The entire process of reaching the right viewers with the right content at the right time in the most efficient and cost-effective way has become so complex in today’s multiplatform environment that you might wonder if the human brain can still make sense of it all. The nagging feeling may creep in that content inventory and internal resources are not put to their best use.
To make well-informed decisions, we need high visibility of data. For instance, we obviously want to keep a keen eye on how scheduled content is performing. But we also need to gain a well-founded understanding of how our content in stock will perform. That is because we will want to make budget simulations or ascertain what content we should acquire to complement our stock. And if we gain detailed insight into how well the content we are looking to acquire or produce will do, we will know how much we want to pay for it.
To get all this right all the time, our brain needs all the assistance it can get from data.
Unlocking data across the content supply chain
There is a lot of data around, but all too often it is locked and hidden in silos. To be useful, data needs to be findable, interpretable, and reliable. That requires a data catalogue, governance, quality control, and a centralized data platform where all the data is available to the organization based on permissions.
Data will come from the BMS and other places in the ecosystem, such as the ATS and the MAM, but also from external data providers and open data sources. For all these systems to understand each other, the data needs to be standardized into a well-documented canonical data model and exchanged through stable Business APIs.
Connecting the dots into actionable insights
As soon as we have all that clean qualitative data available in a data model that we can easily understand and interpret, we are ready to connect the dots and generate actionable insights.
As soon as we have all that clean qualitative data available in a data model that we can easily understand and interpret, we are ready to connect the dots and generate actionable insights. By plugging BI and advanced analytics tools into our data platform, we can produce an unlimited number of ad hoc reports. We can also generate nifty dashboards that visualize how to optimize the content supply chain and pinpoint the workflows where we can effectively make the desired adjustments.
Feeding augmented intelligence
Based on the insights from the dashboards, it is possible to discern which factors influence the parameters that need to be optimized. This can be used in a machine learning model that controls AI workflows. An ML model can, for instance, learn to accurately predict viewer ratings for every relevant target segment.
And now it gets even more interesting. We can use the predicted viewing figures for other AI applications, for instance, to automatically schedule content. All it takes is applying filters and specifying which value criteria have priority. While the planners can always finetune the instantly generated schedule, the algorithm will keep learning which choices result in the best schedule.
Like a flywheel, it keeps optimizing the data, the data strategy and the operations.
As augmented intelligence produces real-life operational results and from this data we can generate actionable insights that in turn feed augmented intelligence, the self-reinforcing cycle is complete. Like a flywheel, it keeps optimizing the data, the data strategy and the operations. That is, as long as the data flywheel is applied to a truly connected content supply chain where workflows are digitized and the data from all workflows is centralized to enable end-to-end, data-driven management.
Lucas Denys, MEDIAGENIX Data & Integration Product Manager
Learn more about the data flywheel and smart content lifecycles as you watch the recorded webinar hosted by Lucas Denys and Jens Costers.
