Copyright © 2009 http://jtonedm.com James Taylor
Syndicated from BeyeNetwork
James Kobielus had a nice list of Advanced Analytics Predictions For 2010 over on the Forrester blog. As usual James is thought-provoking with some interesting predictions. Let’s start with the one’s with which I agree most strongly.
- Advanced analytics sinks deep roots in the data warehouse. Absolutely. In-database/in-warehouse analytics will become more and more important and the analytical processing of streaming data likewise. However, the way data is stored in warehouses will have to change too, not just the way analytics are done. Too many warehouses and marts today store summary data, rollups or data where the crucial time dimension is obscured. No matter how powerful the analytic engines get, this will have to change and warehouses will have to store the low-level transactional data that analytics need.
- User-friendly predictive modeling comes to the information workplace. Yup. While I think there will continue to be a role for experts in building models and that executing predictive models in operational systems is at least equally important, knowledge workers are going to …
Copyright © 2009 http://jtonedm.com James Taylor
Syndicated from BeyeNetwork
James Kobielus had a nice list of Advanced Analytics Predictions For 2010 over on the Forrester blog. As usual James is thought-provoking with some interesting predictions. Let’s start with the one’s with which I agree most strongly.
- Advanced analytics sinks deep roots in the data warehouse. Absolutely. In-database/in-warehouse analytics will become more and more important and the analytical processing of streaming data likewise. However, the way data is stored in warehouses will have to change too, not just the way analytics are done. Too many warehouses and marts today store summary data, rollups or data where the crucial time dimension is obscured. No matter how powerful the analytic engines get, this will have to change and warehouses will have to store the low-level transactional data that analytics need.
- User-friendly predictive modeling comes to the information workplace. Yup. While I think there will continue to be a role for experts in building models and that executing predictive models in operational systems is at least equally important, knowledge workers are going to expect tools that let them build predictive analytics for themselves.
- Social network analysis bring powerful predictive analysis to the online economy. Yes, but not only to the online economy. Social network analysis is a powerful tool in telcos (see this piece on using call detail records to develop networks) and fraud detection already. Social network analysis does not require Social Networks!
- Low-cost data warehousing delivers fast analytics to the midmarket. Maybe. Bringing analytics to the midmarket will be more about packaging the analytics up and making them easy to consume than about appliances.
- Self-service operational BI puts information workers in driver’s seat. I don’t think this one is that compelling and I don’t see most users demanding these tools. Putting information workers in the driver’s seat requires making the BI tools vanish into the day-to-day systems and processes, not just making them self-service. Most business people want to do what they always want to do, which is run their business more effectively. If tools can help with that, then they will use them; otherwise, not. The number who want to build mashups or self-serve on BI is a small and fairly geeky subset in my experience. Personally, I don’t expect the major BI vendors to make anything like enough progress in making their tools “vanish” into the systems business users use every day to deliver on this one.
And I know he had one more — data warehousing virtualizing into the cloud — but I don’t have an opinion about that one.
