Data as the foundation for digital innovation in financial services

22 September 2020 Consultancy.eu

Demand for data-driven innovation in the financial services industry is skyrocketing, as data rapidly takes an increasingly pivotal role in business models and operations. Deborah Haverkort and Roel van Erp from Synechron outline how data serves as the foundation for innovation, and what financial institutions can do to get their fundamentals right. 

From fraud prevention, to customer retention, to process optimisation, data-driven technology has become the go-to solution for a wide array of challenges that the sector currently faces, if not for all. And with their promising results, it is no wonder that banks and insurers have been pushing to implement tooling, hire data scientists, and monetise data where possible.

Financial services firms are hungry to leverage their data in big ways. Yet, as their focus lies mainly on implementing technology and data science capabilities, limited data quality and availability remain inadequately addressed. What’s often overlooked is that the success of these technologies hinge on the foundations of the data landscape.

Data as the foundation for digital innovation in financial services

Data management as the foundation

Just as constructing a building starts with robust architecture and a solid foundation, so does data management form the underlying base of data innovation. What we see is that financial services firms invest heavily in data driven solutions in support of fraud detection, risk-management and compliance, to name a few. Despite the importance of tools for enabling data capabilities, without high quality data the tools will not deliver desired results. In order to effectively process and monetise information, organisations need their data to be up-to-date, accurate and reliable. 

A classic example of this is where tooling -- whether for reporting, client onboarding, or other data-driven process -- requires customer data, but the available flow is incomplete or inaccurate. A common interim solution is having teams manually reconcile and correct data deficiencies. We frequently receive questions from businesses that are in the process of solution implementation when they start facing such data governance challenges. This is often only recognized when innovation output does not meet expected results. 

A little goes a long way

Luckily, a little goes a long way when it comes to data management. From various projects where Synechron has leveraged data management capabilities for successful innovative tooling, starting with defining goals has shown to be an effective way to get data governance on track. 

Creating value is only possible when organisations can extract meaningful information from high-quality data. To achieve this, we recommend starting small by defining data needs around specific goals (e.g. reports or processes). A maturity assessment allows for a bottom-up approach that gives clients a quick insight into their main challenges.

Appropriate data management and governance solutions are then designed and implemented to structurally solve the data issues. This creates a lasting solution that supports the specific business case, speeding up time to market of the desired innovation.

Configuring your data management and governance

We recommend that financial services institutions perform the following four actions to assure that their data-driven technology starts off on the right track, and does not get derailed along the way: 

Define clear goals

  • Define your business’ data problems and challenges that you wish to solve
  • Solve unexplained inconsistencies between Finance and risk reporting 

Perform analysis of data management

  • Identify/capture data flow from source to report to identify root causes of data problems.
  • Data flow analysis will uncover the usage of diverging data sources and help determine that inconsistent data reported by finance and risk

Design and implement a tailored data governance solution

  • Implement key data management capabilities such as master data management, data quality assessment and mitigation measures
  • Develop strong master data management to ensure finance and risk data are aligned 

Retrieve results from the data innovation solution

  • Measurement is the key to success; measure the improvements post-implementation
  • Consistency between finance and risk data output allows accurate risk and finance reporting now and into the future

The reality of today’s market environment is that the amount and importance of data is only increasing, making data valuable asset that needs apt governance and protection. It’s time to set data management as a top priority in your plans for the era of datafication.