This post originally appeared in IABM Journal Issue No. 126. Read the complete issue at theiabm.org
If you run any but the smallest media business you have hundreds, and probably thousands, of pieces of technical equipment from multiple approved vendors. Not just cameras or servers, but radio microphone transmitters, portable monitors, lipsync testers and lighting stands. The number of individual items quickly spirals.
These are the tools of the trade, and you depend upon them to do the job. You need them to be reliable and available. But this is a complicated world, and as well as the routine checking and cleaning of each device when it returns to the facility, you have to consider if there are upgrades or software patches to be installed, if there are potential conflicts between application and operating system versions, and when products are nearing the end of their supported life cycles.
If you have a recurring fault with a particular piece of equipment, is this a one-off? Is it something to do with the way you use it? Is it a common problem with this particular device from this specific vendor? And when a fault is reported, how do you ensure that repairs are addressed quickly, and when the device can be expected to be back in service? The media industry has embraced the idea of asset management in terms of content. The status of content is tracked from camera to transmission, with metadata assembled along the way to show how it is edited, that royalties have been paid, and that transmission copies are QC checked. The asset management system will automate much of this, and present the data in the form each user needs. So why not take the same approach to the technology assets of a media business? The logic says that there should be a management system for the technical – hardware and software – assets of the business: a central repository of all the information, with a flexible user interface that allows you to query what you have, how well it is working, and when it will need replacing.
To avoid confusion, we need to give it a different name. Let’s use intelligent technology lifecycle management for now: a single platform that holds all the information on every piece of equipment in your inventory.
The first challenge will be data take-on. Most operations will have at least some information stored centrally, in anything from an enterprise management system to an Excel spreadsheet. Like any new data-centric system, getting it started with the right information is critical, but loading that information can be seen as the sort of dull routine that never makes it to the top of the to do list. As much data entry has to be automated as possible if it is going to succeed. Once ingested, the information should be converted to a common format. That means you can see what you have and what is happening through a simple user interface.
But just putting the information you already have into a new database does not gain you much. The system then has to fill in all the holes in that information, looking for data beyond the basics of model and serial number. By linking to the support databases of vendors, the system knows the status of every model and software version. Manuals can be added to the database and directly accessed from the same user scheme.
Where upgrades are available, the system determines if there are new versions or recommended patches for stability or security, then makes recommendations on how to proceed. Upgrades and hardware changes can be scheduled. Next, it would be good to incorporate maintenance into the same environment. So the system should support tickets and service tracking. That is not just to reduce the number of screens an engineering manager needs to look at: it means faults are tracked across the enterprise.
If you are a multi-site operation, it is always challenging to identify common problems if something happens only once or twice on each site. Problems with major pieces of equipment will obviously be talked about, but recurring problems for smaller, but no less critical, pieces of technology can be hard to identify. Finally, this database would also track utilization down to the individual asset, with a dashboard view of where bottlenecks can potentially occur, and where you are well provisioned and do not need to invest further. Again, in multi-site operations this can lead to equipment being moved to where it will be more productive. This is just one area where AI can be used for specific decision making, to achieve optimum efficiency. Beam Dynamics is pioneering an intelligent technology lifecycle management platform, designed to bring engineers and business managers onto the same page. You can see at a glance where you have end of life or redundant equipment that can go, and where you need to make fresh investments, you can do so with solid information behind you.
As an industry, “total cost of ownership” has become business critical, without necessarily having the data to know what that cost actually is. In an era when there are so many moving parts in the technology estate – transition to IP, software-defined architectures, the shift from capex to opex – having accurate, reliable and actionable data is vital for forward planning.