Tag Archives: decision support

Building A Business Case For Data Quality: The Steps

Building A Business Case For Data Quality, 3 of a 7-part series

Let’s look at the steps in more detail for building a business case for data quality using the bottom-up approach. Where do you start? You need to find a sponsor—someone who instinctively knows there is a problem and wants help in quantifying it. Marketing knows it has duplicate customer records and wants to get a better handle on them. You should look at these systems or business processes that work with the customer data. You must assess how the data in these systems is used within marketing. For example, what is the data used for, what critical decisions are made based on this data, and how many people use it to make decisions? The more users or the more critical the decision, the more likely this data is a candidate for evaluation. Also look at more than the initial decision support system and data. Look at any systems that get data from the decision support system. Data flow diagrams are always helpful in assessing this but usually difficult to find. (more…)

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Event Driven Decision Support

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…)

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