26 December 2017
A new force for business success
More often than not , we are struggling so much with day-to-day activities we miss the obvious . Many of us create dashboards to carefully watch certain activities within our businesses . These dashboards are typically updated monthly or quarterly for management review meetings . In some cases , dashboards are close to real time , so certain activities can be watched and scrutinised instantly .
People who need to make decisions love data ; for that matter , we all love data to help steer us in the proper direction . The skilled executive knows how to weigh the balance between too much or too little data versus to wait or jump in and finalise a direction . With all this talk about artificial intelligence ( AI ) and machine learning ( ML ), does it make this decision versus time process easier or harder ?
AI conjures up thoughts of robots taking over the world . However , in reality , it helps us sift through huge data sets to correlate or find a specific trend or usage requirement . As people , we provided the data sources and the output requirements . AI is not new ; think back to your school days of curve fitting to find an equation based on specific data points . The difference now is big bata coming into the picture . There is so much data and as mere mortals , even with some computing power , we do not have the resources to sift through the data . This is where cloud comes in . By using hundreds of thousands of nodes around the world , the possible potential of raw data analysis is more than just amazing — it is difficult to comprehend .
Analysts and expert consultants are often called in to provide more inputs into the decision-making process . Will they be replaced with AI ? Certainly not . They know how to “ tease out ” the necessary data for specific requirements . This is exactly where people come in .
We use ML and AI to understand some specific trend , automate a task or find out what we do not know , so we can have new inputs into our day-to-day tasks . Ad-placement engines are the perfect example of high computational requirements based on a series of inputs . Years ago these were pretty simple — ad length , budget , audience and spot availability . AI engines were created to correlate this to place millions of spots every month . Now , with so much personal data being collected and the industry going beyond a one-to-many linear model , ad placement is more complex and it is cloud computational technology that makes this work .
There are many more practical uses for AI and ML for our industry beyond ad placement . Start with some simple tasks you do monthly or quarterly . This takes us back to dashboards : Do you need to review all the dashboards , or can you simply train a machine to know what you are looking for ? How do you correlate between the different charts and graphs ? Then let the machine do the work and provide you with the results . In many cases , you will be able to automate these outputs to react and change workflows automatically , or initially by flagging you before the algorithm reacts .
Seems cool ? It is . Does this put you out of a job ? Certainly not — this allows your organisation to focus more on how to adapt to changes , trends and needs rather than focusing on day-to-day activities . The bottom line is , we have been using various forms of AI in our industry for years : ad placement , archive , special effects , automation — even adaptive algorithms in compression engines . Now with the immense amount of computing power available , AI can benefit every phase of the content chain , from production and post production to management , monetisation , distribution and delivery .
You need to take the leap and learn how to capitalise on AI and ML to grow your business . ��
STAN MOOTE CTO , IABM ( International Association for Broadcast and Media Technology Suppliers )
Stan Moote has worked worldwide in the industry for over three decades and has a clear understanding of technology combined with a solid business twist , and he is also an APB panellist . You can read many of Moote ’ s articles at theIABM . org .