Collecting usually takes place in an outdated environment of a system which worked in the past… – the time of storing data mainly used in tech support when services stopped working. In that period, a company searched through files of a client’s history only when a problem arose. They did that in order to diagnose it, and the moment the case was solved, the files came back to their places. Today, if big data collection limits itself to such operation, that means only one – a problem in itself.
Predictive data analysis begins with smart collection
The smart one would say: “collect and analyze”, but with these words any big company will have started counting the operating costs of processing such amount of information. Then again, the reluctance to implementing the new comes from working in the old system. That is one of business areas which is very often supported out of necessity, so, by default, improving it does not add up to anything – at least at first sight.
Imagine having a software solution which elicits metadata from your current mess. As a video provider or operator you probably have several services like: internet, TV or mobile. Those generate three sources of data requiring different analyses. The software, thus, with its smart interface, breaks down all information into categories of the devices which released such information, chronology and types of users. Along with the past system design, it can be done thanks to integration: a data collecting system with an analytical tool. Today’s software houses offer integration to be done by proper programming without necessity of constant engineering support or sustaining any sort of hard end.
However, again, proper storing and collecting is just one side. The other is analysis. In these days, companies must stop looking at data from the perspective of services: working? not working? The data game takes to the subscriber’s side. Therefore, the analytical tool goes beyond, to statistically draw dependency of the clients’ behavior in order to predict choices and moves of the others. Clients use several services of the same provider and a proper analysis can show how they consume the content (from mobile, TV or computer desktop?), where, when and in what setting – maybe they prefer watching news at the end of their binge watching session?
Moreover, all the above has to be done on the fly and real-time. Processing any data through several company departments puts many on-spot decisions off when it is actually too late to react. One interface gathering all the sources and casting all metadata helps to make immediate decisions right after a change comes. Instead of engaging a team of analyzers, an operator or provider needs just one or a few people who can read the interface and make decisions to implement problem-preventing solutions.
UX and UI of data management
That end users need a well-designed UX and UI has been known for a long time. The UX an UI of a provider or operator, who stands before a waterfall of data, very often remains in the dark corner on a party where everyone else is having fun. But one needs to find solutions in operating data to be able to draw conclusions quickly. That is why an advanced and automated interface is equally important as the infrastructure of collecting and processing lies below. Data is to be useful. Collecting them is no longer a dull chore.
Intuitive interface and smart user experience allow to predict the same occurrences at other clients’ instances effectively. But not only. First and foremost, the dashboard of such an interface should inform the operator about the load of the infrastructure: Does two providers use the same storage? Thanks to peeping into their deeds, the company is always ready to distribute balanced servicing of the users on not one but two systems.
Moreover, the distribution might also occur within the content. When viewers watch a new released show emitted in conflict with another one lasting on a parallel channel, an easy operation of rescheduling the first or second one can profit abundantly. All is centered around smart and insightful following what our viewers do and what they want to watch.
Predictive data analysis automates business
Automation treads here inevitably and it will not skip over the video industry – or maybe especially over it… Moving on the same smart solutions to the clients with a similar problem and occurrence characteristics is becoming a standard. At this scale, no company is going to handle such mass of calls, requests or returns effectively – bearing in mind the gold rule of the client-centered business. Predictive analysis, above other facilities, shortens the path of reaction opening the one to become proactive.