Actionable Reporting (And AI) In A Data-Driven World

Data Quality

As a result, data quality has become more important than ever. Making sure your data is as clean as possible is a critical step! Some signs you have dirty data are:

• Data entry errors – individuals sometimes make errors such as misspellings, transposed digits, or other inconsistent formatting;
• Missing data;
• Duplicate data; and
• Data source discrepancies – data from different sources that have inconsistent or conflicting data.

For AI, if your data is inaccurate, incomplete, or contains errors, the output may be misleading. Good data quality contributes to the model's ability to handle various inputs and scenarios effectively. Also, ensuring that your data is diverse and free from biases is essential to creating AI solutions that are fair and inclusive. Otherwise, you may introduce bias resulting in unfair or unintended results.

How do you know you may have a problem? If you get comments from end users that the data seems incomplete or outdated (lagging), you should investigate. Or if you get complaints from external customers about their account information. Collaborate with the data owners or subject matter experts (SMEs) to help identify discrepancies/anomalies and how to correct the data both present and ongoing.

Also, if your organization is the victim of a security breach or unauthorized access, make sure the data hasn’t been changed, corrupted, or contaminated. Take the time to ensure the data is still accurate and reliable.

Data Governance Framework

It starts by having a comprehensive data governance framework and should be an ongoing process because data quality is not “one and done.” This includes, but is not limited to:

1. Data governance framework – have policies and procedures to establish and enforce data quality standards and data ownership within the organization;
2. Data security – the data owner should determine who should have access to specific data fields. For example, only a small handful of people should be able to access salary/payroll information;
3. Standardize data collection – create a process to minimize data errors and inconsistencies;
4. Data validation – validate data being entered to prevent incomplete or inaccurate data from being entered into the system. For example, making key fields required, having valid values and date formats;
5. Data cleaning – identify and correct any errors such as missing values, outliers, or duplicate records; and
6. Data quality metrics – continually monitor and report on the quality of the data identifying any areas that need improvement.

Otherwise, you may be a victim of the expression “garbage in, garbage out” which will affect your reporting. You want to make sure your information is relevant, accurate, and timely so that the business has actionable reporting that is reliable and can be trusted.

For more information on the importance of good quality data for actionable reporting and AI, follow me on LinkedIn!