As mobile advertising specialists, we depend on the work of our data scientists. They're the ones who figure out exactly what our data can do for us, by applying algorithms that unlock immense value for us and for our clients. But what exactly do data scientists do? Does their day consist of diving into deep blue oceans of data, swimming among shoals of algorithms and algebraic coral? Do they pick away at a rock face of solid data, to find diamond insights compressed over millions of years? Do they surf data? Do they juggle it? Tame it? Ingest it? Given …

Big Data is more than just a physical presence on a server. It requires a change in thinking, as comScore chief Gian Fulgoni hammered home in a recent blog post. If your marketing organization isn't prepared to accept and leverage the Big Data that's accessible to your business, chances are your competitors will. So how does one prepare for, and align their organization to be more data-driven? As with many challenges, the first step is identifying the roadblocks before considering a solution.

It seems the notion that silence is golden has lost its luster. As an industry, a collective collaborating to solve thorny measurement issues, we have not been tooting our horns recently. There has been much progress on the development of standards and testing needed to institutionalize a viewable impressions currency and then some.

Madison Avenue today is a study in contrast. The media ecosystem has become so complicated that, just to get a general idea of what is happening in digital, social, set-top and mobile requires supercomputers parsing Big Data terabytes. Yet for many Mad Ave residents, advertising is still a simple two-way street.

One of the most complex challenges facing marketers is trying to measure and understand the impact offline or online marketing has on purchases, regardless of where they take place. A classic example of this challenge is the difficulty in understanding the value of direct mail: Does it influence a customer to go online and make a purchase there, or ultimately in a store? How can one track it accurately? To truly understand the impact of the entire marketing mix, businesses need to understand what advertisements those buyers were exposed to before making a purchase.

In the past I've written about how the implementation of a marketing attribution solution forces certain changes to be made within an organization. These changes are well worth the effort, given the significant (15-30%) increase in return on marketing spend that organizations typically achieve through the use of attribution, not to mention the multiple non-attribution-related benefits those changes provide.

There is an explosive hype focused on leveraging an advertiser's customer data to enable better digital media targeting. Several approaches have sprung up over the years that attempt to individually match customer level data to cookies for targeting ads. These processes include matching registered users through either a persistent cookie or an email address; some simply match a customer's personal information (PII) with permission. The match rates are not great! Some advertisers have reported match rates as low as 10% with only 50% accuracy in identifying gender. With low individual match rates exacerbated by inaccuracy, advertisers should start to rethink …

There's an old joke about a man searching for his car keys at night under a streetlight instead of around the car where his keys were ostensibly lost. When asked by a passerby why he was searching there he replies, "Because the light is better under the streetlight!"