Attending the Tableau Conference 2018 in New Orleans, Louisiana is definitely a milestone in my tech career! I had a great time immersing myself with fellow data geeks and learning best practices in Big Data and Analytics.

TC18 offered 439 sessions on various topics, department and skill level. I had to be strategic in choosing which talks to attend to by considering my role and interests.

My Track: Business + Technical

Since I work in business-oriented analytics, I opted to attend sessions of companies discussing their best practices. I chose talks on handling sales and marketing data. Some I attended are from Netflix, Tableau, Boeing and Elite SEM.

I use Tableau at work on a day-to-day basis, so I also attended technical talks (Tableau how-to sessions) to sharpen my skills. I was able to squeeze in some sessions on project and program management too.

Below are my five takeaways from the four-day conference:

  1. Netflix's rapid company growth is driven by its data-driven culture and tech
  2. Tableau's analytics and operational excellence made them a market leader
  3. Features in Tableau I didn't know existed
  4. The Agile approach is applicable even in analytics
  5. We are all different and yet the same

Can I just say that I had a hard time limiting my list to just five? I have learned so much, but the actual challenge is to apply everything to my work (and even to my life!).


1. Netflix's rapid company growth is driven by its data-driven culture and tech.

Among the slew of company talks I attended, the Netflix one stood out for me. It wasn't the "We're the best" type of talk, but the tone was more like "We do things well, but we face problems too". The humility of a billion-dollar company to discuss their data challenges has left me awestruck.

The speaker, Netflix's Head of Analytics Blake Irvine, started by talking about their rapid company growth for the past decade in terms of their user base (137 million subscribers worldwide as of 2018) and product offerings (damn, have you watched their first interactive content, Bandersnatch?).

Netflix's Workforce: All business areas have designated data and analytics personnel | Images: Binging on Data: Enabling Analytics at Netflix

Then, he talked about how data-driven culture is embraced across their organization. Reed Hastings, the co-founder and CEO of Netflix, has a high focus on data and measurement that they allocated ~10% of their workforce just for data and analytics. Each business analyst and engineer is assigned to a specific business area, so he can have higher context on the business questions that need to be answered.

Netflix's Big Data Portal | Image: Binging on Data: Enabling Analytics at Netflix

Blake also introduced the tech behind the culture: the Big Data Portal. Their objective in building this is to enable anyone in their organization to be data-driven in their day to day work. It took them 18 months to build this portal.
Netflix's formula for growth = Culture + Tech (Big Data Portal, Tableau) | Image: Binging on Data: Enabling Analytics at Netflix

I highly encourage you to watch the TC18 Netflix session: Click here

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2. Tableau's analytics and operational excellence made them a market leader

As a market leader for BI and Analytics platform, Tableau, Inc. is a reputable resource for best practices in analytics.

I work in reporting and analytics for Sales operations at RingCentral, that's why I was inclined to attend Sales @ Tableau sessions. My main objective was to grab ideas on metric design and data visualization to apply to my work.

TC18 covered sessions on Sales operations. Learned that their CRM is Salesforce too.


Here's my favorite data visualization on correlation of time spent and revenue generated:
Image: Sales @ Tableau: Supercharging Tableau for sales productivity

What I love about the viz below is how simple the execution is, and yet it answers the fundamental question: How exactly does my sales workforce spend their time?

Image: Sales @ Tableau: Supercharging Tableau for sales productivity

One major thing I noticed is how sales productivity is measured by activity level. In my work, this is difficult to pull off because sales employees don't log our hours. I realized that my viz dreams will only be fulfilled with the right data architecture (LOL).

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3. Features in Tableau I didn't know existed

Apart from the company talks, I also enjoyed the technical insights I got from TC18. Tableau offers a wide array of features I didn't know existed, such as the data entry, embedded web pages and integrations with Python!

Tableau Server has an ability for data entry. As shown the image below, Tableau sales consultants log their hours in a Tableau server page. Maybe I should explore this option in the future?

