5 Ways to Spring Clean Your Data (and How AI-Native MDM Can Help)

It’s the spring equinox, an event that symbolizes renewal, growth, and balance. Days are getting longer, flowers begin to bloom, and, for many of us, the urge to spring clean our homes kicks in. But spring isn’t just a great time to deep clean your home. It’s also the perfect opportunity to clean up your data. 

Over time, messy, outdated, or duplicate data piles up, making it harder to find insights and difficult to make confident decisions. Just like a good spring cleaning clears the clutter and gets rid of the dust, a good data cleansing ensures that your critical entities are accurate, complete, and up-to-date. Let’s explore five ways to spring clean your data – and how AI-native MDM can help.

‍1. Declutter duplicate records 

Duplicate records wreak havoc on your data, leading to inaccurate insights and skewed results. But for many organizations, duplicate data is inevitable, especially when critical data such as customer information lives in different systems across multiple divisions or departments. And if your organization recently went through a merger or acquisition, the duplicate data situation could be even more acute. 

Clearing the clutter from duplicate records involves the use of artificial intelligence (AI) to spot and flag potential duplicates. Using persistent IDs, referential matching, and AI-powered semantic search, solutions like Tamr’s AI-native MDM can surface and resolve duplicate records quickly. Further, using capabilities like Tamr RealTime, organizations can match new data with existing source system records while the data is still in motion, preventing clutter from collecting in the first place. 

‍2. Deep-clean key business entities

Entity resolution is a data management process that identifies and links records across multiple data sources and datasets to create a unified view, or "golden record," that represents the best version of your critical business entities. Employing entity resolution is the perfect way to deep clean the entities that matter most to your business such as customers, suppliers, products, or patients. 

When it comes to entity resolution, AI-native MDM is the best tool for the job, blending the power of AI with the expertise of humans. Using advanced AI, organizations can automatically identify and resolve inconsistencies across their key business entities. Human oversight then validates the results, lending credence to them by providing the review needed to ensure they are accurate. This perfect blend of AI and human oversight results in cleaner, shinier golden records that are ready for use in analytics and operational use cases. 

‍3. Freshen-up incomplete information

When your data is incomplete, so are your insights. Fragmented data leads to inaccurate insights and missed opportunities. But the good news is that data enrichment can help.

Data enrichment is a tool in your data cleaning bucket that enables organizations to tap into external data sources to fill in blank fields or add new columns to the data, making it more accurate and complete. Using Tamr’s AI-native MDM, you can take your spring cleaning one step further using ML-driven referential matching to spot relationships that are impossible to see without external data. As a result, you’ll have the best, most complete version of your data. 

‍4. Sweep away outliers

Outliers deviate from the rest of your data set, causing unnecessary clutter in your results. But sweeping them away requires you to do more than just identify them; you also need to decide how to handle them. In some cases, sweeping them away is the right course of action, while other times, you may need to transform them or analyze them separately.

AI-native MDM provides the ability to quickly spot outliers in your data so you can determine how best to handle them. Manually evaluating every outlier is tedious, but using advanced AI, the task becomes much faster. With AI-native MDM, you can scrub deep into the depths of your data to find outliers and sweep them away, clearing out space for the trustworthy insights you need to make better decisions.  

‍5. Scrub records for accuracy

Inaccurate data causes all sorts of issues ranging from bad decisions and inefficient operations or worse, poor-quality customer experiences or failure to comply with regulatory mandates. That’s why, as part of your data cleaning, you must scrub away the inaccuracies in your data so it’s pristine and clean for the season ahead. 

AI-native MDM makes it easy to clean your data, but instead of going in with bleach and scrub brush, it’s better to follow the MDM journey. To start, assess your data to determine how dirty your data is – and which entities require the most scouring. This helps you to prioritize what you scrub now – and what you’ll get to another time. Then, move forward with improving the quality of the data. Once your data is shiny and clean, the next step is to review it with key business stakeholders to validate that buffed and polished data is accurate and ready for use in analytics and operational use cases. This journey-based approach not only makes your data cleaning more efficient, but it also ensures that you are focusing your energy on cleaning the data that is most valuable to your business. 

Just like a good spring cleaning refreshes your home, tidying up your data brings clarity to insights, increases efficiency, and drives better decision-making. By decluttering duplicate records, scrubbing away inaccuracies and outliers, and freshening up incomplete information, you create a stronger foundation that leads to better insights and smoother operations. Remember, a little bit of elbow grease now can prevent big headaches later. So, as you embrace the season of renewal, remember to give your data the deep clean it deserves.