What’s top of mind for CDOs in the financial industry?
Recently, C-level data executives from the financial sector gathered at FIMA Europe 2022, where data and analytics innovation took center stage.
Following are six key data trends and highlights for financial industry data leaders.
1. Data privacy by design, not an afterthought, is a business necessity.
87% of customers wouldn't do business with a company with inadequate security practices, reminds
McKinsey.
The global privacy regulation landscape is highly fragmented, with more than 100 countries enacting privacy laws. The finance sector has
received more fines under the EU General Data Protection Regulation than any other industry. The consequences of non-compliance include not only fines and settlements, but also business disruptions, productivity losses, and revenue losses.
To reassure their customers and provide better services, data leaders are investing in privacy-by-design by:
- Leveraging privacy-enhancing technologies (PETs) to unlock sensitive data processing for long-term data analytics and sharing.
- Going beyond simple data masking and building robust data privacy toolboxes to match individual use cases and data diversity.
- Focusing on insights, not secrets. Trends and insights are often visible without access to sensitive data. Consider the big picture when making data-driven decisions.
2. Data hyperautomation will drive efficiency and enable value-added activities.
Gartner already
predicted that
“by 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes.”
Hyperautomation of data processes will allow data leaders to:
- Enhance business intelligence by improving data accessibility.
- Concentrate on activities that add value and improve efficiency.
- Reduce costs and improve operational effectiveness.
3. Multi-cloud is the new norm.
94% of large enterprises are expected to adopt a multi-cloud approach by 2023, according to
Statista.
Many of the most transformative tech trends have been driven by cloud computing. However, cloud expansion is increasingly under the spotlight of financial regulators, who urge the industry to use
more than one cloud provider.
With a multi-cloud strategy, data leaders will be able to:
- Diversify the organization's cloud infrastructure and data management.
- Avoid vendor lock-in.
- Reduce costs by customizing infrastructure.
- Improve disaster recovery and resilience.
4. Improve ESG data sourcing for corporate sustainability.
“High-quality businesses that adhere to sound ESG practices will outperform those that do not,”
says Tidjane Thiam, executive chairman of Freedom Acquisition Corp., former Credit Suisse CEO.
ESG is on the minds of many in the financial industry. Using ESG data to support sustainability strategies is one of the most popular topics.
Data leaders should consider the following:
- As the regulatory environment tightens, more transparency is required. Be sure to invest in improving the quality and quantity of ESG data as reporting requirements increase.
- Until recently, ESG metrics have not been standardized. In order to provide clear and comparable data analysis, data executives need to take the lead.
5. Enhance the quality of your enterprise data at scale to enable accurate decision-making.
Increasingly, companies realize that poor data quality management will put them at a competitive disadvantage. In financial companies, data quality plays a crucial role in decision-making.
Data leaders today are expected to:
- Develop a solid strategy for managing data quality.
- Utilize technologies that improve data quality to support it.
- Support data democratization through establishing a strong data culture.
6. Fair artificial intelligence starts with data ethics.
According to
Gartner, 85% of artificial intelligence (AI) projects will deliver erroneous outcomes by 2023 due to bias in data.
KPMG research found that 72% of finance leaders are most concerned about security threats when it comes to AI tech.
As artificial intelligence stakes rise, data leaders are expected to:
- Establish clear standards for training machine learning applications with data.
- Set up rules and best practices for gathering, storing and analyzing internal and external data.
- Promote privacy standards for AI deployment.
- Improve the explainability and transparency of AI-enabled solutions, recommendations and applications.
Want to unlock your data and be compliant? Learn more about the business benefits of privacy tech.