Image: Sales @ Tableau: How Tableau uses data to manage its workforce


Tableau Desktop has also an ability to embed web pages in the dashboard, as shown below:
Image: Sales @ Tableau: Supercharging Tableau for sales productivity

Other cool features and good practices I learned (and will explore on my own time):

  • Integrating Python in Tableau: Accelerate Your Advanced Analytics: R, Python & MATLAB - Click here
  • Designing Efficient Workbooks - Click Here
  • Sets Appeal - Click Here
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4. The Agile methodology is also applicable in analytics.

One common thread among companies that presented in TC18 is their iterative approach in practicing analytics, which reminded me of the Agile manifesto in software development. The Waterfall method seems to be out of fashion anymore, well at least for the newer tech companies.

Tableau has discussed extensively their methodology in setting up their metrics. Below is their illustration of how they operate:

Image: Operations @ Tableau | Don’t Ask me what to do you should already know

I like the concept of building proof-of-concepts (POCs). It's about time for organizations to acknowledge that we can't get things 100% right on our first try. Building POCs is an efficient way to validate and communicate that an idea could work. It's also the fastest way to get our feet wet in any ambitious initiative.

Perfect is the enemy of good. - Voltaire

This learning has hit me on a personal level. Perfectionism is actually a hindrance, rather than a strength, in this fast-paced world. In my four years of working, I learned that delivering something is still better that not delivering at all, even if that something is not up to par just yet. A little progress, such as a proof-of-concept, is always better than nothing.

Establishing a baseline brings you back to the "Why" of the project, and ensures that the fundamentals are being covered. It prevents analysis-paralysis. In the context of analytics, baselines are the business questions that the metrics have to answer.

Lastly, communication and collaboration ensures that your initiative is still relevant to the business. Having a feedback loop with the stakeholders allows you to determine if your analytics project still meets their evolving needs.

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5. We're all different, and yet the same

Meeting fellow business analysts from different industries made me realize that we all encounter the same data challenges: manual work, handling data volume and integrity, and information overload

First, we cannot escape grunt and manual work. This slide from Elite SEM perfectly illustrates how Excel and Powerpoint have dominated our analytical lives. I'm not saying it's bad (we all know that executives love their deck!), but tech has evolved a lot that better ways now exist.

Image: Elite SEM | Streamline and amplify marketing analytics with Tableau

Even Netflix, the company I look up to in terms of company growth, experiences data challenges too. One major issue is on balancing data volume and speed of access. As the company grows, the tech must be able to cope with the growing data volume. Analysts won't adopt any technology that is slow and difficult to learn.

Another issue is making sure that data is clean and trustworthy, since more people have write access to their Big Data Portal. "Too many cooks spoil the broth," as they all say.

Image: Binging on Data: Enabling Analytics at Netflix

Lastly, this slide from one of the Tableau talks struck a chord on me. I admit that I am one of the victims of information overload, especially in making dashboards. Analysts like me tend to get overwhelmed with too much data and forget the story they intend to tell.

Image: Sales @ Tableau | Supercharging Tableau for Sales Productivity

We all have the same mission

To cap things off, business analysts have the same mission for their respective organizations to find value from all the data they collect.

As discussed above, most companies experience the same status quo as illustrated below:

Tableau Conference 2018 has convened data geeks from different companies to discuss the right tools (Tableau is one!), approach and mindset in order to fulfill this mission faster.

I see this as another case of human and technology working together. "Finding value" comes from the human work of analysis and strategy, so time should be spent more on that. Let the tech like Tableau help us be more efficient in doing the harder work of gathering and preparing the data. - EM

Myself sporting the RingCentral cap during the conference.


Sessions featured on this blogpost:

  • Binging on Data | Enabling Analytics at Netflix: Click
  • Sales @ Tableau | Supercharging Tableau for Sales Productivity: Click
  • Sales @ Tableau | How Tableau Uses Data to Manage Its Workforce: Click here
  • Operations @ Tableau | Don’t Ask me what to do you should already know: Click
  • Elite SEM | Streamline and amplify marketing analytics with Tableau - Click
  • Accelerate Your Advanced Analytics: R, Python & MATLAB - Click
  • Designing Efficient Workbooks - Click
  • Sets Appeal - Click
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