Three Data and Analytics Trends to Watch in 2023
What will be the talent hotspots in data analytics in 2023? Here are three trends that we’ve identified in the business use of data and analytics that are likely to grow in momentum over the coming 12 months, shaping the skillsets sought by employers.
1. The use of data and analytics permeates an ever-wider scope of business functions
Marketing and Risk have historically made the most use of data and analytics. However, in many Financial Services firms, the use of data and analytics has been expanding considerably in other functions. Operations, Finance, HR, Compliance, Internal Audit, etc. are now being equipped with the sorts of data science resources formerly found only in risk and commercial.
One of the reasons for this is that the range of use cases that are enabled by data and analytics is growing fast and includes:
- “Standalone” predictive analytics – e.g., AML (Anti-Money Laundering) or fraud risk scoring; product recommendations; forecasting operations resourcing needs; predicting employee attrition; etc.
- Process automation – e.g., using natural language processing to automate information extraction for regulatory reporting
- Customer and employee journey redesign – e.g., redesigning the corporate credit underwriting and approval process end-to-end
The widening of applicability across business functions along with greater integration into business processes and customer journeys means that data and analytics professionals with a strong business-orientation and a desire and ability to work in a more integrated fashion with business colleagues will be ever more highly prized.
2. Model governance, model operations and model control catch-up with deployment
Financial Services firms must exercise strong governance and control around these growing data and analytics activities.
Data privacy, data security, model reliability and model interpretability are just some of the aspects that require careful first, second and third line management. First line “model operations” includes inventorying models and their documentation, training and test metrics; monitoring models in production for performance stability; and ensuring compliance and control. Second line model validation independently tests models and reviews their documentation, while third line model governance ensures compliance with model risk policies and procedures.
As the scrutiny and expectations of regulators in this area grows – particularly with respect to the increasing use of AI and machine learning in financial services – we can expect to see strong demand for skills in this “enablement” layer of data and analytics.
3. Upskilling complements greenfield hiring
The use of data and analytics right across organisations is becoming so important that some larger institutions have embarked on an organisation-wide upskilling of employees, using comprehensive training programmes to raise the level of understanding of where, when and how data and analytics can be usefully exploited and to facilitate closer collaboration of all employees with data science and technology teams.
For many, this will represent a tremendous opportunity to explore and potentially initiate a change in career direction towards the world of data and analytics. In our view, whether your organisation has introduced such initiatives or not, management appetite to endorse and encourage enthusiastic employees to broaden skills in this area has never been higher.