17 YOUR TAKE Datazoom
As the streaming industry became more sophisticated, the number of services, dashboards and amount of data morphed into today’s debacle: each OTT provider must log into multiple dashboards, viewing metrics which cross-over between mismatched systems and reducing the usefulness of data, in terms of end-to-end optimisation, to effectively nothing. What good is knowing about buffer affecting a video stream if we can’t determine whether the encoder, transcoder, stitcher, CDN, transit network, ISP, third-party-embedded- service, or a setting on the video player itself caused the issue? GET FOCUSED Looking into 2019, every company should have a new focus: using data to improve video operations. Here’s how you can start: 1. Standardise data: Not data roll-up or aggregation, but cleaning. A ‘play’ event from your iOS player will have a different raw data read-out than your HTML5 player. But they represent the same thing. At Datazoom we have our own Video Data Standard which you may consider leveraging. 2.Data must be available in one place: Think data lake. Data is less useful when left in silos. 3. Data needs to be assembled: How useful is collecting player data, CDN log data, encoding log data and ad server
data if you can’t identify the interplay of one service with another? Less useful than understanding their correlation by aligning all of them together. formatted, in the right place, and in the right context, how do we turn it into action? If you’ve purchased outside services, those are your data’s new stakeholders who must have access to this data in order to adjust their systems on your behalf. 4.Data needs to be put to work: Once we have the data, properly As OTT enters its next industry-wide iteration, data which ties vendor technologies, departments and business units together will allow video distributors Otherwise, the business of video will stay more art than science, and the question of how to reliably, and profitably, stream video at scale will remain unanswered. * “Cisco Visual Networking Index: Forecast and Trends, 2017–2022”: https://www.cisco. com/c/en/us/solutions/collateral/service- provider/visual-networking-index-vni/white- paper-c11-741490.html to finally compete, leveraging the technology’s full array of benefits.
WHILE SERVICES MAY BE PURCHASED INDEPENDENTLY, THEY DO NOT TRULY OPERATE INDEPENDENTLY provisioning the collection and integration of data in and between these systems, across the technology stack. The difference between Netflix’s approach to data versus the rest of the streaming industry lay in their vision for data’s role – supporting the growth and optimisation of an end-to-end network. Even when Netflix purchased outside services, they maintained focus on that service’s ability to be included within an end-to-end system. It was essential that each new service be monitored and adjusted in the context of all other systems in the network. This realisation – that while services may be purchased independently, they do not truly operate independently – guided Netflix toward their holistic approach to data. The linchpin of Netflix’s data strategy is their ability to collect data from any one source and then establish context, correlations and causation amongst any coexisting source. DUMB ABOUT DATA New entrants trying to catch up with Netflix raised cash and loaded up on independent, best-of-breed technologies. Taking the new service live took precedence, while long- term strategies – like how to gather, use and incorporate data – were deprioritised. Collected data was used for individual service monitoring and often provided by vendors. As new technologies were adopted, more data and more dashboards entered the picture, all lacking context, correlation and the ability to determine causation from one service to another. Data was used as a way to monitor the performance (and the investment) of each service individually. Each service came with its own dashboard (and services that lacked dashboards became filled by yet more outside vendors, like QoE analytics for video players), and data turned into metrics which were specific only to that service.
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