The breadth and quality of data is critical to the success of financial services institutions – and firms are accordingly more data centric than ever.
The heightened demand for more personalised products and experiences requires enhanced data gathering, management and analysis, while also placing an onus on robust data privacy and security.
There is also increasing pressure on financial institutions from regulatory bodies to meet new data governance, environment and sustainability requirements. Meanwhile, the explosion of generative AI – notably ChatGPT – is driving a fresh wave of innovation that raises novel questions around the ethical use of data.
Embracing modern data products and architectures while empowering employees with data skills are key if firms are to remain competitive. Below we highlight 10 trends which we believe should inform and shape the data strategies of financial services organisations over the next 12 months.
1. CURATED DATA PLATFORMS
The global automated data platform market size was estimated at USD 1.3 billion in 2022 and it is expected to hit around USD 7.5 billion by 2032.1
Unfavourable economic conditions have constrained firms’ ability to invest in technology and data. Modern data platforms, designed and engineered with curated data products, focus on automation and data management by design. This fosters data reusability, which in turn increases agility and scalability, reduces time to market and promotes long-term cost savings. Financial services
organisations should accordingly derive more efficiency and value from their investments in curated data platforms.
2. DATA PRODUCTS
By 2025, it is projected that 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.2
The ability to effectively harness the power of data and analytics is a key determinant of a firm’s success. Modern data platforms support the development of data products by using DataOps to improve data development and deployment processes, and ensure efficient data flows and data security standards across data pipelines. Firms that invest in data products – allowing them to generate and leverage their own insights to make predictions and prescribe intelligent actions – will have a competitive advantage.
3. GENERATIVE AI
The global generative AI market is currently worth over $13 billion and is expected to reach $22 billion by 2025.3
The release of ChatGPT in November 2022, and its subsequent upgrade in March 2023, has rocketed generative AI (GenAI) into the mainstream. The GenAI buzz can be expected to continue in 2024, with firms investing heavily in this technology in pursuit of efficiency and cost-cutting goals. However, the effectiveness and reliability of GenAI models are predicated on the quality of the data used to train them. Firms should focus on improving their data curation and governance, and adopt tools such as FMOps, which orchestrate the workflow from foundation models to GenAI, and Knowledge Graphs, which can enhance Gen AI models by providing valuable semantic context.
4. CUSTOMER 360
Only 14% of Organizations Have Achieved a 360-Degree View of their Customer.4
A typical banking customer engages with multiple products and services across their bank, giving rise to duplicated efforts in areas such as customer onboarding, and leaving their data dispersed across multiple internal business silos. A more holistic, ‘Customer 360’ experience can be delivered by consolidating these diverse data sources, including internal client revenue, cost, transaction data and external data (e.g., credit ratings), enabling a single view of the customer. The Customer 360 paradigm can also allow for a proactive offboarding of unprofitable clients while improving KYC onboarding processes and ensuring customers have smoother, personalised experiences and journeys.
5. THE RISE OF DATA UX
Every $1 invested in UX results in a return of $100.5
The central role of data in modern financial services has meant an exponential growth in the users of that data. Where data was once handled almost exclusively by expert analysts and then data scientists, employees and teams across the enterprise now need to manage and apply data in their day-to-day activities. With the ubiquity of interactive data dashboards and now the rise of GenAI, the representation of complex data and analytics models has been simplified, significantly improving the data user experience (Data UX) by making it as easy to understand and use as it has never been. By delivering insights that are accessible and easy to digest via such ‘data democratization’, firms can promote enhanced (data-led) decision-making, leading to a higher return on investment.
6. DATA LITERACY IN HIGH DEMAND
Basic data literacy is the most critical skill for 89% UK leaders.6
Data literacy – the aptitude to understand, write, and communicate data – is key for organisations as they become more data centric. From basic data entry in Microsoft Excel to the work of a full-stack data scientist, firms that invest heavily in upskilling their employees stand to gain a competitive edge. Given the high entry barrier for data science, firms might also consider enabling ‘citizen data scientists’ – analysts or knowledge workers who, while not necessarily expert in advanced statistics, mathematics or computer science, are still equipped to extract and deliver high value insights from data.
