Data Was A Journey. Now It's An Odyssey

THE BEAUTY OF THE DATA BEAST

Reality check: do we live in a more complex or complicated world than yesterday? In the age of Big Data, the volume, variety and velocity of data streams have been growing at an increasing rate, apparently overwhelming at first but extremely promising as a universal solution. Structured and unstructured data, on-premise and online data, social media data or IoT data, they all seem like a whole bunch of wild animals coming at us.

When Gauthier Vasseur, a Stanford instructor teaching data analytics, first drew this picture through his words, I thought of the world as a Delacroix painting. But with his experience of more than 15 years in companies such as Oracle, Google or TriNet, Gauthier went on to simplify the painting: “Whatever type of data we encounter, we must not forget for a second that it’s all “only” data and apply a common logic for all of them. The tools we might use to process them might be slightly different but the principles remain the same. This foundation should be as widespread as possible in terms of knowledge and skills to empower anyone to engage with confidence and efficiency with any data and that’s really the core component of my classes.”

This is why Gauthier has been teaching since 2012 at Stanford the course suggestively entitled How to Tame Data to Drive Big Insight, a data journey - as he calls it - for people to learn to master the key data techniques. The data process is as easy as capture, connect, filter, store, analyze, share.

“It may seem easy indeed and people living the data revolution are looking to dive into all the data streams that could give them an edge. They are not afraid to do so. And this is scary.”

However, even though the business users are willing to take all the data in the world and apply it to their company, Gauthier believes that there are still competencies missing. People are jumping very quickly from ‘no Business Intelligence’ to wanting to test Artificial Intelligence or Machine Learning. Now, with the burst of data from social media, they are creating dashboards to analyze their performance in these channels. But data should be tamed for a purpose - to drive insights. His course is intended to determine BI users to make their data discoveries sustainable, to maintain quality and control of the data flows, and to ask themselves questions such as How will people react to these findings? or Can our team reiterate the analysis and scale it?.

Ultimately, concludes Gauthier, it drives people to ask not all the possible questions and start boiling the ocean (just because the solutions nowadays enable us to do so) but the right questions from their data.

THE RIGHT PROBLEMS

In this context and with the rise of OpenData and the democratization of data, one might wonder if these right questions could be asked of humanitarian issues as well as business processes. Gauthier comes to the rescue again: “Indeed, people are more inclined to apply BI to business matters - as a means to make more profit but do not forget that be it a humanity issue or a business one, we are still talking about problems and the same data techniques can be applied to any of them.”

He highlights the fact that most people still look at BI in relation to analyzing sales or revenue data and evaluating financial performance, but he also draws a distinction between purpose and goal. BI can be used as SI (Society Intelligence) - from an artist who wants to understand how their work can be seen by more people to marine biologists wanting to understand how to protect an eco-sytem (check these guys: The Farallones Marine Sanctuary Association (FMSA) is the only non-profit organization dedicated to protecting the Gulf of the Farallones National Marine Sanctuary's wildlife and habitats through the development of a diverse community of informed and active ocean stewards). As a matter of fact, NGOs that master data can make a big difference.

There is a breadth of possibilities regarding what you can do with BI, says Gauthier. “It’s essential to not only have data but to have Great Data, that is cleaned and well assembled for results to be accurate enough to generate an impact. And yes, maybe the ones who have already started applying BI to try to solve humanity’s issues should learn from the focused attitude of BI business users. Good insight starts in the trenches: cleansing, governing and connecting data is critical to valuable subsequent analysis. And actually, BI can also play a big role in that phase in visualizing data issues or inconsistencies.”

Judging by its pervasiveness and the short and long-term benefits of analyzing data, it may seem that data science is becoming as popular as the coding movement. Gauthier brings a grain of salt  though: “Allow me an analogy. You can give cars to many more people but this does not mean we will have more good drivers. I let you imagine what would happen if we were all given Formula 1 cars to drive around. People working with data and performing only basic processes with it reach a plateau of usage quite quickly. On the other end, using large data sets and advanced analytics with little to no math or stat background is dangerous. This is why enhanced learning is extremely important in this field, too.”

