Ppt netsharp

Notas del editor

• #4: Algo peor que no tener información disponible es tener mucha información y no saber qué hacer con ella. La Inteligencia de Negocios o Business Intelligence (BI) es la solución a ese problema,
• #6: "En esta organización tenemos montañas de Datos pero no podemos accederlos" "Sólo muestrenme lo que es importante" "No es posible que me reúna con dos personas que tienen el mismo indicador con distintos valores" "Queremos que la gente use la información como soporte para la toma de decisiones" "El área X tiene la información que necesito pero no me la brinda"
• #7: La creación de un datawarehouse representa en la mayoría de las ocasiones el primer paso, desde el punto de vista técnico, para implantar una solución completa y fiable de Business Intelligence.
• #9: Where does SAS Financial Management fit into the SAS platform? Everywhere, from Data Integration, to advanced analytics, and report distribution/business intelligence.
• #10: How does software help? …. Well, everyone is talking about analytics these days, so let’s spend a moment thinking about “what are analytics?” Think of business value along the Y axis and degree of intelligence along the X axis. What I want to argue here, what I want to put to you is that when we talk about analytics, we can’t look at “does the vendor offer analytics?” We have to think of analytics along a spectrum of offerings. Different capabilities for different tasks. And we can divide those up into looking at the past and looking into the future. Build 1 … Very simply, at a very low level, we have standard reports. We answer the question, “What happened?” We all know about these. They’re generated on a regular basis, probably a lot of them monthly. Financial reports are a great example. Just “what happened” in a particular area. Useful to some extent, but not very useful. Build 2 … Ad hoc reports. “How many? How often? Where?” Okay, great. Give me that report writer I need and let me run a couple custom reports. That’s pretty good. Build 3… How about query drilldown – an OLAP environment. “Where exactly is the problem?” Allow me a little bit of discovery. This is where OLAP can come in and allow me to drill down and find out how many, what color and where. Build 4 … And then we have things like, “What actions are needed?” Alerts. So we kind of understand this; we know when we have a problem. Let’s put an alert in place so we know when this happens again in the future. Maybe put this on our scorecard; let’s turn something red. Now in all four cases here, we’re using data that occurred in the past obviously, and we’re looking at what happened in the past. But it’s not telling us anything about what’s going to happen in the future. And this is where the majority of analytic and performance management offerings are today. We’re looking at what happened in the past, and we’re reacting. Sure we may have some “leading” indicators on our scorecard … but we need more. Build 5 … Statistical analysis. “Why is this happening?” We can begin to run some things. We can begin to run some frequencies. We can begin to do some regression analysis. We can begin to look at why things are happening using the stored data and then beginning to answer some questions. Build 6 … Forecasting. “What if these trends continue?” This is probably one of the hottest markets right now, one of the hottest applications. Forecasting – it applies everywhere. The retail industry relies on this -- all these stores that they have out there today – what sizes do they need in this store versus what sizes do they need in that store? Forecasting demand. So that you don’t have a lot of things left over. So that you don’t run short. So that you supply just enough and maximize your inventory. Build 7 … Predictive modeling. “What will happen next?” I have 10 million customers. I want to do a campaign. Who’s most likely to respond? How do I segment that group? How do I determine who’s most likely to leave my organization? This is where predictive modeling comes into play. Build 8 … And then finally, in true support of innovation, we have optimization. “How do we do things better?” How do we make sure that we really trim all the fat around a particular activity, get that activity taken care of and allow us more time to support innovation with our remaining funds and focus? So look at this thing in terms of a spectrum of offerings. Ask yourself, what are the problems that you’re facing within your industry, and say, “What do I need to apply to fix that problem or to satisfy that issue?”
• #11: 10