In the world of sport, data is widely spread to understand many metrics about team performance. This analysis is crucial to success. This careful attention to data however, is not always applied when utilising a commercial database.
For a football club to be commercially successful for example, all data must be used. Using all data improves decision making and all aspects of marketing, as you are operating using all available information. Without data strategy your data will never be effective in increasing and tracking supporter advocacy. Data strategy is also crucial for accurately monetizing your database to your partners.
Disconnected data silos
Data strategy however is a serious challenge for sporting organisations. Football clubs for example have typically been holding data in Silos. Storing data separately makes it very difficult to have a complete view of what’s going on. Different databases need to be understood as a whole to identify what is truly happening and what the emerging trends are so you can respond to them.
Disconnected data creates a number of problems:
- Data isn’t providing an accurate view of the ‘true’ situation
- Targeting the right people can’t be done as you don’t have the depth of information
- Supporters receive duplicate messaging and possible mixed messaging if your database is over inflated with duplicate information
- Your data quality is too poor to market (How many Mr & Mrs Tests do you have?)
- Partners lose faith in the data and its management
Due to advances in Big Data, these no longer need to be issues. It should be your mission to strive for a Single Customer View, all data in one place, for a complete understanding of all data. But how do you move towards a Single Customer View? To find out what needs to happen read how the Rugby Football Union achieved an SCV, in our Whitepaper: Six Stages to Transforming Data Quality, Structure and Management.
Following the stages of the lifecycle will lead you into a Single Customer View. Considering each stage shapes data strategy:
- How do you capture data?
- How do you hold your data?
- How do you maintain your data?
- How do you analyse this data?
This series of questions is a tool for analysing your ‘data quality lifecycle’ and pinpointing areas for improvement. Having a process in place at each stage of the cycle is crucial to data strategy.