Pivot

We knew we had to pivot. Plain and simple. Our last product proposal was both infeasible and its usage was unclear. We had to make something more concrete and implementable in less than 15 weeks. 

We met in the d.school at 10pm. Drawing from the conversation we had earlier that day with Jay and the teaching staff, we began brainstorming with emphasis on: 

• Clear use cases. We knew we had to define its usage in a very concrete way. Although our original goal was to make very 'agnostic' software, making concrete use cases would probably mean making ourselves more niche and honing in on the IT space due to our relationship with VMWare. 
• Actionability. We wanted our product to not only to have clear use cases but be a service to actually do something in the real world. Focusing on the IT world (see above bullet) this meant we want to make the jobs of either VMWare or VMWare's customers easier and more effective. 
• Data Driven. We wanted to play to our machine learning background and allow the power of the algorithms we know to deliver value to new verticals. 
• "Under-promise and over-deliver". Out product had to be something with a valuable core but with room to add on all sorts of sexy features. This way we could give ourselves reasonable goals and allow for the possibility to exceed expectations. 

We began our brainstorm. Working with our skills, we asked "What is modeling good for?" We talked about reactive alerts. We jumped around to a lot of different topics and many different users. The one person we had met with regarding needfinding, Denis the dogfood environment IT Admin, kept coming to mind. Denis found the task of troubleshooting IT environments to be a tiresome task often laced with the trouble of communicating with many silos... 

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With some teamwork and iteration, we thought we had it. We would once again deal with log data, creating tickets from anomalies and errors found within the logs. These tickets or alerts would be intelligent though, giving IT Admins like Denis advice on how to solve tough errors based on errors statistically similar to it. We would allow for easy or even automatic collaboration with these tickets, routing tickets to those who really can address the issue at hand. Once again this was a very data driven product, but with clearer use cases, actionable results, and features easier to rapidly prototype. Plus, we were excited about the idea! We felt that we had successfully pivoted, and couldn't wait to pitch the idea to our users, IT Admins. 

Post-Meeting Brainstorm

Saturday morning after meeting our liaisons for the first time, we reconvened at the d.school to brainstorm solutions to the task that Karthik had laid at our feet. Once again, Santi led us by moderating.  

We quickly came to the conclusion that we wanted to work with unstructured log data. We felt that this helped keep our product more "data-agnostic" as simple logging techniques are both ubiquitous and information-rich. We began brainstorming about methods to extract insightful information from these logs. We came up with a variety of solutions from easy querying and visualizations to summary statistics to alerts and report generation. We felt like we had hit a jackpot of great ideas and started to dive into more detail about each one. 

At some point, Splunk came up. We knew Splunk was a large company with amazing technology to access your "machine log data." So we googled them. We found that all the sticky notes we had on our board were already on Splunk's sales page. We had spent the last hour reinventing Splunk. 

This was both saddening and encouraging. On one hand, our "new innovative ideas" were already implemented, and were surely to be better in basically every way than something we would make in 6 months. On the other hand, we realized there was value in what we were talking about. In about an hour we had thought up the core products to a billion dollar multinational company. We knew we were onto something, but we had to change direction. We had to distinguish ourselves from companies like Splunk. 

Differentiating from Splunk ended up being great for our brainstorming productivity. All of a sudden, we had a flurry of ideas. Synthesizing and compiling them, we found an ambitious product with a catchy, buzzword-filled tagline "Turning Big Data into Small Data." Inspired partially by the Stanford course, we postulated that with proper unsupervised modeling techniques we could turn the large, intractable data companies had on their servers into smaller representative sets that users could interact with in their browser or mobile app. We envisioned that with smaller scale data we could allow for sweet visualization and interactivity without sacrificing the integrity of the overall data. Our main users would be executives and managers (decision-makers) who wanted to make data-driven decisions quickly without a lot of technical expertise. Additionally, we felt our product would be useful for employees that had to directly deal with the data at hand, such as IT Admins or Data Scientists, as they could get a summary of the data before using more technical tools like Splunk for deep-dives. Our envisioned product could give you an overview of your data, a "feel" for your data in some sense, with the potential to shed light on problematic or interesting trends automatically and without waiting for external reports. 

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Altogether, we felt like we had successfully ideated a product that: 

• Was different enough from other business intelligence solutions such as Splunk, Tableau, Domo, etc. 
• Was capable of giving valuable insight to our users 
• Helped to address the "3 month" reporting issue

As such, we compiled our notes and prepared to pitch this product concept to Karthik, who was to meet us the coming Tuesday to hear our proposal. 

Pre-Meeting Brainstorm

The night before our first meeting with our VMware liaisons, all members of DVation met in the famed Stanford d.school to have a preliminary brainstorming session. The point of this brainstorm was to go into our first meeting with some broad ideas of what we wanted to accomplish and what sort of products we wanted to make. Our Project Theme had hinted at applications of Big Data and Data Visualization, so these two themes guided our discussion. 

In preparation for this brainstorming session, we all researched different aspects of the space we were to be in. Some of us researched exactly what VMWare does and the products they offer, while others researched the data visualization competitive landscape and other business intelligence solutions. These insights proved to be invaluable during our brainstorm, 

Santi, our design thinking messiah, led the brainstorming activity. In true d.school fashion we proceeded with "yes, and..." statements, writing and rearranging sticky notes. We focused on thinking of new and innovative ways to visualize data and give insight into one's business. We made guesses regarding the nature of the data we would have access to via VMWare. We thought of ideas which would leverage our skills and backgrounds, mainly in the area of machine learning. 

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After a few hours of deliberation, we encountered a few main themes that we all agreed on: 

• We wanted to make a data-agnostic product. Sorry VMware, we don't want to make something that only applies to your space. 
• We were all allured by the idea of quantifying productivity. 
• The idea of interesting info coming to you was consistent. We liked the idea of not having to make queries, but having analytic solutions give a business owner info "before he/she even needs it."
• We wanted to make some sexy visualizations. 

With this collection of broad ideas, we felt prepared to meet our liaisons at VMWare the next afternoon.