Editor's note: This article was originally posted on LinkedIn here: https://www.linkedin.com/pulse/understanding-dos-donts-using-ai-data-quality-john-muehling-oiasc
AI plays an important role but will not solve data quality and reliability challenges alone.
Artificial intelligence (AI) is gaining widespread recognition as a transformative tool for addressing a multitude of business challenges. The phrase “I can use AI” has become synonymous with the pursuit of efficiency and automation.
While AI holds immense potential, it’s not a magical fix, especially when it comes to ensuring data quality and reliability. Achieving these requires more than just an algorithm—it demands strategy and effort.
The pursuit of dataquality and reliability is not a new challenge. Yet, AI stands at the intersection of both the problem and its solution, offering innovative ways to tackle long-standing issues.
Preparing your data to be “AI-ready” has become a top priority for many organizations investing in data initiatives with Datagence. We’ve explored this topic extensively on our blog, but this article shifts the focus to the practical do’s and don’ts of leveraging AI to tackle data quality and reliability challenges.
As a former data and marketing operations specialist and consultant who has guided hundreds of companies through the complexities of data quality and integrity, I can tell you this: AI alone is not a cure-all for your data issues. While AI is a powerful tool for enhancing data reliability, it must work in harmony with human expertise, well-defined processes, purpose-built tools, and a strategic approach to implementation.
But I use ChatGPT for everything. Why won’t it solve my data quality and reliability challenges?
This is a common concern raised by business and technology leaders.
If a subscription to ChatGPT can handle your day-to-day questions and execute programs using the vast knowledge of existing models, why can’t it clean your data?
The answer lies in how ChatGPT operates. When you use it, you’re contributing data to its global model, but the answers you receive are not trained on the unique preferences or requirements of your business.
And even more importantly, would you entrust your proprietary business data to ChatGPT, knowing it could potentially be part of a shared, global system? Would you rely on it to make high-stakes, business-critical decisions, potentially involving or impacting millions of dollars?
If companies like Salesforce, CitiBank, BMW, and Toshiba aren’t using ChatGPT, why should you?
The unfortunate reality is that most companies lack the resources to develop proprietary AI engines, train large language models (LLMs), or hire teams of data scientists and experts dedicated to assisting them in achieving data quality and reliability. The result? Significant investments of both time and money, often with limited returns.
This is where a comprehensive, end-to-end solution like Datagence comes in. By combining expertise, AI-driven technology, streamlined processes, and specialized tools, Datagence helps businesses effectively address their data quality and reliability challenges without the need for costly in-house development.
Where and how to apply AI to achieve data reliability
Imagine handing a toddler power tools without any training or supervision—you can probably predict the outcome. This is what it’s like to let AI operate unchecked without the guidance of data and AI experts to train and oversee it.
To achieve true data reliability—whether migrating data to the cloud, implementing new software, migrating to a new system, cleaning your database, or preparing your data for AI applications—you need a strategic plan. That plan should include experts, advanced technology, structured processes, and AI working together seamlessly.
Here are the do’s and don’ts we’ve learned about using AI to aid in achieving data quality and reliability.
1. Don’t let AI run loose without establishing your data quality standards
One of the most valuable aspects of data reliability is enabling your data to move freely across systems, processes, and by authorized users. Achieving this requires established data standards tailored to your unique business needs. AI alone cannot define or enforce these standards—it needs a framework to guide its processing and cleansing efforts.
This is where data quality experts come in. They leverage their knowledge of global standards, industry best practices, and your specific requirements to design a robust framework. Advanced unification technologies powered by AI can then be used to execute within this structure, ensuring alignment with your systems and processes.
2. Do create a data governance framework to ensure data integrity
Achieving high-quality data is difficult; maintaining it is even harder. A data governance framework establishes rules and guidelines for how your organization manages its data to ensure security, reliability, and compliance over time.
AI can support governance initiatives but cannot create or implement a framework on its own. Experts are essential to designing a governance structure that aligns with industry standards, securing executive buy-in, deploying tools, and training employees to maintain compliance and consistency.
3. Do engage experts skilled in prompt engineering
You’re not going to use ChatGPT to power your company’s AI engine, so what’s the next step? Buying an off-the-shelf AI model won’t instantly solve your data problems. These models aren’t pre-trained to understand your specific business requirements.
To make them useful, you’ll need data scientists to train the model using relevant datasets and established standards. This process can take months before the AI is viable for your projects. Alternatively, partnering with providers who offer proprietary AI models already trained on hundreds of data quality projects can fast-track this process. These models can be fine-tuned to your specific needs, saving you time and effort.
4. Do work with experts who understand the right AI prompts
Once you have a trained AI model aligned with your standards and requirements, effective execution hinges on crafting the right prompts. This requires expertise in prompt engineering to guide the model in producing the desired outputs. As AI evolves, your experts must stay informed about the latest advancements to maximize the model’s performance and ensure successful outcomes.
5. Don’t assume AI results will integrate seamlessly with your systems
After investing significant effort into preparing and deploying AI, it’s easy to expect flawless compatibility with your CRM, ERP, Marketing Automation, or other systems. However, AI often produces outputs like JSON files that require further processing.
To ensure smooth integration, you’ll need a data expert familiar with programming languages and data pipelines to translate these outputs into usable formats. They’ll also ensure that the data adheres to your established rules and flows correctly back into your systems.
So, can AI solve your data quality challenges?
If you’re beginning to question AI’s role in solving data quality issues, you’re not alone. AI can be a powerful tool, but its success hinges on a collaborative approach that includes expert guidance, robust processes, and tailored technology.
Here’s the good news.
Companies don’t have to start from scratch, wasting precious time and budget. Within the time it might take a company to hire a team, onboard a data platform or tool, and define and get consensus on data standards and a governance plan, it could already be experiencing the benefits of a unified, clean, reliable, and trusted data set.
Datagence brings data reliability and AI experts, trained AI models with global standards and best practices, and a powerful data reliability engine enabling a full-service, end-to-end solution. So you don’t have to do it alone. We work closely with your stakeholders to define and deliver a solution that is as unique as your business.
The team at Datagence has been tackling data quality challenges for decades. We understand what it really takes to achieve data reliability. It’s our mission and our focus.
Stop denying that you have a data problem and end the struggle to clean it up – partner with the experts at Datagence today.
Outtakes
Please look at the header image for the article again. Not too bad, right? Well, let me tell you that the image you see is the result of SEVEN (7) requests to ChatGPT to execute based on the following prompt:
“can you create an image for an article called ‘Understanding the Do’s and Don’ts of Using AI for Data Quality and Reliability.'”
The first six (6) versions are below; here’s a link to the thread of prompts.
So, I ask again…Do you really want to lean on AI alone to help your company achieve data reliability?? For crying out loud…it can’t even spell “reliability.”
