A Guide for Building a Data Architecture ready for Real-Time Decisioning and Automation
Originally posted to LinkedIn | June 8, 2018
Over the course of the nearly twenty-three years since the dawn of the streaming media space, beginning with that fateful Yankees-Mariners game in 1995, the industry has grown substantially, evolved its strategic approaches and technologies utilized, and then re-evolved accordingly to keep pace with the demands of the next generation. Today, online video commands 20% of total streaming hours, and counting.
What was once a concerted fight for legitimacy, the streaming media industry has transitioned into a quest for efficient and effective business development — with the biggest challenge now shifting from distribution to achieving sustainable margins. Thanks to advances in technology, audiences have benefited from a wider range of content, available to them at any time, in any place, and on any device. Although this has led to a paradigm shift in consumer expectations and the disruption of the linear television market, the business of streaming media has not caught up to its predecessors, with streamers struggling to monetize their content.
The challenge is two-sided: Content distributors need to increase demand for their content and grow earnings coming from online distribution (revenue), while at the same time they need to get more from less, and build greater efficiency and stability into the resources they are working with today (COGS). Despite falling price points for some key systems (like CDN), we have not evolved how we handle the dependencies, fail-overs and trigger points for how critical infrastructure and peripheral systems should work together to maximize potential.
We’ve reached a point where solving the latter challenge actually helps us kill two birds with one stone. If we want to build more profitable streaming businesses, we need to upgrade our core infrastructure technologies, not by replacing any one system but by creating technology glue that holds all of these systems together. Video distributors need a fundamentally different approach for harmonizing interactions between services, providing greater flexibility and ability to self-heal when failures are detected.
What is Adaptive Video Logistics?
Adaptive Video Logistics (AVL), is a new category of software for the streaming media industry, offering better data collection pipelines and integration for content distributors. Datazoom is pioneering this software category, with our patent-pending technology that is uniquely able to pull data from any software delivered environment (i.g. a webpage or video player framework) using “Collectors” — and push high-frequency, sub-second data to various software tools and destination(s) — what we call “Connectors”. We worked with some awesome companies to join our growing ecosystem at Datazoom.
So far we’ve built Collectors for iOS, Android, Anvato, Brightcove , HTML5, JWPlayer, & THEOPlayer. We’ve completed Connectors for Amplitude Analytics, Google Analytics, Heap Analytics, YOUBORA Analytics, Adobe Analytics (Omniture), New Relic Insights, Google BigQuery, Amazon Kinesis Data Streams, Datadog, Keen IO, and Mixpanel. New integrations are released every 1–2 weeks.
From Datazoom, which acts as an integration mission control, you can manage pipelines of data moving between Collectors to Connectors without needing developers to deploy new code. In addition, our platform is redefining real-time when it comes to data: we operate with sub-second latency, enhancing the usability of the metrics output by other platforms. But the true value of Datazoom is found in its strategic utilizations.
Three Pillars of Data for Adaptive Video Logistics
1. Efficiency — Simplified Data Collection
Most content distributors use several analytics tools, each of which requires a separate SDK to capture data. There are several consequences for using multiple SDKs: The added weight caused by each script has led to what is now known in the industry as “player bloat.” Furthermore, data collected for each tool not only ends up in silos but adds weight to the payload. But the real implication is the damage to the overall usefulness of data — any attempt to unify data gathered from disparate sources would have to face the challenges of data inconsistency, variability and duplication.
As a solution, the Datazoom SDK ensures efficient and consistent data collection and creates a single source for any data to be routed out, maximizing data utility.
2. Latency — Sub-Second Latency
Real-time seems to have a variety of definitions these days… in video streaming conditions aren’t changing hour-to-hour or minute-to-minute, but second-to-second. And the stakes are high — 2 seconds is all it takes to lose a viewer. For an industry whose conditions can change so abruptly (with significant potential consequences) why do we permit our data to be minutes or even hours behind?
Datazoom is laying the groundwork (specifically the data pipelines) required to usher in a new era of video operations, and can enable content distributors to adapt the ability to make decisions as changes occur, and leverage the limitless scale of AI and Machine Learning to power better video experiences — Automation. However, the strength of a decision (whether made by man or machine) is only as good as the information it’s given. We must begin to see that the value of our data is indirectly correlated with latency — the greater the latency the smaller the value.
We should begin to shift our focus not to only ensuring we have access to the data we need, but with the least delay as possible so decisions can be made as soon as changes occur. We’ve built Datazoom to capture data in less than a second. In fact, we guarantee it under our SLA. We know that this is what will be required to grow the business of online video, and match the experience of traditional television.
3. Utility — Creating the Data Feedback Loop
With a unified dataset being updated in real-time, video operations departments are in a better position than ever to receive meaningful feedback from distributed video. But the utility of data doesn’t stop there — the effort of video delivery requires alignment with critical, external services along the video delivery path, from Encode to Device. Data collected from the video player can be used to improve the hand-offs between these systems that ultimately impact the user experience and business of video.
Datazoom enables content distributors to provision access and distribute data in a way that create a sub-second latency feedback loop for key providers. Since Datazoom can segment data and parse it into various intervals, we ensure that the data we capture can be fit the unique backends of any system, increasing the total utility of data.
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