Exploring the ocean of T&M data

By Simen K. Frostad, Chairman

Published in InBroadcast March 2015

It has been said that knowledge is like a pool of light: the larger it is, the greater the circumference of darkness that surrounds it. Or, the more we know, the more we discover new things we don’t know. Then, as someone else put it, there are the known unknowns, and the unknown unknowns. We are aware of the darkness at the edge of the pool of light, but not what lies further in the shadows behind the edge.

So our exploration of the unknown almost always proceeds in a way that is defined by what we already know. Only occasionally does a flash of intuition descend on a scientist or inventor, so that some new way of thinking emerges, and with it the freedom to find new knowledge. Some of the most important advances in science have occurred this way.

But empirical testing has been the cornerstone of scientific thought for centuries, and even the most breath-taking intuitive leap must be tested against existing understanding of reality if it is to be proved and become accepted ‘knowledge’. Science exists therefore in a balance or dialectic between the imagination of the scientist, and empirical testing to determine whether or not the theory is valid.

In the world of test and monitoring for digital media, the conventional model is heavily weighted towards the empirical. The industry uses T&M technology to watch and test our systems, and we respond to the alarms when things go wrong. We use defined criteria to observe the functioning of the systems within set parameters, and when a variable strays outside those parameters, the T&M equipment alerts us. Many people consider this to be the only thing we should expect from test and monitoring.

A thermometer works in a similar way: it tests for a single defined criterion, and we may use this simple test to observe when acceptable parameters have been attained. But if we want to go beyond simply knowing that today at 12:00 it is hotter than it was yesterday at the same time, we need to consider other parameters. We need to put one test in the context of others, because temperature alone only tells us ‘what is’, but if we want to understand ‘what will be’, and ‘why’, we must have much more nuanced and correlated information.

Even a basic weather forecast takes into account many many factors and sources of information, including air pressure and movement, land and sea temperatures at multiple locations, ocean currents, the development of weather systems in other parts of the globe that will influence the systems in our own area, and so on. With all this information to hand, the meteorologist can analyse conditions based on known precedent, and begin to predict what should happen in the short term.

For a long range forecast and the prediction of larger trends in global weather systems, advanced computer modelling systems are required. The knowledge gained from this kind of modelling helps us decide how we should plan for eventualities such as rising sea levels, increased energy demands, regional famine.

The current T&M model is more akin to the thermometer hung outside the front door than to a long-range weather modelling technology. This is a pity, because an advanced T&M system can gather and correlate an inconceivably huge amount of data, and if the system only applies simple thermometer-type tests to this information, a great opportunity is being lost to understand and predict the future.

In the connected media consumption landscape, there’s a vast amount of data to be accessed and understood by the digital media operator. Every OTT client and set-top box can contribute to a rich data resource for the operator to use in identifying and understanding new patterns of use, emerging market niches, increased competitive stress, and so on. But the enormous volumes of data available need exceptional analysis tools if the data is to make sense.

Complex scientific research makes use of advanced visualisation to pick a path through massive data sets, with the ability to correlate data from heterogeneous sources as a way of discovering new patterns and connections. And advanced visualisation can do something similar in our industry.

What’s needed is the ability to use this treasure house of data in innovative ways, so that it can be sliced and visualised from any angle, with correlations from multiple sources both inside and outside the T&M system, and with the capacity to use the data for predictive modelling.

In other words, instead of the current rather flat-earth T&M model, which is tilted towards reporting on what we already know, the industry should be demanding a model that allows us to explore what we don’t know, and to hypothesise using the data the system gathers. Instead of being confined to showing a red light when a problem has already occurred, advanced T&M can become a tool for navigating an ocean of information, identifying hazards looming on the horizon, and striking out for opportunities and profitable new destinations beyond.

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