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Data accuracy pertains to records that are free from errors and can serve as a dependable source of information.

For investment management, data accuracy constitutes the foremost and essential element of the data quality framework. Jack Olson’s widely referenced work, Data Quality: The Accuracy Dimension, highlights form and content as two crucial characteristics of data accuracy.

Consider the following example:

In the United States, dates follow the MM/DD/YYYY format, whereas in the European Union and other countries, the format is DD/MM/YYYY. If 09/10/2020 were converted to content, which format would be accurate?

Is it September 10th or October 9th? This classic form versus meaning problem poses a threat to data accuracy, typically resulting from a lack of standardization and rules.

Ensuring that data stored on your server is accurate and suitable for use is crucial. This impacts an organization’s business intelligence, forecasting, budgeting, and other vital activities. If data is irrelevant, incorrect, incomplete, or inaccurate, it can hinder processes and undermine operational efficiency.

4 Key Reasons Why Data Accuracy is Vital for Investment Managers

Effective data management is the only reliable way to adopt cutting-edge technologies and outpace competitors.

As businesses increasingly utilize modern technology, they generate more data. This data growth presents a valuable opportunity to enhance profitability.

Given the significance of data, data accuracy becomes vital for business success. High-quality data offers a greater chance of thriving. Here are four compelling reasons why data accuracy is critical to your business.

1. Data Accuracy Enables Better Decision-Making

High-quality data instills confidence in those who rely on it. When data quality is exceptional, users can produce superior outputs.

This boosts business efficiency and reduces the risk associated with outcomes. With dependable outputs, businesses can refine their decision-making processes and effectively mitigate potential risks.

2. Data Accuracy Improves Productivity

The importance of data accuracy extends beyond decision-making and is directly connected to productivity.

Accurate data simplifies tasks for your employees. Rather than spending time identifying and correcting data errors, your staff can concentrate on more important tasks and objectives.

Data accuracy

3. Data Accuracy Mitigates Financial Risk

Data errors pose significant financial risks for businesses, though the consequences extend beyond monetary losses. Insufficient data quality can adversely affect brand credibility, productivity, and efficiency.

In the most severe cases, a company’s reputation may suffer, leading to the loss of numerous customers. The main problem with subpar data is that a single error can cause additional mistakes, making the identification and correction of these issues time-consuming.

Rather than using recently acquired data to formulate business strategies, you must devote time to correcting errors, which comes at a cost.

Inadequate data may also impede your capacity to stay current with emerging marketing trends and capitalize on lucrative business opportunities. Failing to keep pace with these trends provides your competitors with a competitive advantage.

4. Data Accuracy Supports Compliance

Adhering to various regulations is crucial for any business, as it directly affects your relationship with customers.

Maintaining high-quality data can make the difference between incurring costly fines and achieving compliance. Regulations continue to evolve, and your business must adapt accordingly. The only way to accomplish this is by maintaining data accuracy.

Artificial Intelligence (AI) Enhances Data Collection and Analysis

Artificial Intelligence can streamline the process of gathering and processing vast amounts of data to identify patterns in user behavior and derive insights almost instantaneously.

The strong capabilities of Artificial Intelligence in data analytics make AI and big data a powerful combination. Research indicates that an average enterprise loses up to $12.9 million per year due to subpar data quality, underscoring the immense importance of quality for data-driven businesses.

By employing automated data capture, AI can detect data coming from various sources and moving toward different destinations, ensuring no input or data stream goes undetected.

Artificial Intelligence excels at identifying and eliminating even the slightest errors in a dataset, which could impact the data’s quality and usability. Such precision is unattainable through manual data monitoring, and with advanced AI overseeing the process, nothing goes unnoticed.

Conclusion

In the majority of software implementation projects, data migration is usually one of the final stages to be carried out before the system goes live. This is done after establishing connections with custodians, market data providers, and other sources, as well as setting system parameters. This approach can lead to problems with data reconciliation if the migration occurs after the entire system has been built.

To address this issue, FundCount prioritizes data during the implementation process. During the analysis phase, which takes place before execution, we thoroughly examine your data sources, account information, investment types, and the relationships between accounts. We scrutinize statements from custodians and other third-party sources and discuss workflow, ensuring that we understand every aspect of your business. This comprehensive understanding enables us to identify the structural requirements and the incoming data that will support your firm’s accounting and reporting.