Tag Archives: Data Warehousing
- The cost of losing one customer is four times higher than the cost of obtaining that same customer (Return on Behavior Magazine)
- Satisfying and retaining current customers is 3 to 10 times cheaper than acquiring new customers, and a typical company receives around 65 percent of its business from existing customers (McKinsey, 2001)
- A 5% reduction in the customer defection rate can increase profits by 25% to 80% (Return on Behavior Magazine)
- 7 out of 10 customers who switch to a competitor do so because of poor service (McKinsey, 2001) (more…)
In my previous entry, I mentioned how our customers are democratizing innovation in data warehousing and data integration. One thing is clear: there is renewed energy around using data to become competitive in the global market. We also have many options in data warehousing –a centralized enterprise data warehouse (EDW), departmental data warehouses and data marts, including appliance options and cloud computing for data warehousing. And yes, we do have an ongoing argument of EDW versus data marts. What’s different at this time is that, it’s the business that’s fueling this debate. A customer may outlaw a data mart because the maintenance costs and risks managing marts outweigh the potential benefits and thus do not meet the operating requirements. Another may opt for a cyclical data mart to allow for faster time to market and analytic flexibility for departmental business needs with a shorter turnaround.
One of our customers in the entertainment industry recently went live with their real-time data warehousing and master data management project. (more…)
In my last blog, we discussed the ever changing face of data warehousing. As the data warehouse evolves, so does the decision support system that is built on the platform of the data warehouse. We are moving from historical and analytical decision support to event driven decision support.
What this means is, instead of observing patterns and trends from historical data and then influencing changes in your business workflow, you will introduce data as events occur in your operational systems into the data warehouse, and take instant measurements and provide the results for analysis and decision support.
Event driven data integration will require significant data architecture and data mapping efforts from operational systems to the data warehouse. There are multiple ways to implement this with ETL/ELT/CDC types of technologies. But do not forget that you will need to pay attention to data quality, metadata and master data management in addition to all the other details. Last but not least; you will need to remember that all of this needs to happen with extreme agility. (more…)
Technology vendors like to talk about platforms, because platforms imply a broader footprint both in terms of functional capabilities and in terms of implementation usage. Platforms also sound more “strategic,” even if the practical implications are vague. But the term “platform” can also be simple marketing hype. How do you know when a software “platform” is really a platform? More specifically, do data integration platforms exist now?