Customer centricity has been at the heart of Microsoft’s enterprise mission and perhaps that focus in no more important than right now within the media and entertainment (M&E) industry. The last 18 months has certainly driven several changes in media consumption habits along with the processes and tools companies are using to continue to produce new material in a very different way. While many of the changes over this time have been significant, perhaps the one that is most significant and the most persistent is the increasing role data plays in supporting decisioning and how new data entities are being created through these new consumption habits. Over the last 6 months Microsoft and PwC have been listening to the voices inside M&E organizations to help gauge how the data landscape has changed and what can be done to support these companies going forward. In sights and recommendations drawn from these conversations with industry leaders have been documented in a new whitepaper, now available for download.
This process involved interviewing executives, data scientists and strategic advisors focused on the media and entertainment industry. The takeaway from this was that companies are no longer concerned about the ability to generate vast libraries of data. Rather, these companies are focused on the ability to generate insight from this data. Indeed, this was being limited by several consistent pain points.
- Privacy. Consumer knowledge and engagement around the use of data and personal privacy has changed. Add to this GDPR, CCPA and other regulations have made the collection (and storage) of personally identifiable information (PII) more difficult thus making personalization tasks based on purely in house data has become much harder.
- Volume. The sheer size of company data has created storage overheads along with process problems that can now lead to a greater amount of time being used on data collection rather than data for insight.
- Granularity. Companies in the media and entertainment space still want to be able to offer personalized experiences through persona creation but the increasing level of granularity required to generate these narrower and narrower segmentations is proving difficult.
- Quality. In connection with issues around the volume of data needed, there are also the overheads spent on cleaning data so it can actually be usable in any insight generation.
- Unification. Understandably there are many larger M&E organizations who still have problems getting data unified across their whole landscape and federation of data across different platforms can also create downstream implications.
The validation of these pain points lead directly to the creation of the consumer knowledge graph. As an aggregator of cross-company data, the consumer knowledge graph is a creation designed to address these problems directly by focusing on level of insight rather than volume of data. Granted, a concept such as this will require companies to become comfortable with both data sharing and data monetization, however, the benefits of this way of working are likely to make that business transition easier. Once an organization has agreed that value lies in the analytics and models that sit on top of the data rather than the data itself, the benefits of this new model start to become clear.
So, what if you have lots of data but no way of generating any real insight? What if you know your data is incomplete and therefore any insight being generated is based on only a portion of the information available? What if data availability across systems is preventing your media company from taking a single view of each customer you have? And what is the cost of never being able to connect the sentiment of your customers back into your content creation process? This is where the consumer knowledge graph comes in.
The consumer knowledge graph will:
- Help organizations keep data anonymous and build trust.
- Allow ever more refined persona creation.
- Help break down interorganizational silos as data can be fed into the consumer graph from multiple business units with insight then shared across multiple functions.
- Drive a more diverse thought process by ensuring that individual bias is not overstated as data is coming from multiple sources.
- Creates an easier to manage data process as data storage and third-party data aggregation tactics are less relevant.
- Enable a more holistic view of consumers that would not be possible through a data set from a single organization.
Microsoft and PwC are already underway in the development of a proof of concept and conversations are taking place with early adopting companies on how this can be integrated into their processes and built upon. Throughout this process the hypothesis has been “that a consumer graph deriving insight centrally based on multiple cross organizational data sources would not only provide better customer segmentation, but it would also drive new opportunities for the integrated organizations.” While this is a vision for the future, if the last 18 months has taught us anything, it’s that the future may become the present before we know it.
If this is a topic you would like to know more about, I encourage you to read the new whitepaper Consumer Knowledge Graphs for Media Companies, and to also connect with me and other members of Microsoft’s Media & Entertainment team.
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