With NAGRA Insight, operators can collect and connect data sources
Artificial Intelligence (AI) is suddenly all the rage, with the potential to assist in many areas of the industry from network diagnostics to edit assembly. The BroadcastBridge gets under the skin of AI to ask how useful is machine learning today in media today. Simon Trudelle, Senior Director, Product Marketing, NAGRA share his thoughts.
Is there confusion between what is basically data analysis and what is genuinely a form of intelligence?
Simon Trudelle (NAGRA): The evolution of the data analysis solution market is taking different dimensions. First, the use of unstructured and structured data, coming from multiple sources, combined with configurable data science algorithms, allows for the development of powerful prediction engines that bring a revolution to the data analytics and business intelligence market. New knowledge graphs and correlations can be found and refined in real time by data scientists. This is clearly where most value can be delivered in the pay-TV industry in the short term, as the players face a business transformation challenge and need to drive their business using more relevant data that they often don’t have, as legacy TV systems are not always two-way connected. In fact, social platforms and other external systems can offer extensive sources of data intelligence that can benefit service providers and other players in the TV/video value chain.
The second dimension to consider is the development of machine learning predictive algorithms that use a feedback loop to improve the relevance of the prediction engine over time.
Finally, pure AI/ML platforms/applications go further and leverage neural networks (using dedicated chipsets) to create systems that have proven to outperform human beings for repetitive skill-based tasks such as speech or image/video recognition for instance.
How can AI and machine learning be used to help streamline decision making, make better decisions, personalize the UX, cut costs or other benefits within the TV industry?
ST: The range of opportunities for improving business operations in the TV industry is quite wide in the short term as service providers and broadcasters start feeling the pressure from OTT native players and realize they have to deal with a more segmented market, forcing them to become a lot more consumer-centric. And this implies relying more and more on data-driven decisions and processes.
In this context, most service providers appreciate the fact that they have to walk before they run. So the bulk of the effort is on capturing new data sources and rapidly addressing specific business issues through actionable business intelligence and an iterative approach.
At NAGRA, we have developed a solution called NAGRA Insight (announced earlier this year). With NAGRA Insight, operators can collect and connect data sources, create and share reusable analysis, enable actionable outputs with operations for immediate action and measure the impact of these actions on predefined key performance indicators in real time.
Leveraging data collected from NAGRA’s products, the operator’s systems and external sources, NAGRA Insight uses data science algorithms to develop advanced knowledge graphs, create smart predictions and recommend business actions. The resulting KPI-based action loops empower internal teams to better drive business operations. NAGRA Insight contributes to deliver business impact by addressing key service provider challenges such as consumer value optimization, content management, service operations and advertising management.
Are there real world examples of AI being incorporated into TV specific platforms and workflows to achieve particular goals?
ST: NAGRA is working with several pay-TV service provider customers to improve their operations by leveraging data analytics. While not all projects technically qualify as “pure AI” at this stage, here’s an example of an interesting customer case: the approach taken leverages usage data, sent back through an innovative mobile app, as well as other data sources to determine the type and frequency of marketing and commercials actions that will drive subscription renewals/cut churn, based on a predictive, data science driven machine learning model. Other similar business applications, using the same technology paradigm, are being developed with other customers.
What things should be considered when working with AI/ML results?
ST: Such advanced systems will change the way things are currently done. They require supervision and proactive management to make sure key business objectives are reached. Constant benchmarking with other predictive applications is often needed to validate the performance of the new system. So it’s clear that while automated AI/ML technology can help reduce the workload associated with some tasks and improve business performance, it does not take away the other more business-centric angles to consider when deploying any IT-based solution: scoping the business issue, defining the “why? and what if?” questions, making sure the outputs have an actionable business impact.
What training is needed? Do media companies parachute in data scientists?
ST: All pay-TV service providers have used data to successfully run their businesses over the years. What is changing is that new technology and tools make it possible to apply data analytics to multiple, complex problems. And in some instances, service providers can leverage “smart data” that truly makes a difference in the way business operations are run.
Resident service provider business intelligence specialists are already learning new techniques, and data analytics platforms such as NAGRA Insight are designed to let them adapt the provided reference algorithms to the specific needs of their organizations, now and in the future. Overall, what is most important is the focus put on solving business issues that have an impact for service providers.
Other “point solution” capabilities are also becoming available as “AI/cloud APIs” that can be integrated into an existing E2E TV solution without having a PhD in data science – typically a content recommendation capability or speech-to-text/translation. Such an integration brings the benefits of AI without having to master all the details of the underlying technology.
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