 and completing my taxes in a spreadsheet on our home computer. I have been a devoted spreadsheet user ever since (I still call my dad for help with Excel power-user moves).

Data and Analytics are just the natural evolution of that affinity, which went to a whole new level when I joined Business Objects, shortly after the SAP acquisition. I spent three years as the Chief Experience Officer of SAP BI & Analytics.

Why is data-driven Marketing so important today?

We are entering the post-digital era, where B2C and B2B buyers alike expect highly personalized products, services, and experiences. And they expect them on-demand. To meet, let alone exceed, these expectations, companies, and especially marketers, need a far deeper understanding of their customers, their buying habits, and more.

Lots of this information can be found within the data that companies already have, but standard-issue Business Intelligence (BI) reports don’t give all marketers the ability to explore the raw data that they need to excel today. Marketers need a lot more flexibility and the freedom to explore data themselves, rather than look at it in a predefined report, which someone else made for them. In addition, many marketers aren’t comfortable writing SQL, Python or R to explore raw data in an ad-hoc manner.

Marketers, more than any other team, need to be able to indulge wild-hair ideas. They may have a hunch that a specific buyer is interested in one product or another, but they need data to validate those theories through exploration. Only then will they be able to find the consistencies, patterns, and trends they should be focusing on to move forward and get ahead.

Marketing is becoming increasingly analytics-driven. How does Sigma help and where do you see the future going?

More often than not, Marketing teams are only looking at pre-built dashboards in a specific application with data from the last 90 days. This kind of view is great for very tactical applications or to check on the performance of a specific campaign, for example. But you need access to Big Data to explore Marketing program data holistically and to observe trends over time. This kind of analysis requires data well beyond the narrow scope that is common today.

This is where Sigma Computing comes in. Sigma is a gateway to incredibly large volumes of data because it sits right on top of Cloud data warehouses, like Snowflake, Redshift, and BigQuery. Not only giving you the opportunity to access and explore enormous amounts of data without writing any code – it also allows you to assemble data from multiple sources of data and look at it all together. This is quite different from other tools that require a lot of heavy integration work and professional services, just to bring those other types of data into your warehouse, let alone incorporate it into any kind of analysis.

The future really presents the opportunity to break down information silos further, and co-mingle multiple sources of data. Sigma removes those barriers and provides a datacenter view of the world. For example, you can look at HubSpot and Salesforce data alongside in-product analytics to discover connections and insights, which would otherwise be impossible to see. The more sources of data that can be brought together, shaped, and looked at in context, the better your insights and marketing program are going to be.

Why should every marketer look to make data analysis part of their toolbox?

Your Marketing program is only as good as your understanding of the big picture. The ability to spot patterns, trends, and outliers within that big picture, really hone in on them, and derive actionable insights from them is mission-critical in this highly competitive, global marketplace.

If you don’t have the complete picture, you’re going to miss stuff, and that stuff may just turn out to be the key detail you need to set your brand apart in the minds of your potential customers.

What do you think are the biggest analytics challenges facing modern marketers today?

The biggest challenge is data literacy, but it is also the easiest to solve. Let’s face it, large data sets can be extremely overwhelming for anyone – even people who love data, which a lot of marketers do. It is totally normal for there to be some initial trepidation and people may need help taking that first step into analysis. They also need to be assured that they can explore the data safely. That they won’t “break” it, or accidentally delete anything. There is also a prevalent assumption that data analysis requires technical skills, like being able to write SQL or understand how databases work. People have been conditioned to believe that they must rely on others to find the insights contained in their own data. That is just not true.

The next obstacle is disparate data sources. According to the World Economic Forum, it is estimated that 463 exabytes of data will be created each day by 2025, but this data lives in millions of places, like the countless apps and tools we use in our personal and professional lives everyday. It would require an incredible amount of time, effort, and budget to integrate those various products and the data generated and stored within them. Additionally, those products can only serve up a certain amount of data. They can’t handle Big Data, because they just weren’t designed to.

The last challenge is shaping the data once it is brought into the warehouse, so marketers have a clear starting point. This requires tighter collaboration between marketers and the data team to model the data into a consumable and usable form. They need to work together to ensure the data is shaped in a way that is usable for their use cases. Marketers won’t know all the questions they want to ask of their data when they are getting started. They really just need to get in there and play around. Collaborative analytics is an iterative process.

How does Sigma Computing address those challenges?

Sigma exposes what’s inside the data warehouse in a far more consumable fashion than ever before because it uses a familiar spreadsheet-like interface. Marketers interact with the data in the warehouse, via drag and drop, clicks and keystrokes, and Sigma translates those gestures into SQL queries that the warehouse understands. We perform the reverse, once the warehouse answers the query, displaying the results into a user-friendly format in our spreadsheet UI.

Marketers don’t have to go spelunking through thousands and thousands of tables. Data teams can curate and elevate the data from a variety of different sources that are appropriate for a marketer to use. They can also model the data, in such a way that they pre-join multiple data sources in anticipation of the most common queries marketers need for spotting patterns and trends across the broader ecosystem. It is also really easy for data teams to go back in and quickly make adjustments for domain experts. Something like that can take hours of SQL coding, but in Sigma, it takes a few minutes.

What would you say to someone who doesn’t come from an analytics background and wants to become more data-driven in their everyday workflow?

