Data, information and their value: a DB community challenge

Data is all around and it seems that when it is,properly processed it can lead to valuable information. The data management industry mastodons  must see a value on data that emerges in social networks, public storage systems, devices, since they are investing   and storing all these gold hidden behind mountains of Gigas. They are also investing in public front ends that provide information out of these raw data collections (cf. sky server, chronozoom projects). I was impressed by,the google front end able to,provide information in real time about the elections in Mexico, under a geographic OLAP like metaphor.  Users could navigate along different aggregation granularities organized in a geographic way: from ballot places in quarters to a global vision! This will be possible for following other political processes like Egypt elections  and the USA elections.

Huge data collections and decision making apps, prêt a porter and free with nice properties as freshness and provenance, availability. Can other companies, governments and communities still catch the boat and start investing on data harvest, storage and delivery?   is there still room for new amazingly simple and useful ideas? Of course there is ! I believe that there is even room for doing some business through research and development. The important thing is not to do alternative apps to those that are already there but try to look for the hidden markets that are still open.

New ways of data provision like data marketplaces is starting to gain popularity and force. If you have a nice, curated data collection, that you are willing to sell, you can provide it in a market according a specific economic model. Curated data can have added value properties such as  quality, provenance, reliability properties that impact the economic of the data. Database tools that can help to build and maintain data collections with nice DB properties are still missing. Indeed, building databases and putting them on line is still challenging, ongoing Big Data programs are a testimony of it.

The requirement is there, the financial sources are there, the expertise is also there, the DB community has fundamental research and R&D opportunities there. for making new decision making, data analysis data mining applications turn.