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Acing analytics with AI

by Adrian Pennington on Oct 5, 2017

Limits of training data

To be useful, however, you first need to collect lots of data, know how to use it, and have a purpose or strategy in place. The industry is quite immature when it comes to the use of data-driven insight and processes, but a lot of experimentation now seems to be underway. Netflix and Amazon, for example, already use data extensively to commission original programming and curate ‘discovery’ based on user feedback. 

“Media applications and infrastructure, in general, don’t emit enough useful data, and their control systems and interfaces are designed for people rather than machines,” says Plunkett. “Without enough data, machine learning can’t be effective, and unless we can control systems with machine-friendly interfaces such as APIs (application programming interfaces), then we limit our ability to increase automation and insight.”

Hodges agrees: “The biggest drawbacks today are the lack of existing data to feed the AI engine, and a lack of understanding about how the AI engine can help.”

Rajeev Dutt, CEO and co-founder, Dimensional Mechanics, goes further.  He calls most AI “stupid” because machines are typically trained on narrow pieces of data. “Anything outside of that dataset and the machine can’t understand it,” he says. “A lot of times the project will fail because the training does not encompass broader data sets.”

A neural network trained on a dataset of faces, for example, could have built-in bias because it is seeing only the majority of faces belonging to one ethnic group. 

“You need a greater level of specialisation,” Dutt says. “An AI for Fox News will not necessarily work as accurately with an MSNBC audience, and vice versa.”

Bullett advises that media organisations will need time to adjust to AI/ML, and he expects some organisations to be very cautious at the prospect of machines ‘freeing up’ staff time, as the finance team evaluates cost-saving options in this space. 

“However, ML/AI does not need to be about resource reduction, and can be used to provide powerful tools for operators looking to make better use of data, for example segmentation information or data to improve quality control,” he says.

Media companies are advised not to rush to embrace machine intelligence but to instead spend time learning the basics and discovering how it might help to improve business decisions and performance. “They should then experiment to find practical implementations that work for them,” adds Plunkett. 

“This is best achieved with external help and, depending on the results, they will probably need to hire experts in data science, data engineering, and so on. One word of caution: embracing data-based insights and operations requires a mindset change, ongoing investment, and can be met with significant cultural resistance; simply hiring a few propeller heads into a new data science department will not create a data-driven business.”

Such advanced systems will change current practices, but 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. 

“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 that need to be considered 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,” says Trudelle.


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