Data Differentiators in 2022

The complexity and volume of data continues to increase relentlessly year on year, and financial institutions burdened with ageing legacy systems and processes are struggling to maintain full knowledge or control of their data. 

At the same time, technologies including artificial intelligence and machine learning are becoming firmly established as invaluable tools to automate processes and enhance data discovery. Meanwhile migration to the cloud is accelerating, allowing for increased accessibility, flexibility, and performance of data processes. 

We have identified the 10 key trends that we believe will shape the data landscape in the coming year and inform how banks, insurance, and investment managers position their data strategies to extract maximum value from that data in 2022.

1. Rise of the CDAO

As the volume and value of data assets continue to grow, there is growing demand for a Chief Data and Analytics Officer (CDAO), a more all-encompassing role that brings intelligence, data and analytics together to derive optimal value from data assets for corporate strategy and drive innovation.

2. Data Mesh & Data Virtualisation

Data meshes and data virtualisation are flexible, decentralised, and distributed solutions that enable large financial organisations to tackle the ever-increasing volumes and complexity of data.

3. Data Reusability – data assets vs data mastering

Master data is today the foundation of any business and key for financial services institutions to extract maximum value from their data assets. Robust master data management will foster data reusability, the last element of the FAIR principles – but certainly not the least.

4. It’s all about Metadata!

Good metadata is essential to describe and structure an organisation’s data. A large and expanding volume of unstructured data is as good as useless. Financial services firms need to focus on effective ways of annotating data to make it usable and valuable. It is no longer about the race for data, but rather the race for data about data.

5. Ontological skills are in demand

Concepts such as knowledge graphs, ontologies, and linked data have started to emerge from the shadows, and examples of their application are multiplying. Most large financial institutions have recognized their value and the challenge now lies in acquiring the necessary (and also scarce and expensive) talent to derive the most benefit from them.

6. ESG Data

Climate change is front and centre of corporate agendas as firms face mounting pressure from stakeholders, governments and regulators to provide indisputable proof of their Environmental, Social and Governance (ESG) claims and credentials. Positioned at the centre of the global economy, banks should lead by example.

7. Cloud Standards (Open Source)

Cloud computing is a highly successful business model, yet it is one the highly regulated financial services industry has been slow to embrace. The EDM Council have now released their Cloud Data Management Capabilities Framework, enabling safer cloud computing than ever. This is the perfect opportunity for more established financial institutions to make the jump to the cloud.

8. Multicloud strategies

The old adage “don’t put all your eggs in one basket” also applies to enterprise cloud architecture. As financial services firms migrate to the cloud, they should align their strategies to benefit from the agility, flexibility, scalability and overall enhanced risk management capabilities provided by a multi-cloud approach.

9. Data Management Automation for data discovery

As artificial intelligence and machine learning become more prevalent, we will see financial services organisations ramping up their investment in augmented data management. This will allow a streamlining of their data and analytics pipelines to enhance data discovery, minimise risks and increase the value of data.

10. Data Science skills shortages and training

Whilst higher education institutions have been expanding the data science courses, they offer, this is a long-term solution to what is a more immediate problem. In the short to medium-term, a better strategy is to democratise data science through upskilling and reskilling of current employees. For the foreseeable future, the data and analytics strategies of financial services firms will only be fully realised through focused investment in employees’ data literacy.