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For Danielle Rifkin, a master’s degree candidate currently studying social research methods at the London School of Economics, data cleaning has its ups and downs. Some of the challenges she’s encountered include measurement units being off, naming inconsistencies, and time variations of single variables. But despite her frustrations, she still genuinely enjoys the process of cleaning data.
“I wonder if all those people who are like ‘cleaning relaxes me’ think I’m as bonkers for loving to wrangle a messy data set as I think they are for going to town on the baseboards,” Rifkin says.
Lucky for her, being a fan of cleaning data will definitely come in handy, especially with the proliferation of AI in her field. While Rifkin admits that she wasn’t exactly on board as an early adopter of AI and LLMs, becoming a student again has changed her perspective on the value of working with AI. And this newfound openness means she gets to do what she loves—pull together disparate data sets and tidy them up for ingestion.
Why clean data is essential for AI success
When it comes to AI, your data isn’t just an input; it’s the bedrock of every decision and insight your AI can provide. What happens when the data foundation isn’t solid? Much like building a house on shaky ground can lead to problems down the line, trying to build AI on inconsistent or incomplete data can lead to unreliable results, misguided strategies, and an insecure structure. That’s why it’s essential to make sure your data is clean and orderly before launching any AI initiative.
“You can build a function or a model that runs over and over again but only if you’re feeding it data that it knows how to parse,” Rifkin points out.
Part of this preparation includes undertaking more technical tasks to address some of those pain points that Rifkin struggles with—tasks that will ensure your data is AI ready. The other part is getting the right people, systems, and processes in place. But by establishing a strong foundation, improving your data integrity and security, and fostering a data-quality culture, you can make sure the data you are feeding into your AI will result in models that deliver meaningful insights.
Step 1: Data cleansing best practices
It shouldn’t come as a surprise: To get better-quality AI results, you need better-quality data.
Start with the right rows in your data set
Which data rows you need depends on how you plan to use the information. That's why starting with the right sample matters. At first, your data set may have some of the right rows, some of the wrong ones, and some that are missing entirely.
Sit down with the stakeholders involved and think concretely about what you want from your AI project. For example, if your goal is to predict employee turnover, you should consider:
- Who qualifies as an employee?
- What kind of turnover are you considering?
- What time period are you looking at?
You may need to delete rows of data or add more rows to complete your data set. This upfront work takes time, but it’s less labor intensive than having to go back and prepare your data all over again.
Clean and standardize your data set
Data cleansing is like preparing your kitchen before you start cooking. It’s essential for keeping your AI effective and efficient. Begin with removing duplicate entries to prevent the same information from skewing your analysis. Then move on to making your data formats consistent. For instance, all dates should be in YYYY-MM-DD format to avoid confusion and errors in time-based analyses.
Cross-check data against reality
Let’s go back to the turnover question: Do the hourly wages of each employee make sense given the population’s minimum wage? Are there surprising outliers? If so, don’t just get rid of these values, investigate them. In this case, check the numbers with your human resources director. Even tiny typos can throw off your analysis.
Apply validation rules to catch errors
Once your data has been cleaned, apply validation rules to automatically highlight potential errors. For instance, a salary field showing a negative number should automatically trigger a review. Machine learning models can predict typical error patterns based on historical corrections and automate fixes for these issues.
Step 2: Improve data integrity and governance
Cleaning a data set isn’t just a one-off occurrence; you’ll want to invest in making sure the proper procedures are in place to keep your data clean.
Strategies for dealing with missing data
Missing data can be misleading; it might not seem like a big deal until your AI starts producing biased results. You don’t have a complete picture of your data when your data set has missing pieces. Some algorithms can’t handle missing values, which means they’re learning from faulty information.
Develop a strategy that fits your AI’s needs, whether it’s using statistical imputation to fill in missing values or taking algorithmic approaches that adapt to gaps in data. Our data scientists walk you through their process in part 1 of our AI Insights livestream series.
Conduct regular data audits
Follow up with regular data audits. Think of audits as detective work for your data, where you hunt down inaccuracies or missing bits that could chip away at your AI’s foundation. As mentioned, automated tools can help you spot anomalies, making sure your data stays pristine.
Establish a strong data governance framework
Now that you have great data, you need to make sure it’s secure. As you implement AI, set up a comprehensive data governance framework that defines who can access which data sets and under what conditions. This should include not only permissions but also tracking who accessed what data and when, to keep your organization accountable and compliant with data protection regulations.
Educate your team on data security best practices
Provide ongoing education and workshops for all employees about why data quality matters and their roles in maintaining it. You could also establish key performance indicators (KPIs) related to data quality and include them in performance evaluations.
Step 3: Build a data-quality culture
If you’re working at an organization, you’re likely not the only person touching data. Implementing best practices requires teamwork.
Engage your team in data quality efforts
Involve your team in maintaining data quality. Encourage them to identify potential areas of improvement and suggest solutions. This not only improves your data but also helps cultivate a culture of quality across your organization. Celebrating these contributions can boost morale and encourage a proactive approach to data management.
Review and update data practices regularly
Data requirements and technologies evolve, so your approach to data management should, too. Regularly review your data practices and stay updated on solutions for improving the quality and security of your data.
Listen to feedback and encourage continuous improvement
Opening up a dialogue about data quality within your organization can lead to new insights and improvements. Encourage feedback and use it as a stepping stone to better practices.
Bring it all together: Build your AI on a foundation you can trust
Creating a solid foundation ensures that your AI system and the data feeding into it are safe, secure, stable, and accurate. The steps outlined here—cleansing, governance, and building a data-quality culture—are essential to creating that foundation.
But consistently improving and adapting to new challenges is key to maintaining high-quality data and getting better results from your AI. Of course, it helps to have a strong arsenal of data tools in your back pocket to simplify these processes, even if it means embracing new technologies like Rifkin did.
With Domo, you can manage every part of your data pipeline—from cleansing and transformation to governance and AI integration—in a single platform. Domo’s transparent tools for cleansing, governance, and integration ensure that every AI initiative starts on solid ground.
Hear from customers who have discovered the value of clean data and high-quality AI insights with Domo. If you want to see Domo in action yourself, schedule a demo with our sales team who can show you all of the evolving features our platform and products have to offer.
And when you’re ready to strengthen your organization’s data foundation and accelerate your AI strategy, take the next step:
Download the AI Readiness Guide to assess your current state and learn where to focus first.
