In addition to all this consumer-centric data, let’s not forget that there are lots of additional classes of data that have moved onto the web – for example, government data, public records, prices – data that was previously locked in databases and file systems behind firewalls. I expect that being able to access and normalize these additional classes of data will be a key ingredient for additional insights by correlating it with all this not-previously-available consumer-centric data that John is discussing in this blog post.

Unexpected correlations between disparate data sets lead to unexpected insights!

Timo Kissel
CTO
Fetch Technologies

I see significant value associated with Conversation Data in both the consumer and enterprise space.

For me, elements of conversation data include things like:
- conversing parties
- converse time / date / duration
- conversation mode – voice, video, text
- conversation threading
- conversation followers
- conversation lifecycle
- conversation context
- conversation graph

Just a thought…

1. Location Data
2. Social / Identity Data
3. Product Data
Another data?….uhm…well it’s very hard…but i want you to share another site that will help you…http://inboundmarketing.web.id/services/search-marketing

My data is MY DATA.

To start, I see 5 layers that define the “behavioral / segment” of a user
- Transactional
- Search
- Social network
- Engagement (reviews, check-in)
- Demographic

Then you have on their own
- Location
- Products / Services
- Knowledge
- Events

Of course, the way we perceive data layers is strongly influenced by the nature of our respective industry.

1. Location Data
2. Social / Identity Data
3. Product Data
4. Review Data
5. Real Estate Data
6. News / Blog Data
7. Travel Data

The main issue I have with the approach being taken here is that it seems to come from the perspective of someone producing data, rather than those consuming data.

As far as the question of interest graph, etc.. so far we’ve seen the above 7 types as the main base and most everything else is derived.

Great post.. You’re right, adding a data layer to the “Points of Control” map seems like a natural next step.

We’ve been looking at data for quite some time here at Infochimps, and the groups you’ve laid out are almost identical to the way we see the world.

Identity & Social Graph, however, doesn’t seem like its own continent. Rather, it seems more appropriate to consider it a subterranean level of the world underneath all of the data continents.

For example, location data is a continent populated by players like Foursquare, Yelp, Facebook, Twitter, and etc. So, at the surface, aggregated data can provide excellent insights without compromising privacy. But, if you dig deeper — mine it, if you will — then it’s possible to get the social data. But, of course, digging deeper is messy and carries its own set of dangers.

I feel like all of your data continents that you’ve proposed are great, but they all seemed to be tinged with the specter of individual data. Let’s not forget that the “Database of Intentions” still works quite well even if we don’t dig down into personally identifiable keys.

In any case, for your map metaphor here, I suppose we at Infochimps are building bridges between continents and also running a commercial shipping fleet that transports data all around the world. There’s a lot of data to move around, and we’re having a great time building the infrastructure to support that need.

Cheers,
dennis.