Posts Tagged 'customer relationship management'

Tips for adversarial analytics

I put togethers this compendium of thngs that are useful to know for those starting out in analytics for policing, signals intelligence, counterterrorism, anti-money-laundering, cybersecurity, and customs; and which might be useful to those using analytics when organisational priorities come into conflict with customers (as they almost always do).

Most of the content is either tucked away in academic publications, not publishable by itself, or common knowledge among practitioners but not written down.

I hope you find it helpful (pdf):  Tips for Adversarial Analytics

“But I don’t have anything to hide”

This is the common response of many ordinary people when the discussion of (especially) government surveillance programs comes up. And they’re right, up to a point. In a perfect world, innocent people have nothing to fear from government.

The bigger problem, in fact, comes from the data collected and the models built by multinational businesses. Everyone has something to hide from them: the bottom line prices we are willing to pay.

We have not yet quite reached the world of differential pricing. We’ve become accustomed to the idea that the person sitting next to us on a plane may have paid (much) less for the identical travel experience, but we haven’t quite become reconciled to the idea that an online retailer might be charging us more for the same product than they charge other people, let alone that the chocolate bar at the corner store might be more expensive for us. If anything, we’re inclined to think that an organisation that has lots of data about us and has built a detailed model of us might give us a better price.

But it doesn’t require too much prescience to see that this isn’t always going to be the case. The seller’s slogan has always been “all the market can bear”.

Any commercial organization, under the name of customer relationship management, is building a model of your predicted net future value. Their actions towards you are driven by how large this is. Any benefits and discounts you get now are based on the expectation that, over the long haul, they will reap the converse benefits and more. It’s inherently an adversarial relationship.

Now think about the impact of data collection and modelling, especially with the realization that everything collected is there for ever. There’s no possibility of an economic fresh start, no bankruptcy of models that will wipe the slate clean and let you start again.

Negotiation relies on the property that each party holds back their actual bottom line. In a world where your bottom line is probably better known to the entity you’re negotiating with than it is to you, can you ever win? Or even win-win? Now tell me that you have nothing to hide.

[And, in the ongoing discussion of post-Snowden government surveillance, there’s still this enormous blind spot about the fact that multinational businesses collect electronic communication, content and metadata; location; every action on portable devices and some laptops; complete browsing and search histories; and audio around any of these devices. And they’re processing it all extremely hard.]

Knowledge discovery — good or bad?

Most people have some awareness that computer algorithms can be used to extract useful knowledge from large amounts of data. This is the basis of customer relationship management, which is used by many businesses to evaluate (?improve) the quality of their interactions with their customers, both individuals and other businesses. This way of extracting knowledge is called knowledge discovery or data mining.

Most people have some intuitive idea of how this might work — after all humans are extremely good at extracting knowledge from certain kinds of data themselves. However, people tend to jump quite quickly to one of two diametrically opposite assumptions about how knowledge discovery works.

The first is a dystopian view — knowledge extraction technology can be used to learn everything about individuals from their social gaffes to their deepest thoughts. With this kind of power, governments will be unable to resist and will use knowledge discovery as a tool for control, in the style imagined in 1984. A variation on this theme is that knowledge discovery only looks effective and so will seduce governments and others into spending vast amount of money and collecting huge datasets without any payback.

The second is a utopian view — knowledge extraction technology will make every interaction as efficient as possible, and will prevent all of the bad things in the world from happening.

The truth, of course, is somewhere between these two extremes. There are many powerful things that knowledge discovery can do, some of them non-obvious; but this requires careful thought about the process, and, potentially, considerable cost. We are a long way from using knowledge discovery to improve the collection of library fines.

There are serious issues around the intrusiveness of data collection for knowledge discovery. Many of these issues are less difficult and more manageable than they appear on the surface. The question of whether knowledge discovery is good or bad is more nuanced than almost all of the discussion about it would suggest. Stay tuned.