BACK TO BASICS IS THE FAST-FORWARD PATH TO THE FUTURE

There is a growing desire nowadays to see the immediate ROI of doing data analytics. Even though users still seem to analyze everything by revenue, sales or classic finance indicators, Gauthier - who taught at the Bordeaux Business School, SP Jain Singapore-Dubai and the Ohio University before joining Stanford -  believes that people are starting to understand that they are not always measuring what they need to measure, not asking the question they really need to answer - and that Big Data will not automatically solve these missteps. “There are times when the charts and analysis we did are showing amazing results but those KPIs might not solve the business issue we initially set ourselves to solve.” This is why we need to dare going to the bottom of a business issue and ask the right questions even if the resulting indicators to answer it might seem unconventional and hard to get. This is where you see the true value of data, and understanding the breadth of the data supply chain (from capture to share) will empower everyone to do things right.”

It’s been 3 years now since he is teaching Silicon Valley professionals at Stanford about the key data techniques through an experiential, interactive and multi-faceted approach through a blend of stories, interactions and guest speakers. He noticed indeed a spike in the interest of his students when he first tackled the topic of Big Data, but he believes that the buzz has already passed, so they focus on understanding the basics of the data system - systems, processes and people. As a proof of the success of his class, one of the most popular in the Continuing Studies series at Stanford, Gauthier Vasseur also launched this year the online version of the course.

The popularity of data analytics, let alone the concept of Big Data, was not always so high. Gauthier recalls: “Until the 1990s, being a data guy did not make you look awesome. But after that, people became data-friendly. Today, we can have a conversation with a marketing person about how to combine revenue and profit data with web traffic data that wouldn’t have been possible 10 or even 5 years ago. Analyzing data became popular because it opened potential sources of insights to people. Now it is our mission to make them understand that they need to analyze quality data to get the right insights.”

AN ODYSSEY. THE ODYSSEY

This mission is a leitmotif of Gauthier’s experience. Reading about his career, I stumbled upon a quote that he considers to define this mission: “Data Management is not a project, not an initiative, not a journey. It is an Odyssey!” The first moment, I asked myself - is data analytics advancing as fast as technology and science nowadays?

Gauthier has an extended view here, too: “We all see the pace of technology - there are many more surprises ahead and that’s an easy guess but even though people see data analytics and Big Data as a technical domain, the concept of the odyssey means going beyond data. We are going beyond our solar system in this, we are going to tackle the borders of our galaxy. And data science, machine learning, artificial intelligence - all the tech buzzwords nowadays - are becoming part of all this. When you start an odyssey, you know you are going to have a lot of encounters such as opposition, supporters, surprises, breakdowns, pains, politics or the simple move from insight to execution, which could all kill a project with the best intention. And this is why I do not foresee the future of business coming exclusively from tech groups or AI...”

The Stanford instructor considers that a new frontier is visible in this field. Everybody can mine a data set, learn how to connect to data sources and run basic queries. But the essential point he makes is that for business intelligence to evolve (and especially cloud BI, the basis for collaborative interactive analytics) is to enhance the user intelligence -  to make the users understand which sources to choose to connect, to blend, to analyze together, to ask better questions and to solve real business problems. That is, in his words, to move out from the equivalent of our solar system to the realms of our galaxy.

And, because we have already signed up for the odyssey, I challenged Gauthier to a classical imagination exercise but with a twista twist; if he was on an abandoned planet, would he rather have a survival kit, or a Big Data dashboard about the parameters of the planet?

The suddenly transformed space explorer smiled and answered without hesitation: “I would definitely take the dashboard. And let me tell you why. People have 3 types of intelligence quotients - the IQ (the intellect), the EQ (the emotional intelligence one) and the CQ (the curiosity quotient). I consider that the CQ is one of the strongest points of my personality. Therefore, with a Big Data dashboard, I believe that I would have even better chances to mine data to find new ways to survive than just with the kit.”

So there is only thing left to say, I believe. All aboard. The odyssey starts here.