The Data Roundtable

In his book The Most Human Human, Brian Christian discussed what Baba Shiv of the Stanford Graduate School of Business called the decision dilemma, “where there is no objectively best choice, where there are simply a number of subjective variables with trade-offs between them. The nature of the situation is such that additional information probably won’t even help. In these cases – consider the parable of the donkey that, halfway between two bales of hay and unable to decide which way to walk, starves to death – what we want, more than to be correct, is to be satisfied with our choice (and out of the dilemma).”

Your community of customers, like any other community, consists of a collection of individual parties that can also be referred to as “actors” who are related to one another. These relationships can be modeled using the graph abstraction, in which every actor is represented as a node, and every connection between two actors is represented as a link or an edge between two nodes.

Most businesses obsess over their sales performances. Nowhere is this taken to more extremes than the retail sector. They increasingly employ ever more sophisticated means to track, cajole, entice, motivate and understand their consumer purchasing behaviour.

“It’s easier to ask forgiveness than it is to get permission.”

–Rear Admiral Grace Hopper

Grace Hopper’s quote goes a long way – and applies to many facets of life. While the axiom may be true, some companies are finding out that it’s better to go the permission route.

My previous post about change management, which advocated nudges not mandates, received an excellent comment from Karen Way: “What I’ve found that works to nudge people into accepting data quality as part of their norm is to demonstrate the benefit to them, the WIIFM (what’s in it for me) factor. This is especially true…

When you consider the development of strategies for integrating customer centricity into the breadth of business applications in different business functions, one emerging analytical focal point is social media analytics.

Although I do far less on-the-ground consulting than I used to, I still occasionally get to sample the delights of walking into an organisation, performing a data quality assessment and watching the occasional jaw hit the table when the client sees the actual state of their data.

Can an organization that has historically managed its data poorly start “doing” Big Data well? It’s an interesting question, and I’m not the only one asking it.