If you work in financial services, transforming your data into a valuable asset (flexible, available and owned by you) requires a proactive and long-term vision that encompasses a proven process.
Robust data management, woven through every aspect of your business, helps you deliver on your business goals. But, with Asset Managers dealing with specific sets of data rather than a firm-wide view, this siloed understanding and interpretation of data, can create fragmented data and inconsistent reporting.
Effective data management and governance are critical, but your process and approach to data transformation needs to support business ‘as is’, as well as the business that ‘might be’.
So, when executing a data transformation project, it can be helpful to think of it as a 3-step process, with each phase building on the one before:
1) Resolve immediate data needs
2) Protect BAU
3) Build for growth
It’s important to get value from day one. So the first step of your transformation is not strictly a transformation; but more about addressing any urgent data needs.
This may be about implementing some data validation to fix key reports and improve data quality.
Next make sure that BAU activity is protected. Your data flows from one system into another. The system receiving the data might be responsible for controlling risk and it might be part of your daily and weekly routines. You need to make sure that putting a question mark in the wrong field won’t cause a broken report. Or that someone not sending a file through from the administrator doesn’t cause a report to stop running. Because BAU is a rich source of data and could provide the data or leverage the funds you need to grow.
Once BAU is sorted, you can start to move forward and build the architecture that will make you more money. Think about how you can restructure your investment research process and decision-making. How could you streamline the information you get from speaking to investors? How could you use data and the services around data more effectively?
Finally, an important part of the data transformation process is keeping a very close eye on data costs.
Track where you're spending money to acquire data, as well as which sources are effective in giving you an advantage. Being aware of this enables you to potentially trim costs and cut superfluous data sources.
And when you save costs or experience growth, plough that money back into BAU to make that as robust as it can be. Once you’re maximising BAU data, you can start to build on it and identify how to make (or save) more money by leveraging that data.
Getting your data processes right and not losing sight of BAU is just one aspect of making data your foundation. Bringing your people on board and creating a data-focused culture are equally important.
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