Today the media and broadcast industry is in a true state of flux. Long established service companies are struggling or in administration. Every week there is a new acquisition or merger and the role and functions of systems are converging as they try to retain their market share. Additionally, content owners need to generate new revenue streams and require dramatically reduced costs to process and exploit their content. This sea change presents a challenge for all companies but especially for those vendors providing Media Asset Management (MAM) systems as they are being forced to adapt to retain their client base and competitiveness in the market.
The impact of the 2008 financial crash on spending cycles affected how media companies used traditional MAM’s and their ability to shift the way they worked. The shift was often limited by either not having the relevant technology or simply lacking the necessary functionality and flexibility in other core systems. Traditionally, a MAM is still viewed as a core technical component of the media, broadcast production and distribution process. However, to address these new industry demands a MAM’s focus must now extend to offering a multi-functional system which spans from contractual data to finance to operations to technology and also support extended business and commercial user communities.
As an example, the next generation MAM must support configuration driven workflows, managed via a business process management function, in order to simplify implementing changes. Practically speaking, most traditional workflow code is embedded in the main product. However, a move towards metadata driven workflows is now required. For flexibility, branching of workflows should be quick and simple, defined and controlled by business rules and thus requiring only token regression testing. This approach minimises or even eliminates the need to manually re-write workflows or processes, dramatically reducing costs and speeding change. As importantly, it also moves the control to the business.
As the media industry adapts to the changing landscape, the demand to drive down processing costs and increase profits becomes paramount to remaining competitive. Additionally, the flexibility to onboard new workflows, platforms and clients within hours / days is critical not only to keep costs down but also to ensure that new revenue streams can be secured. The ability to respond quickly to changing customer requirements and shifting market demands is now a key requirement for any next generation MAM.
This represents a significant and perhaps challenging change for the traditional MAM vendors, all of whom are moving towards increasingly automated processes but typically are still encumbered with heavy weight deployments where implementation and change is not easy (nor fast). For most MAM systems, any workflow typically incorporates numerous manual steps which are the ‘glue’ that holds it together and often acts as an inter-connect to other workflows or processes. These systems tend to be highly customised, heavily technical and often riddled with extensive bespoke workflows with all or most logic buried in code. These broad limitations, coupled with limited flexibility and significant degrees of vendor ‘lock-in’ (and cost to change), form the fundamental issues which must be addressed in the development of the next generation of MAM’s.
The next generation MAM must be lightweight and able to easily integrate with any platform component with business rules and workflow logic sitting outside the integration logic. While today’s MAM’s have come a long way in enabling media companies to locate, organise, distribute and archive content, a great deal more is achievable. Equally, advancing technology will unlock the full potential. The development of enhanced AI driven metadata processes is crucial for ‘labelling’ and cataloguing content for exploitation purposes.
Three Media has been working closely with media companies and a top-flight university to develop and advance methods of searching the content itself. This has led to employing AI and ML functionality in a highly automated fashion to assist with identification and validation of content and its metadata. However, there is some way to go before it adds real value to the business, but there is a level of confidence that is achievable and consequently the MAM must be able to support the complex data schemas and management of the ever larger data sets to support it.
And here lies possibly the greatest challenge for the MAM vendors. Many of the traditional MAM data schemas are built on the foundations of a ‘video tape’ concept, essentially a physical media model, which limits the ability to introduce virtualised hierarchy layers and then to further extend to support multiple layers of metadata extracted from content, or supporting files, or scraped from industry web-sites. The extension is significant as changes will be required within the data schema from the ground up but must also be supported within the UI, all the time ensuring the system remains performant.
Professional services companies like Three Media play a pivotal role in assisting media companies with how a MAM can be used to drive the business and how it can be extended to deliver all these new capabilities, always striving to maximise content exploitation with reduced costs and improved timelines.
Functionally, Three Media believe a MAM must be able to analyse its workflows and processes to identify future resource issues and requirements, both technical and human. Iterative optimisation ensures that platforms are configured and operating at optimal levels, and associated costs are controlled and minimised. We believe this capability must quickly become a key feature for any MAM.
From our experience of working with broadcasters and content owners on defining and implementing MAM systems, we have progressed the development of our own commercially available media management toolsets. This began with the XEN: range of media management tools, which was re-built from the group up, evolving into a new product bringing all our capabilities into a common application. XEN:Pipeline was launched at IBC 2019 and is aimed squarely at addressing next generation MAM demands including ‘big data’ mining, analytics, ‘codeless’ workflows, extensive automation and integrated business process management.
Next generation MAM’s and metadata management tools like XEN:Pipeline must rise to all these challenges to “stay in the game”. The next 12 months will be interesting to see who is successful and who is best positioned to adjust to the next set of demands the industry throws our way.
Originally featured in the TVBEurope November/December 2019 Magazine