MVPD data processing

MVPD data processing

Giles Cottle, Wed 24 May 2017

We have worked with many MVPDs across the world to collect, store, and analyze their viewing data from set-top-boxes, OTT services, and smart TVs.

We have built viewing data processing platforms for many MVPDs covering platforms from Ericsson/Microsoft, Cisco, Verimatrix, RDK based and bespoke data collection platforms.

The main characteristics that we find between all platforms are issues with the quality of the data, especially the EPG data and metadata for VOD, privacy issues connecting to the CRM data, and the complexity of Pay TV packaging.

The architecture we implement is reasonably standard, and we now have sample code to interact with many of the common industry platforms. The conventional approach is collect the data from different devices, cleanse it through a set of learning filters, then storing it in a data warehouse where it can easily be queried and acted upon within the business.

Because this type of data is reasonably well structured, we prefer a data warehousing approach as this allows us to apply a standard schema to the data as we load into the data store. We have regular schemas to facilitate efficient querying across platforms including Amazon Redshift, Oracle Exadata, and SQL server.

TV Data Collection

We build rules to cleanse the data streams and transform them into meaningful viewing data that can be stored in a data warehouse and then activated for multiple applications across the MVPD ecosystem.

Building a data platform is the first step in developing a data-driven organization. Most operators expect to get a return on their investment over the long run as they can data to subscriber analytics and advertising sales. However, we often find there are quick wins to reduce content licensing costs. This is not merely a matter of dropping under-performing content, but also about rearranging the packaging - and thus the fees relating to each item of content. Focusing on which tier-2 content performs best often means that operators can spend less on tier-1 content and cover the costs of implementing a data pipeline within the first financial year.

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