It’s very noticeable that we are undergoing some new changes in enterprise computing. Some would say that it’s the cloud computing buzz, while other would say that it’s the fact that consumers now have more computing power then before while it’s mobile and close to their fingertips.
Basically it’s different views of the same world. Fast communications, cheap chips and universal standards (and habits) had made data creation and data consumption extremely easy and cost effective. As a result we now have more data created outside the organizational walls that is relevant for decision making.
In commercial enterprises, this data is usually applicable to customers and potential markets, or for R&D of new technologies, but in GRC (Governance, Risk management & Compliance) based organisations, this new and extra data can be extremely useful in adding some new insights:
- Customer’s background can be easily verified by exploring social networks and public personal space, and thus know your customer policies can be better enforced. This can be achieved with customer consent or even without it. It seems that “Tell me whom your friends are and I’ll tell you who you are” was never more true.
- Sentiment analysis which is the process of aggregating trends in public social space can be used not just for measuring brand recognition and campaign effectiveness, but also for recognizing inside information leaks from publicly traded companies or finding hot-spots of public unrest whether geographically or semantically oriented .
- Mashing up this outside data with organisational data can by achieved more easily using mature semantic technologies. The usual way of doing a mesh-up is implementing an ETL (Extract, Transform, Load) process from one data-source to another. In the case of multiple external data-sources which change frequently, this process is extremely work intensive and requires mapping from one physical source to another. When making this mapping via semantic association, one can reduce mapping workload since ontologies can provide rules that associate family names fields with surname fields.
Web Intelligence (WEBINT) and Open Sources Intelligence (OSINT) have come off age, and although semantic technologies still look exotic to the standard world of Information Technology, they are here to stay. Providing solutions based on those technologies is easily adapted as SAAS (Software As A Service) model since those systems deal with external data anyhow and internal data can be selectively anonymized.
Software engineering is a young discipline, when I learned it, it was more of an art form where guru artisans were building their own object libraries and carrying then from project to project. There were no QA teams and information security standards meant that you had to have a magnetic card to get access to the computer room.
Now, of course things are different, and when planning a software or data project, you have to choose between various ecosystems and navigate between industry standards, but still while not an infant, SW engineering is rapidly evolving.
Real Estate on the other hand, was dealt with since Sumerians and Egyptians discovered geometry to measure plots and orient buildings. So one major difference is historical depth.
The other issue is physics. When dealing with software and data, you’re not dealing with physics, your actually working with applied math. Real Estate is all about classical physics, mainly statics, but you get to have some dynamic modelling once you’re at the extreme size, height or elevation.
When you’re not bounded by physics (at least not by classical one) you have to responsibility to control Entropy and by so try to keep it simple as possible, either by reducing the system variables or its dynamic behavior.