Data exploration should start with a hypothesis and list of questions. Then you go in and start testing that hypothesis. You will find that your data literacy will increase with every interaction. Data exploration is a very iterative process and the more you do it, the more you will understand the kind of data you need access to, and how it needs to be shaped, in order for you to ask the questions you need answers. I’ve noticed that the more people explore data, the more they want to do it, and before you know it, they’ve been exploring data for hours on some intense fact-finding journey. It can be addictive in a way. Even simple actions, such as grouping data in a different way or applying more complex filters, can yield insights.

Do you see the future of analytics becoming more accessible to those without technical backgrounds in programming?

Absolutely, it has to. That’s really Sigma’s goal. Legacy tools deliver BI through dashboards and reports prepared by analysts. But that isn’t data exploration and that’s not how the curious human mind works. With Sigma, you don’t need to be able to write a single line of code to get your questions answered. Once people realize that they don’t have to be an engineer to ask questions of their data, you won’t be able to stop them. Unleashing your Marketing team to find insights in your data, means absolving them of the need to learn how to code.

What types of data problems do you see agencies and in-house marketing teams encounter?

First, most marketers don’t have access to all the data they need. Second, some marketers may have access to data, but it is siloed in different tools. They may have dreams of combining multiple data sources, but they don’t know how to bring them together in a way that leads to insights.

Finally, most BI tools today are not user-friendly and still require a significant amount of coding ability in order for a marketer to be able to get the views into the data warehouse that they need to glean actionable insights.

What Digital Technology start-ups and labs are you keenly following?

Superhuman, hands down. Superhuman is a new email client that is on a bold mission to help people achieve inbox zero. As if that didn’t make them cool enough already – because who doesn’t want an empty inbox? They also aren’t rushing to get the product out there. They are taking a really user-centric approach through beta customer feedback, so they can improve the product before officially rolling it out on a large scale. They want to be sure that the day I start to use Superhuman that it helps me achieve my specific goals. I still don’t have access. 🙂

What technologies within your industry are you interested in?

I am most interested in ELT technology, like Fivetran. It really amazes me how they are able to bring together the data from so many different sources, put it in the data warehouse, and keep it fresh in real-time. That seems like a magic trick and I need to learn more about how they do that so I can better advise our customers on how they should handle combining data sources.

As a tech leader, what industries you think would be fastest to adopt a data-driven approach? What are the new emerging markets for these technology markets?

The personal transportation industry, indeed any verticals that generate significant numbers of transactions, are blossoming through data-driven approaches and we will continue to see them flourish as they find new ways to harness the power of their data.

Which superhero character/movie/sci-fi book are you most inspired by?

I am really into the Detective Bernie Gunter mystery series, written by Philip Kerr. Each book is set in multiple time periods, from post-WWI Germany all the way into the Cuban Revolution in the 1950s. Commissar Gunter solves mysteries that take place in the backdrop of actual historical events and feature well-known people from those time periods. What inspires me about Gunter’s character is that he is very detail-oriented and perceptive (like all good detectives), and throughout it all, he is unwavering in his moral stance and is true to himself, while navigating some pretty hairy circumstances. Gunter simply doesn’t give up and just when his antagonists think they have him stumped and beaten, he manages to beat them through the evidence and data he has collected throughout his investigation – which may have inadvertently started decades prior. His memory is his superpower.

What’s your smartest work-related shortcut or productivity hack?

I move really quickly when I design and one of the ways I am able to do so is by maintaining a library of design patterns for common interactions. With pre-made patterns, I can ‘rough-in’ an idea really quickly to establish an initial structure. Once that’s done, I can focus on innovating in new areas. Patterns also ensure that I am not introducing new ways of performing common tasks that might create inadvertent mental friction for the people who use Sigma’s products. This is a common way for designers to work, and for me, it is a proven method to make sure I am focusing on the right areas of my designs, rather than wasting time by re-creating the wheel. At Sigma, we maintain our design pattern library in InVision DSM, so we can easily update and add, as well as make sure that everyone on the team is drawing from the same library, enforcing consistency and predictability in our designs.  It allows us to move quickly and deliver designs at a rapid clip.

Tag the one person in the industry whose answers to these questions you would love to read.

I’d love to read Megan Smith’s take on these questions. Megan is the only woman to have served in the role of CTO of the United States, where she served President Obama for three years. I have always found Megan’s view of technology, and its potential for good in the world, engrossing and I find great inspiration whenever I have occasion to hear her speak. As a trailblazer for women in STEM, her perspective on all matters in technology is all the more relatable to me. Given how fast data and analytics are moving, Megan’s forward-looking view of the space would be inspiring and enlightening, without a doubt.

Julie Lemieux is the VP of Product Design at Sigma Computing. Julie focuses on enabling business people to query cloud data warehouses to answer their business questions – all without writing a lick of SQL.

Previously, Julie worked at Databricks as the Head of User Experience, Heighten as the VP of Product & Design and as the Chief Experience Officer for BI & Analytics at SAP.

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Sigma is analytics built for the cloud. Trusted by data-first companies, Sigma provides live access to cloud data warehouses using an intuitive spreadsheet interface— empowering business experts to ask more of their data without writing a single line of code. With the full power of SQL, the cloud, and a familiar interface, business users have the freedom to analyze data in real-time without limits. Sigma is closing the gap between data teams and business experts.

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