The Move to Knowledge Based Digital Lifestyle Services and M2M

Raw data on its own is pretty useless. The world is already full of it and doesn’t seem to know what to make of most of it. However, there is a fundamental difference between ‘data’ and ‘knowledge’. The world is not full of knowledge (in fact, it is often devoid of it…). M2M devices, and the applications built using them, will generate a lot of raw data. For example, smart-meters and other forms of sensors transmit reams of raw data. This data needs to be examined, parsed, combined, enriched, presented and generally moulded into knowledge so that it can be put to good use. Raw data may also need to be enriched with data from a completely separate system. As an example, if a government wished to make a country more energy efficient. By combining energy usage information with census data, it can examine which demographics consume energy in the most inefficient way and then target those demographics rather than having a blanket approach. This is real, useful, workable knowledge. In another example (implemented by Openet for a leading US cable operator), we examined how raw data from set-top boxes could be converted into useful knowledge. There are a growing number of limitations with the traditional audience measurement methods employed by the TV industry – most of which are difficult, if not impossible, to fix. These stem from inaccurate data collection systems that are typically based on information drawn from a smattering of electronic set meters, polling boxes, or «people meters» connected directly to TV sets. However, M2M devices such as set-top boxes create a huge amount of raw data relating to the channels being watched and the use of added services such as video on demand or recording. This data can be extracted and converted into knowledge. With the help of more accurate audience measurement, advertisers have much greater ability and flexibility to target commercials to more distinct audience segments. As a result, TV providers can increase the charge to advertisers, and increase their revenues. Openet Convergent Mediation has been used in order to achieve this aim. Large scale viewership data is collected off set-top boxes and inputted into Openet Convergent Mediation. This data is then enriched with subscriber demographic data to give a view of the programs that each demographics watch. As part of this process, any data generated is anonymised in order to protect the identities of the subscribers. This information is presented in a manner that allows further knowledge to be extracted with an interface that allows extensive queries to be run on the data – ever wondered how many 35-40 year old golfers watch Spongebob Squarepants? Openet’s Convergent Mediation product is a data management tool that can be used to collate large amounts of raw-data, combine it with other internal/external data, enrich the data, and pass the data to a presentation layer for display. In short, Openet Convergent Mediation is a Data-to-Knowledge converter. Read about more of the challenges and opportunities of M2M, as well as how Openet can address them, in our M2M white-paper: https://ow.ly/jspEI