As a private wealth manager, you rely heavily on performance reports that demonstrate the value of your advice and service to your clients. Yet with so much of your credibility and the ultimate value of your services riding on the quality of these reports, what steps are you taking to ensure the accuracy of the data?

If you are like many private wealth managers, you simply assume that your reports are being created in a standard way and with the highest level of data accuracy. The problem with this assumption: there is no industry standard regarding processing procedures or even requiring a minimum level of data accuracy. It is up to each reporting firm to determine its own standard for accuracy.

Accuracy takes work

“Perfection is our goal,” says Robert Fiore, president and chief executive of Private Client Resources, the Connecticut-based private wealth reporting firm. But according to the company’s measurement standards, even PCR falls a little shy of accuracy perfection, though by less than a rounding error.

PCR performs more than 100 data quality assurance checks daily and follows procedures that identify and address any incidences of inaccuracy. The firm views every error it catches as an opportunity to develop a new procedure to keep that specific error from ever recurring. It’s an approach that has led us to achieve what we believe is an industry-leading accuracy standard of 99.7 per cent and counting.

The more data used in a calculation, the higher the likelihood that the data file will contain erroneously entered numbers. In fact, 90 per cent of all Excel spreadsheets with more than 150 rows of formulae contain material errors

Why superior accuracy matters

Reporting inaccuracy doesn’t just compromise the integrity of your decision-making or impact how your fees are calculated. Productivity is lost to correcting the resulting errors. The cost of an erroneous report can also include damage to your credibility since it reveals deficiencies and inadequacies, which will erode the trust that underlies your client relationships.

If you are not receiving accurate data, you are not going to be able to deliver what your clients need and expect from you. Not knowing the level of data quality creates exposure to risk that can put more than your relationships on the line. It can quickly erode your reputation and impact your business.

Indeed, 16 per cent of client relationship management time is lost on administration and error resolution .

This is why it is so important that you start asking more questions about the accuracy levels of your reports and how that accuracy is achieved. You have too much riding on the quality of your private wealth reporting capabilities to rely on unquestioned assumptions.

Six measures of accuracy

The elimination of data inaccuracy requires that your reporting firm be managed with a focus on quality. That means it should employ the following procedures when processing your reports:

- If it isn’t measured, it can’t be managed. All elements of the quality assurance program must be measured and managed.

- Data should be collected directly from the source. Each data source has unique policies and data conveyance procedures. Having a direct relationship leads to a quicker response time when questions arise and helps resolve issues before they become problems. The direct line of communication also facilitates the realization of more consistent results.

- Root cause analysis should be used when issues arise. In order to prevent a data issue from recurring, a firm needs to focus on understanding what caused that issue in the first place. Companies that embrace learning and then adapt better training policies and procedures, and regularly update technology are better positioned to deliver higher levels of accuracy.

- Preventative measures are the best defense. Quality assurance procedures and processes need to be incorporated at every data interaction point. This means establishing practices that yield predictable results in every interaction with data from access, extraction, and capture, to reviewing and reporting. If an unpredictable result is produced it needs to be investigated and resolved immediately.

- Consistency in all practices is key. All interactions need to be consistently executed by the same people. A strong sense of familiarity is needed by the individuals who interact with the data and make decisions about it.

- Responsibility and accountability lead to improved accuracy. Eliminating inaccuracy is a team goal. However, errors are typically created or caused by individuals. Therefore, people should be assigned to, and encouraged to, own every data interaction point. By assuming such accountability, they are better positioned to achieve a standard of performance and learn from any mistakes they make.