6 Ways Compliance Can Build Data Analytics Skills

Libby, a compliance manager, wants to create a risk assessment dashboard to present real-time data segmented by program, region and regulatory authority. But does she have the skills to do it? Maybe not.

Given the changes in how organizations operate and do business today, all corporate functions — including compliance and ethics — have to build various skills to upgrade core program activities.

Compliance staff often lack the necessary data acumen to effectively use data and draw meaningful conclusions

“Although most organizations recognize the importance of data analytics skills, compliance staff often lack the necessary data acumen to effectively use data and draw meaningful conclusions,” says Aaron Pinnick, research director at Gartner.

To build the data analytics skills of their team, heads of compliance and ethics can take these six steps.

1. Create a data analytics skill development strategy. First, set out a clear vision for why the team should use data analytics, where to obtain the necessary data and how to use it. This is crucial because employees are unlikely to learn and engage with training unless they believe in its value.
2. Outline specific learning objectives. Help employees understand the types of training they will receive and how they might use data analytics in their day-to-day work. Managers can help compliance teams connect data analytics to their current roles, work with individual employees to determine their competency in working with data and set realistic objectives accordingly. To make this process easier, create a simple employee development plan that includes long- and short-term career goals, data analytics proficiencies and strengths, and a timeline to achieve improved data skills. Constructive feedback and regular updates to the development plan are especially important as employee roles and responsibilities change.
3. Train compliance employees specifically on data analytics. Training models vary depending on the organization and program and the employee’s individual development needs. The easiest way to provide formal analytics training is to find existing training material from other internal partners such as IT, finance, or audit, and to adapt it for compliance employees. Compliance managers can also acquire external data analytics training courses — ideally ones using simulated, case-based exercises to give employees experiential learning similar to their actual day-to-day workflows.
4. Provide on-the-job learning opportunities. On-the-job learning opportunities enable employees to get comfortable with data and move and stretch their abilities with new tasks. Embed learning from training into workflows and give employees additional exposure to projects where analytics are used. These projects can be used as opportunities for employees with limited analytics experience to shadow and learn from peers with greater expertise.
5. Provide ongoing coaching and support. Managers should schedule weekly team meetings or one-on-one coaching sessions to determine how employees are benefiting from the training, answer questions on how to deal with challenges, and compare practices and perspectives within the team. Compliance and ethics departments should also include data analytics information in staff newsletters and meetings to highlight training sessions. Peer monitoring offers development opportunities and can be linked with shadowing to give team members access to more skilled peers for day- or week-long learning rotations.
6. Monitor employees’ data analytics skills development. Managers who are effective at development can increase employee performance by 25%, and they are ultimately responsible for the results of the training process. Hold employees accountable when they don’t meet objectives but reward them for achieving development goals and suggest new projects that can help them develop further.

Compliance and ethics programs are moving in the right direction when it comes to developing data analytics skills among staff and using data to support functional decisions, but managers should lead the charge when it comes to improving employee proficiency in data analytics by ensuring team members have adequate training, can practice working with data and get ongoing support for development.