The Trellance Data Blog

Improving Data: What questions should we be asking ourselves?

Posted by Aaron Wang on Dec 19, 2013 11:58:10 AM

Many credit unions have just completed their strategic planning process, preparing them to navigate the many challenges ahead. I wonder how many credit unions ended up with one of their strategic priorities for 2014, a strategy of Improving Data. I wonder if the question of data and the complexities and opportunities present were even discussed. While it is clear that the volume of data is exploding and coming from different sources, it is not clear what questions should be asked.

1. If we conclude that Data is important, then what is our Data strategy? Those who embrace data as a strategic priority will “leap frog” the competition. Many credit unions view Business Intelligence (BI) or Data as a project and not a strategic priority.

2. If we develop a BI function, who would lead that group and who would be part of the group? Some credit unions have re-allocated resources and have created a BI function that includes folks from finance, IT, marketing, lending, and operations.

3. Is our current analytics, data, and reporting functions efficient and effective? Survey and studies reveal that 70-80% of time of any analytics project is focused on data collection, integration, transformation, cleansing, and other preparation activities. This all means that analysts are spending relatively little time coming up with interesting questions to ask the data or doing any actual analysis. Data silo’s is a roadblock for impactful analysis.

4. Is our current enterprise reporting tools and processes improving productivity (sales), transparency, and accountability? Credit unions continue to use excel as a primary reporting tool. Case study after case study show that the use of intuitive and interactive (drillable) dashboards enterprise wide will have an immediate positive impact on performance.

Do we have an integrated, single source data warehouse? A database or data cube should not be confused with a data warehouse that is a single repository for all your data. Data warehouses contain a wide variety of data that present a coherent picture of business conditions at a single point in time.

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Topics: Data Analytics