The data ambitions to an understandable strategy

Updated: November 16, 2017

We turn data ambitions into a coherent data strategy. We help the world’s leading brands and technology companies refine their data “wants” into a roadmap, working collaboratively to identify what they already have, what they need to have, and how to begin implementing this.

OTT competition

How can data solve the sports viewing challenge

If you believe everything you’ve seen written, sport on TV – often considered the last bastion for “traditional” TV in the face of more and more OTT competition – may be in trouble. At Dativa, we’re still bullish about sports. Let’s not forget – it’s still true that almost every successful MVPD has built its audience to a greater or lesser extent thanks to living sports, and millions of us still tune in to watch peak sporting events. But there are issues. We’ve had it put to us that amongst younger generations, there is perhaps a tendency to get updates on the ‘important details’ by watching the goals in the highlight clips, seeing one player’s heinous tackle in GIF form and following the Twitter storm surrounding the referee’s appalling offside call, rather than watching the whole 80 or 90-minute event. Piracy is also certainly an issue, with the illegal re-streaming of live sports beginning to affect viewing figures. But this in itself is a complex situation and may be financially motivated.

Understanding multi-modal consumption

For an operator, it does matter how people watch their content. The mix of viewing between living, PVR, VoD, and OTT, and how that evolves, is illuminating. It provides them with insight around. At Dativa, we work with operators all over the world, in different market positions and with very different service propositions. This gives us a unique vantage point to better understand how viewing might evolve from a life to a multi-modal model, and how those dynamics might differ around the world.

Managing the risks of re-identification

We all know that data security is essential, but are we doing enough to protect our data feeds? In 2006, Netflix launched a competition to improve the accuracy of their recommendation algorithm, releasing an anonymised dataset of 10 million consumers move ratings. The following year, two researchers at the University of Texas, Arvind Narayanan, and Vitaly Shmatikov published a paper about the Netflix Prize dataset. They’d taken a look at this data and developed a methodology to break the anonymization of this dataset. Their work leads to a class action lawsuit against Netflix for breach of privacy, including a high profile case of an “in the closet” lesbian who claimed to have been “outed” by the Netflix dataset.

The big issue of re-identification

The biggest problem with re-identification is any PII is regulated differently to other data. If we are sharing data between parties and we cannot prohibit re-identification then we cannot guarantee the security of our consumer’s data or that the uses of the data are going to comply with the terms and conditions we agreed with our consumers when we collected their data.