BLOG – January 2023

Golden source data environment

by | Feb 2, 2023

Golden source data environment

How to create a golden source data environment: Lessons from Olympic success

Think small, not big.

It’s a philosophy that changed the British Cycling team from also-rans to Olympic champions – and is one wealth managers would do well to emulate.

British Cycling performance director Sir David Brailsford’s achievements were based on a profound insight: the power of marginal gains. Identify the critical success factors, then focus on making continuous, incremental and aggregating improvements. As the team’s cluster of Olympic medals testify, 1% performance improvements compounded over time can add up to transformative results.

Data quality is a competitive advantage

In wealth management, data has become firms’ critical success factor. Focus on continually improving the data and good things happen. Wealth managers can start to act on that data with confidence. They can run client, regulatory and management reports knowing the information is accurate and complete. Clients are happier. Compliance becomes easier. Reputational risk diminishes. And operational gearing and profitability improve.

Eradicating the inaccurate, incomplete and inconsistent data that causes problems, wherever it’s found, takes constant, progressive effort. As an MIT Sloan Management Review study observed, “fewer errors mean lower costs, and the key to fewer errors lies in finding and eliminating their root causes.”

Yet many wealth management firms’ strategies for tackling data deficiencies don’t do the job. Employing manual fixes to correct data issues that often have manual origins creates as many problems as it solves.

Four-eyes checks of data entry are commonplace. But the checks eat up staff time and slow processes down. As do four-eyes checks of client reporting, a typical recourse when firms no longer trust their own data.

Often we see wealth managers resort to running spreadsheets, which they compare against downloaded data and data scripts to check for discrepancies that they then have to resolve. Larger firms may have dedicated individuals and teams responsible for data integrity. All take manual effort, while engraining key person risks.

Human involvement means errors can be missed, or corrections misapplied. Many firms don’t have a robust, standardised procedure for staff to follow when making amendments, nor a recheck process to confirm mistakes have been properly rectified. Data remediation also depends on staff actually completing the task, and the results being relayed to supervisors through the management reporting.

The golden source solution

Creating a true golden source data environment demands a different approach.

Relying on manual effort produces expense and unpredictability. Switching to automation-based systematisation provides vital control, and enables constant improvements to be made to wealth managers’ datasets.

By automating data management, firms can move from a reactive to a proactive environment. Automated data quality tools remove the threat of human variability, making checks accurate and consistent. Data can be examined as it comes in, preventing errors from occurring, while identifying problems faster and ensuring any issues that do arise are resolved quickly.

And an automated system is always on. It offers wealth managers a 24/7 data analyst that takes no holidays, requires no training and makes no errors. The result is significantly enhanced operational efficiency and scalability.

Trusted and transparent

Automated solutions with an integrated workflow engine able to catch and flag errors early in the process also provide management with improved transparency into their data quality. Workflow reports can detail what issues are being investigated, where they stand, what has been resolved and when, and what hasn’t.

Plus the systematisation and transparency helps firms enhance their staff knowledge and training. Users can see where and why an issue emerged and how to fix it, so the same mistakes aren’t repeated. These incremental improvements become embedded in the process and aggregate to much greater accuracy and efficiency over time.

The accumulation of small – and sometimes big – quality gains allow users to move from no data visibility to data clarity, and from untrusted to trusted data. The result is a golden source of data that is truly golden.

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