What Big Data Means to Me

AlyssaWhen I first joined YarcData in September 2012, I was fresh out of grad school and new to the tech industry—well, let’s face it, new to the workforce entirely.

I have learned more here than I did in my five years of college. Big Data was a foreign concept to me, someone who spent her time studying Latin literature, not SPARQL queries.

But even as I have familiarized myself with the world around this buzzword, Big Data was never about numbers to me. Working as a product marketer of YarcData’s Urika appliance taught me that analytics is about relationships—how people build social networks, patients react to treatments, and athletes perform better in games.

We all want to do better business, cure more diseases, and catch more bad guys. Big Data is more than the racks in our data centers. Instead it’s about how we use technology to achieve our most essential objectives and solve the most complex problems.

Sure, metrics are important. I certainly don’t want to discount that point. But as much as I love managing YarcData’s social media accounts, all the likes and retweets on Earth don’t amount to anything if they don’t serve a greater purpose than lead generation.

The quality of content that I produce has always been a higher priority than my number of followers, whether I’m writing corporate articles or book reviews for my personal blog. Big Data has become such a bandwagon that we’re all going deaf from its noise, so it’s to up to us to ensure that the information that is being spread doesn’t just regurgitate common talking points but provide real business value.

And although one could argue that my posts on the role that graph analytics could play during wine tasting or the Super Bowl are more fanciful than useful, my response is that looking at data in new and imaginative ways is precisely how we drive innovation.

The folks at YarcData encouraged me to incorporate fun facts about major holidays or online dating into my writing, because they understand the importance of fostering a creative, collaborative community of colleagues (not to mention, forgive my obsession with alliteration!). When the passion of your team reaches petabyte or even exabyte scale, you can get more done, in less time, and actually enjoy doing it!

This is why I’m saddened to write that this will be my last blog for YarcData. I have appreciated my time here immensely, and I will miss everyone whom I had the pleasure to work with. I have made lasting bonds and been part of countless memorable experiences, both of which have meant more to me than I could fathom.

If Big Data is about people not numbers, and relationships not rows and columns, then YarcData is amazing for showing me that. I wish all my colleagues the very best in the future, and I hope that no matter where we end up, we will continue to cheer each other on.

So what does Big Data mean to me? The simple fact that it always has been, and always will be, bigger than data.

I wish the best for everyone at YarcData, especially this amazing group of women who share my obsession with shoe shopping! You’re all awesome!

Finding the Perfect Wine Pairings with Graph Analytics

AlyssaIf there’s anything I have learned at YarcData, it’s that graphs are everywhere. Some are obvious, as seen in social networks, but others take a bit of creativity to comprehend.

Case in point: As a Bay Area resident, I love heading to Napa for wine tasting. On a recent trip to celebrate a friend’s birthday, one particular winery gave us an excellent lesson on how food like cheese and chocolate enhances a varietal’s flavor. We also learned that the coffee drinkers among us preferred red over white, because they were more accustomed to bitterness. The tasting demonstrated that palettes are more nuanced than we thought and that what we enjoy drinking is heavily influenced by what we pair our drinks with.

Overwhelmed by all the combinations that could exist between various food and drink, I decided to investigate whether any researchers developed this information into a graph. I discovered that in 2011, researchers Yong-Yeol Ahn, Sebastian E. Ahnert, James P. Bagrow, and Albert-Laszlo Barabasi, published a paper about the ‘flavor network,’ which they constructed by combining a list of flavor compounds and three online recipe datasets (two from America, one from South Korea). Since then, they have added further datasets, a process that is much easier and faster when using graph analytics technology.

Scientific American discussed their findings, and created an interactive graph of the flavor network. Each node represents a different ingredient and the edges are the connections between them in North American and Eastern cuisines. The size of the node correlates to that particular ingredient’s prominence in the pool of recipes.

The results? Ahn and his colleagues found that the food pairing hypothesis, which states that ingredients of shared flavor compounds go well together, rings more true in Western cuisines. Eastern cultures tend to prefer contrasting flavors over complimentary ones. This is why American recipes frequently include milk, butter, egg, and wheat, whereas Asian recipes employ flavors that pack more of a punch, like cilantro and soy sauce.

The flavor network is more than figuring out that bleu cheese and dark chocolate make an excellent pairing. It holds a wealth of knowledge that can be applied to everything from personal diets (What’s a delicious vegan alternative to honey?) to corporate menu planning (What ingredients will make my salads most profitable?).

Moreover, the impact that can be derived by clustering entities by attributes is not restricted to the food and drink industry. By using graph analytics to discover relationships between clusters, financial institutions can target their VIP investors, sports teams can optimize their lineups, and retailers can offer more personalized recommendations to their customers.

As for me, I can’t wait to head back up to wine country and put the flavor network to the test—in the name of graph analytics, of course!

Sochi or S—Oh no!—chi?

AlyssaI love the spirit surrounding the Olympic Games; in fact, I’ve been fascinated by their cultural significance ever since I learned about first Olympics in Ancient Greek history. A sporting event so important that the ancients dated their calendar around it!

For better and worse, a lot has changed since 776 BCE. Today marks the Opening Ceremonies of the 2014 Winter Olympics in Sochi, Russia, and as much as I would rather enjoy the festivities, it’s clear that this Olympics has been tarnished by controversies as serious as the violation of human rights and the mass killings of stray dogs to as laughable as missing doors on hotel bathroom stalls.

If anyone claims that the Olympics isn’t one of the biggest big data problems right now, then I say that he’s dizzy from too many luge runs. Here’s a snippet of stats on the 2014 games:

  • 98 events in 15 winter sport disciplines, including new competitions like women’s ski jumping and snowboard slopestyle
  • Over 2,800 athletes from 88 participating nations
  • A budget of over US$51 billion, the most expensive Olympics in history

Thus, data will be collected and analyzed from a plethora of sources—athlete performance, social media engagement, financial transactions, and security surveillance, to name a few. Security is, of course, the most pressing concern in Sochi, since the location is such a prime target for terrorist activity. Just today a plane landed in Turkey due a bomb threat by an alleged hijacker intent on flying to Sochi.

YarcData has demonstrated that all of these big data issues are best represented as graphs so as to analyze not only the data points, but also the relationships between them. This way, Sochi can provide real-time updates on sports stats, prevent fraud and identity theft, and protect the lives of everyone involved in the games.

So here’s to a safe, exciting Winter Olympics! And to quote from a tale about a much more morbid games, “May the odds be ever in your favor!”