When ChatGPT was released at the end of 2022, it caught the attention of the world. People were amazed at the platform’s ability to answer nearly every question (and still fail occasionally) and generate content from news articles to poetry.
What has since become clear is that generative AI like ChatGPT can also play an important role when it comes to data quality. The use of artificial intelligence (AI) tools in data processing has tended to focus on predictive analytics, but is now moving towards natural language processing, data analytics, and automation.
Fast forward to 2023 and the scope of AI’s use has expanded exponentially. In fact, according to the 2023 Data and Analytics Leadership Annual Executive Survey, 80.5% of his data executives said AI and machine learning (ML) will be the areas where data and analytics investments will increase in 2023. I am answering. For 16.3% of them, is the top investment priority.
This is because data quality has become critical to effective business management. This enables organizations to make informed decisions based on accurate, complete, and consistent information. Poor data quality can lead to inaccurate results in decision making, financial loss, and damage to brand image and reputation.
Improving data quality with generative AI
This makes generative AI components attractive to data analysts and a reason for significant investment planning. The result is a different way of managing and analyzing data, with even greater business benefits.
According to analyst firm Gartner, by 2025 at least half of all data management tasks will be automated. Most of this automation is achieved using AI/ML-driven tools, especially generative language models.
This change has a significant impact on data quality. These technologies have the potential to revolutionize data management by automating and simplifying tasks like never before. This advancement is expected to improve the accuracy, completeness and consistency of an organization’s data handling processes.
As an example, improvements can be achieved by following a two-step process. First, a technical data quality assessment is performed using ML algorithms that can identify anomalies and quantify the severity of issues.
Based on the evaluation results, it then uses a generative language model to suggest data quality rules and transformations in natural language that business stakeholders can easily understand.
AI-generated rules and prompts
Generative AI also helps companies automate data reviews and create recommended rules and prompts based on the results.
For example, a company that operates an online store can use AI tools to check whether order dates are acceptable and whether customer details are correct.
Business administrators can also create additional rules by simply asking in natural language without writing code or developing complex UIs. Once a new rule is approved, it can be automatically converted into executable code such as Python or SQL.
Before deploying new code to production, you should test and validate it with a sample of data to ensure that your rules work as expected. Once the data cleansing process is complete, the refined data can be used for a wide range of downstream tasks including data analysis, visualization, machine learning, and business intelligence.
data conversion
The pace of change in data management shows no sign of slowing down. The use of generative language models such as ChatGPT is still in its early stages, but its use is expected to grow.
As more companies realize the value this new generation of tools can provide, they will push implementation to the top of their to-do list. But even as exciting as its promise is, the technology is still relatively new, and to avoid distorted results in AI models, we need to be aware of this and implement in-house training, responsible practice, or Preparations such as UX improvement and fostering interaction are essential. Preparing and training your organization or team can bring great benefits. And one of the most important benefits is significantly improved data quality. soon.
