The Supply Chain of the Future has the instrumentation, interconnectedness and intelligence to predict, if not prevent, disruptions before they occur. It relies on new approaches that employ sensor technologies, new analytic capabilities and simulation techniques to not just sense and respond, but anticipate and act.IBM Global Chief Supply Chain Officer Study 2009 View the Smarter Supply Chain Study video

The Supply Chain of the Future has the instrumentation, interconnectedness and intelligence to predict, if not prevent, disruptions before they occur. It relies on new approaches that employ sensor technologies, new analytic capabilities and simulation techniques to not just sense and respond, but anticipate and act.IBM Global Chief Supply Chain Officer Study 2009 View the Smarter Supply Chain Study video

Business intelligence—and its predecessor concepts decision support, executive information systems, and so forth—have been circulating for several decades in business. However, I don’t think it’s ever fully worked. What we’ve done is to throw data (often in the form of difficult-to-navigate data warehouses) and software tools at business users, and said “Go at it.” That’s simply been too hard … I’ve argued for a while that organizations need to increase their focus on decision-making. In particular, they need to think again about the relationship between information and decision-making. I recently completed a study on this topic, with the sponsorship of IBM’s Information Management business unit, in which I looked at 26 efforts to improve decision-making in organizations. I concluded the following ten things about how business intelligence (BI) needs to evolve: 1. Decisions are the unit of work to which BI initiatives should be applied. 2. Providing access to data and tools isn’t enough if you want to ensure that decisions are actually improved. 3. If you’re going to supply data to a decision-maker, it should be only what is needed to make the decision. 4. The relationship between information and decisions is a choice organizations can make—from “loosely coupled,” which is what happens in traditional BI, to “automated,” in which the decision is made through automation (see graphic below): (via 10 Principles of the New Business Intelligence - Tom Davenport - HarvardBusiness.org)

Business intelligence—and its predecessor concepts decision support, executive information systems, and so forth—have been circulating for several decades in business. However, I don’t think it’s ever fully worked. What we’ve done is to throw data (often in the form of difficult-to-navigate data warehouses) and software tools at business users, and said “Go at it.” That’s simply been too hard … I’ve argued for a while that organizations need to increase their focus on decision-making. In particular, they need to think again about the relationship between information and decision-making. I recently completed a study on this topic, with the sponsorship of IBM’s Information Management business unit, in which I looked at 26 efforts to improve decision-making in organizations. I concluded the following ten things about how business intelligence (BI) needs to evolve: 1. Decisions are the unit of work to which BI initiatives should be applied. 2. Providing access to data and tools isn’t enough if you want to ensure that decisions are actually improved. 3. If you’re going to supply data to a decision-maker, it should be only what is needed to make the decision. 4. The relationship between information and decisions is a choice organizations can make—from “loosely coupled,” which is what happens in traditional BI, to “automated,” in which the decision is made through automation (see graphic below): (via 10 Principles of the New Business Intelligence - Tom Davenport - HarvardBusiness.org)

James Taylor Reports on Predictive Analytics World Some trends: There is now about 100Gb stored per person on the planet and it is doubling every year. Market research can now combine explicit survey data with implicit behavior data There is a move from models being assumption heavy to being data rich thanks to the number of visitors and the amount of information. From knowing about transactions (enough for recommendation) to knowing interactions (enough for targeting) and ultimately relationships (can move to a long term relationship basis). (via  The unrealized power of data )

James Taylor Reports on Predictive Analytics World Some trends: There is now about 100Gb stored per person on the planet and it is doubling every year. Market research can now combine explicit survey data with implicit behavior data There is a move from models being assumption heavy to being data rich thanks to the number of visitors and the amount of information. From knowing about transactions (enough for recommendation) to knowing interactions (enough for targeting) and ultimately relationships (can move to a long term relationship basis). (via The unrealized power of data )