Pay attention to the quality of your data, says SSP

Our partners at SSP know a lot about data. Their insurance industry software powers more than 700 clients across 40+ business classes around the world. But, as we know, the quality of the output depends heavily in the quality of the input. Here’s an article that highlights the importance of inputting high-quality data in order to extract optimum results:

Automation is a crucial element in today’s business world. It enhances efficiencies and reduces costs for organisations across sectors. But remember, any degree of automation relies on data to establish trends and facilitate better risk assessments. The insurance industry is no exception; thus, data stands as a vital component for the future growth of insurers. While this sector possesses a substantial amount of data, the critical concern lies in its quality. As the adage goes, “rubbish in, rubbish out.”

For this reason, insurance companies should prioritise assessing their data and actively work to enhance its quality before delving into the realm of automation.

The following stages outline a structured process for insurers to follow before immersing themselves in automation. This thoughtful approach will prevent potential shortcomings and maximise returns on investment.

Steps for enhancing data quality

  1. Assessment and baseline establishment:
    • Data audit: Conduct a thorough audit of existing data to identify issues, inconsistencies, and gaps.
    • Define metrics: Establish KPIs and metrics to measure data quality
    • Baseline assessment: Establish a baseline data quality level against which to measure improvements.
  2. Data governance implementation:
    • Policy development: Formulate data quality policies and standards.
    • Governance structure: Define roles and responsibilities for data ownership and stewardship.
    • Communication: Communicate data quality expectations across the organisation
  3. Data profiling and analysis:
    • Profiling: Use data profiling tools to analyse the structure, content, and quality of data.
    • Identify anomalies: Detect anomalies, errors, and inconsistencies within the datasets.
    • Prioritise issues: Prioritise data quality issues based on their impact on business processes.
  4. Data cleansing and standardisation:
    • Cleanse data: Implement automated tools to correct errors and inconsistencies.
    • Standardisation: Standardise data formats, units, and naming conventions.
    • Enforce validation rules: Set up validation checks to ensure data conforms to predefined rules.
  5. Training and awareness:
    • Employee training: Conduct training programmes to educate employees on the importance of data quality.
    • Promote ownership: Encourage a culture of data ownership and responsibility.
    • Feedback mechanism: Establish a feedback mechanism for employees to report data quality issues.
  6. Continuous monitoring and improvements:
    • Real-time monitoring: Implement real-time or near-real-time monitoring of data quality.
    • Feedback loops: Create feedback loops for continuous improvement based on monitoring results.
    • Iterative process: Iterate on data quality processes based on feedback and evolving business needs.

By following these stages, organisations can establish a structured and iterative approach to improving data quality, ensuring that data is reliable, accurate, and aligned with business objectives.