Data management is a critical function for financial services organisations. It encompasses the processes and technologies used to collect, store, manage, and protect data. Data management is essential for ensuring the accuracy, reliability, and accessibility of data. It can also help organisations to identify and mitigate risks, make better decisions, and improve customer service. However, financial services organisations face a number of challenges in data management. These challenges include:
- Data Governance: Data governance is the process of establishing policies and procedures for managing data. This includes defining data standards, ensuring data quality, and protecting data from unauthorised access, use, or disclosure. Financial services organisations need strong data governance practices in place to comply with regulations and protect their data from cyber threats.
- Data expenditure: Managing data can be expensive. Financial services organisations need to invest in the hardware, software, and personnel needed to collect, store, manage, and protect their data. The cost of data management can be a significant barrier for small and medium-sized financial services organisations.
- Stakeholder engagement: Data management is a cross-functional activity that involves stakeholders from all levels of the organisation. It is important to engage stakeholders early in the data management process. It ensures that they understand the goals and objectives of data management. It also makes it easier to obtain their support for data management initiatives.
- Uncertainty about where to invest: Financial services organisations are constantly bombarded with new data technologies and solutions. It can be difficult for organisations to know which technologies and solutions are right for them. This uncertainty can lead to wasted time and money on data management initiatives that do not meet the organisation's needs.
How financial services can keep up with data management challenges
How can financial services organisations keep up with the sector’s digital challengers? For banks and other financial services providers, converting data into commercial gold is critical to attracting customers, improving experiences, boosting efficiency and ultimately staying relevant and desirable in an increasingly digital world.
Every business in the industry knows the importance of good quality data to achieve their data-driven aspirations so what’s holding them back from making their vision a reality?
Hosted in collaboration with our strategic partner, Informatica, we recently invited a select group of data decision-makers from the financial services sector to talk candidly about their data management challenges – along with opportunities and expectations over dinner at Searcy’s at the Gherkin. Among them was special guest Robin Miller from Lowell Financial, who has recently guided his organisation through a major data management transformation.
Here’s what attendees had to say about the obstacles they are facing, and how they plan to overcome them:
“Data Governance is still a struggle”
Most financial businesses understand the importance of data governance in theory but putting data governance into practice is still an ongoing struggle for many financial organisations, even those that have data governance initiatives in place or have invested in them in the past. Bringing data owners, stewards, and other stakeholders onto the same page and following the same agenda for data governance is particularly difficult when different departments or groups within the business have their own data objectives so, it is critical that the various threads involved with a data governance initiative are well aligned across these different groups.
Cultural attitudes towards data can also play a significant part in this. Lines drawn between different generations and different experience or understanding of data possibilities. Add legacy technologies or even more recent software that has been put in place to solve specific problems or enable data-driven activities for a specific department and the data governance challenge becomes even more complex.
Action: automate and integrate Data Governance
To overcome these data governance issues, banks are looking to data governance automations and integration approaches. One example is data fabric, which allows a more democratised approach to data governance, while still bringing the organisation together towards core data governance objectives. By driving accountability and ownership, these approaches can cement data governance from a cultural as well as process-driven perspective.
“Data expenditure is scrutinised.”
In the financial sector, securing investment into any data project is all about the value case. What tangible monetary value is this data investment going to bring to your organisation? Projects with a clear commercial benefit are always going to get more support from stakeholders and financial decision makers. This can mean that the more exciting projects like introducing AI, data streaming or automation are favoured over more practical solutions like data management. Yet anyone who knows anything about data understands that without those strong foundations of well-managed, high-quality data, those exciting data-driven projects won’t get the desired results.
Action: build a value case
Data management is a commercially viable investment on its own, and can lead quickly to cost savings, improved efficiency, reduced risk and profitable initiatives. The key is to a) create a clear business case (using a combination of industry views like Gartner and specific analysis on your current process vs your desired outcome), b) outline a well-defined strategy with tailored commercial objectives and c) use an agile delivery method that enables you to illustrate results early on, and keep scaling to new use cases.
“It’s difficult to get stakeholder engagement”
Once a data lead in a financial firm does secure funding for their data management project, the next hurdle is maintaining buy-in from all stakeholders. Data management can be seen as the less exciting phase of a bank’s digital transformation, but it is important and the challenge is generating excitement for the project and sustaining it as it’s rolled out across the business.
Action: demonstrate scalable results
Again, agile, scalable delivery is key to this. Stakeholders can quickly see initial use cases in action and measure the results it achieves. They can then apply this approach to other areas of the business and see the commercial results accumulate. Communicating progress is essential: presenting results at board level at regular points in the implementation can make a big difference to how the project is perceived, as can appointing ambassadors throughout the business.
“We need to know if we’re investing in the right areas”
There are a lot of data options available to financial services organisations. Sometimes, it can be difficult to gauge the urgency and commercial viability of all of them to make the right decisions. How can data leads know they are recommending investment in the right areas? How can the board know that they are committing funds to something that will generate results and have longevity?
In a fast-changing market like data, knowing for sure whether you are investing in technology that is going to be the best solution for your business is almost impossible. You can make educated decisions, but technology changes fast. However, the risk of not investing means that you can’t scale your business. In a fast-paced digital world, not exploring data-driven initiatives will hold back growth and ultimately leave your firm behind both the digital challengers and your existing competition.
Action: invest from the foundations up
Arguably, an investment in data management is always the right decision. Regardless of whether the technology you use changes in a few years’ time, or your data goals shift with new business objectives. Building a foundation of data that is fit for purpose is vital for whatever you choose to do with data next. As it shifts both your practical approach to managing data and your organisation’s cultural attitudes towards it.
Do any of these data challenges strike a chord with you? If you’re looking for practical support in rolling out any data initiative in the financial services sector, our team can help.