7. AUGMENTED DATA MANAGEMENT
By 2024, worldwide spending on data management technologies is predicted to reach $315 billion.7
Given the increasing volume and complexity of data, traditional methods are falling short when addressing today’s data management challenges. Augmented data management focuses on automation and scalability, making use of state-of the art analytics and AI techniques to automate manual tasks, enable quick implementation of data controls, and reduce maintenance overheads. Notable examples include AI-driven data discovery, data observability, and self-describing data products.
8. UNSTRUCTURED DATA
Unstructured data accounts for at least 80% of all data generated.8
Highly organised, following specific patterns, and obeying strict sets of rules, structured data is well-understood and the industry standard within financial services firms. Yet the vast majority of data – a large proportion of which is text in the form of emails, social media content, teams messages, slide decks, etc. – is unstructured and consequently unmanaged, posing a significant risk that remains largely ignored. However, by implementing a trust framework, firms can ensure data source reliability and validity for unstructured data. Trust can also be improved by enforcing authenticity through the use of digital signatures, encryption, and secure channels, and by promoting transparent data lineage practices, and regular audits. On the flip side, unstructured data can also be a huge opportunity – firms that leverage AI can gain a competitive edge by tapping into this wealth of unstructured data, transforming it into a consumable, and understandable asset, and turn it into valuable insights that drive additional revenue.
9. DATA AND AI ETHICS
85% of consumers say that it is important for organizations to factor in ethics as they use AI.9
The rise of ChatGPT has placed AI ethics firmly in the spotlight. Financial services organisations are currently underprepared and should be proactive in mitigating the emerging risks from this new technology. This includes establishing strong data management foundations to ensure quality, availability, and trust in data inputs, controlling for bias, and improving the transparency and traceability of data outputs. The transformative potential of new AI technologies cannot be denied, but failing to implement adequate controls and risk management could carry a high cost in terms of reputational damage and lost revenue.
10. INCREASED REGULATORY SCRUTINY
Global spending on regulatory technology (RegTech) will exceed $130 billion by 2025.10
Post-Brexit the UK continues to slowly decouple from the EU regulatory regime, with the Financial Services and Markets Act of 2023 (FSM Act) bringing numerous changes and the UK pushing ahead with the implementation of its bespoke Consumer Duty. More broadly, recent reforms to BCBS 239 and Solvency II, and the publication of the first draft of the EU’s Digital Operational Resilience Act, all pose additional compliance challenges for both banking and insurance data officers. This will be a year where chief data officers will have to juggle budget constraints while coming under increasing pressure to meet data governance, lineage, environment and sustainability goals in a complex and ever-changing regulatory landscape.
SOURCES:
1 https://www.precedenceresearch.com/automated-data-platform-market
2 https://www.prnewswire.com/de/pressemitteilungen/real-time-real-value-80-of-businesses-see-revenue-increases-thanks-to-real-time-data-870705221.html
3 https://explodingtopics.com/blog/generative-ai-market
4 https://www.gartner.com/en/newsroom/press-releases/gartner-marketing-survey-finds-only-14--of-organizations-have-ac
5 https://www.forrester.com/report/The-Six-Steps-For-Justifying-Better-UX/RES117708
6 https://www.datacamp.com/blog/closing-the-data-literacy-gap-key-insights-from-the-state-of-data-literacy-report-2023
7 https://zipdo.co/statistics/data-management/
8 https://www.komprise.com/resource/the-2022-komprise-unstructured-data-management-report/
9 https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/ai-ethics-in-action
10 https://www.juniperresearch.com/press/regtech-spending-to-exceed-130-billion-in-2025
