Organizations have a lot of data and need to collect and transform it into information and actionable reports. Businesses require relevant, accurate and timely information for decision making, problem solving and continuous improvement.
For example, the information may show trends or identify issues that need improvement or attention to improve performance. And when there is a continuous feedback mechanism, the information can be used to measure the effectiveness of improvement efforts and make data-driven adjustments as needed to achieve better results.
Some organizations go a step further and use artificial intelligence (AI), such as ChatGPT and Bard, for additional insights. Organizations use chatbots for customer service inquiries and automate tasks and create different types of content, saving time. Organizations also use data to analyze performance metrics, identify areas of inefficiency, and even analyze historical data to make predictions about future trends.
AI models can use historical data to make predictions, providing valuable insights. Make sure you have corporate governance policies for responsible and ethical use of AI and to minimize risks. This includes things like use cases, what it can (and can’t) be used for, where (public AI vs. private instance), data privacy, and more.
As a result, data quality has become more important than ever. Making sure your data is as clean as possible is an important step. Some signs that you have dirty data are:
- Data entry errors. individuals occasionally make errors such as spelling errors, transposed digits, or other inappropriate formatting;
- Missing data;
- Duplicate data; and:
- Data source inconsistencies – data from different sources that have inconsistent or conflicting data.
With AI, if your data is incorrect, incomplete, or contains errors, the output may be misleading. Good data quality contributes to the model’s ability to effectively manage different inputs and scenarios. Also, ensuring that your data is diverse and free of bias is essential to creating AI solutions that are fair and inclusive. Otherwise, you may introduce biases that lead to unfair or unintended results.
How do you know you might have a problem? If you receive comments from end users that data appears incomplete or outdated (late), you should investigate. Or if you receive complaints from external customers about their account information. Collaborate with data owners or subject matter experts (SMEs) to identify aid discrepancies/anomalies and how to correct existing and current data.
Additionally, if your organization has been the victim of a security breach or unauthorized access, ensure that data has not been altered, damaged or contaminated. Take the time to make sure the data is still accurate and reliable.
Data management framework
It starts with having a comprehensive data management framework in place and should be an ongoing process because data quality is not a “one and done”. This includes, but is not limited to:
- Data governance framework – having policies and procedures in place to establish and enforce data quality standards and data ownership within the organization;
- Data security – the data owner must decide who should have access to certain data fields. For example, only a small number of people should be able to access salary/salary information;
- Standardize data collection – create a process to minimize data errors and inconsistencies;
- Data Validation – Validate data being entered to prevent incomplete or inaccurate data from being entered into the system. For example, making key fields mandatory to have valid values and date formats;
- Data cleaning – detect and correct any errors such as missing values, outliers or duplicate records; and:
- Data quality metrics – continuously monitor and report on data quality, identifying any areas that need improvement.
Otherwise, you may fall victim to a “garbage in, garbage out” scenario that will affect your reporting. You want to make sure your information is relevant, accurate and timely so that the business has actionable reports that are reliable and can be trusted.
For more information on actionable reporting and the importance of good quality data for AI, Follow me on LinkedIn!
From articles on your site
Related articles on the web