Gravity7: Social Interaction Design SxD By Adrian Chan

By all accounts, there’s a big data revolution on its way, and soon. New, distributed, and increasingly real time database and data warehousing solutions have made big data storage and querying more viable. Data collection, of course, continues apace. And the number of data sources available, too, continues to grow.

The big data problem and solution, as it stands today, is very simply a matter of: what to do with it. Data reveals insights only as good as the organization mining for them. Even patterns need to be coded into queries — and that has to be done by somebody interested in searching for the pattern in the first place.

So while big data holds an immense amount of potential insight — it’s not obvious what this insight is. Nor whether, once identified, it can be made useful. (To wit, the many data analysts currently confounded by financial breakdowns.)

Given that big data is the type of data to make its truths at scale, that is, by means of high altitude “observations” of very small things, two orders of the big data query emerge: the big and the small. The big, in which the “meaning” of the data is determined. And the small, or the relevant data itself.

There’s no finding value in the small bits of data without first determining the big picture views one hopes to obtain. The need for both big data strategy as well as big data tactics is fascinating. It should not only open new vistas on worlds and patterns not seen before; it should lead to new kinds of business, decision-making, forecasting, marketing, and much more.

Where then does social data fit in? Social data is data about users: their activities, identities, habits, relationships, interests etc on social networks and social media. Social data should be about what users do. Its the exhaust, if you will, of their actions and communication. And ideally well suited to the aims of commerce.

Sometimes it seems that big data contains social data. Other times not. But if big data includes social data within its purview, then an interesting question arises. For social data, presumably, is a different kind of data.

The issue concerns what the data means. And since social data is often activity data, it’s more than data. A tweet may be tweeted to somebody. A share, because it expresses a user’s interests. Events attended because there are friends there. Or because the user is a fan.

In other words, social data isn’t data about something, it’s data created by the action of somebody. Data about events, things, objects is that: objective. Data produced by people interacting and communicating is subjective: it needs to be interpreted, because it is intentional.

It would seem that these two kinds of data won’t mix very well. Data about a population, for example, vs data created by its members. The former view of big data would suggest making use of patterns for efficiencies. The social data view would suggest micro-targeting individuals based on their behaviors. The former wants to better grasp correlations, high level views, and find meaning by mining. The latter wants to describe individuals more accurately, richly, and target behaviors based on expectations.

Big data and social data are not one and the same. But nor are they mutually exclusive. Both tell stories worth paying attention to. It will be interesting to see what comes for each — and of course, who is involved.