In November I attended the annual Financial Information Management conference (FIMA) and spoke to a number of attendees about the importance of data in their organisation. Data concerns varied in size and type, but it was very clear that the majority of financial organisations are at a pivotal point in deciding how to most effectively manage their data.
When discussing data management, I find the conversation always seems to come back around to the same 3 things:
1. Don’t forget, Data is an asset (And an expensive one at that!)
Data is not just an outcome of doing business; it has a tangible, monetary value. Gartner recently stated that issues with data quality cost an average company more than £5m per year. Not to mention the time costs, missed opportunities and bad decisions that may have been made as a result.
Data quality was certainly the ‘buzz word’ at the FIMA conference; I prefer the term data enrichment. Standardising, consolidating and validating your data has huge benefits. But enriching it with other data sources (from other silos or vendors) transforms it into intelligence, which provides real business value.
2. Go beyond data quality… Geographically
Where are your customers? Geocoding your data is a great example of this; adding a locational element to an existing customer profile provides invaluable insight. In the quest for a ‘single view of the customer’ data enrichment adds more meaningful information, like demographics and location, which help you to make better decisions about how you interact with your customers.
In the world of financial services, location intelligence can also help to:
- Understand local marketing campaigns
- Target marketing campaigns to locations
- Evaluate local and regional store forecasts and targets
- Analyse which services should be available where (and why)
3. Data utopia is a journey, not a destination
We all know what needs to be achieved with data, but how you go about it is open to plenty of choice. In my opinion the goal posts will continuously move. As the world around us changes so will the amount (and types) of data. Big data will turn into enormous data! So starting the journey now, and tackling it bit by bit, will mitigate the risk of data quality issues costing you millions in the future. Don’t try to boil the ocean all at once, or eat an elephant whole. Whichever you prefer!
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