Editor’s Note: Sean McGuire is a skilled Database Administrator for Apex42. Sean has over a decade of experience helping clients with their “dirty data” by cleaning and standardizing their real estate portfolio data, truly making him an expert in his craft.
In today’s world, we’re inundated with data—everyone wants it, and there’s an overwhelming amount of it available. But here’s the catch: data is only valuable if it’s usable. Without proper structure, accuracy, and accessibility, all that data is just noise or dirty data. And more often than not, when we finally get the data we need, it’s a mess. In fact, 70% of CRE leaders say they don’t have the data they need to make critical decisions. In our experience, the problem isn’t the absence of data—it’s that the data is locked away in silos, poorly structured, disorganized, and difficult to access. Simply put, the data is dirty!
The reality is data is inherently messy and is at the heart of many business challenges today. From slowing down decision-making processes to eroding trust and creating security vulnerabilities, dirty data is a significant roadblock. It hampers the ability to analyze data effectively, leading to flawed insights, misguided metrics, and ultimately, poor business decisions. Confluent even found 58% of leaders make decisions on gut feel because it’s too difficult to access the right insights in real time. Plus, it can impact technology adoption—how often have you found yourself frustrated with a tool, only to wonder: Is it the software that’s the problem, or is it the data and processes behind it?
The Many Faces of Dirty Data
Dirty data isn’t just a vague concept—it manifests in several distinct forms, each of which poses unique challenges. Here are the most common categories of dirty data, and how to address them:
1. Poor quality: There are six dimensions used to assess data quality.
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- Accuracy: Incorrect data could include anything from wrongly entered details to outdated or poorly validated records. This can lead to misguided decisions based on inaccurate information.
- Completeness: Sometimes records are missing important attributes, and these gaps can make it difficult to generate accurate insights.
- Consistency: Inconsistent data occurs when information is recorded differently across different datasets. For example, a company might store customer details in both its CRM and accounting platforms, but the entries may not match up.
- Timeliness: For data to be most useful, it must be available when needed, which is increasingly becoming “real time.”
- Validity: When data is entered in different formats or with varying units of measurement, it becomes difficult to analyze effectively. For instance, dates may be recorded as MM/DD/YYYY in one system and DD/MM/YYYY in another.
- Uniqueness: When data is duplicated it creates redundancy.
Solutions: Following a data governance strategy helps to identify and resolve data quality issues.
2. Stale Data: Once data has become outdated or obsolete it no longer provides value for generating insights and could lead to misguided decisions and decreased efficiency.
Solutions: Establish data monitoring processes to track data health and dispose of outdated or obsolete data.
3. Insecure Data: Real estate data alone doesn’t contain PII (Personally Identifiable Information), but it’s often coupled with demand data which does. Employee IDs, names, and even home addresses, needs to be securely stored. Failure to ensure this can lead to significant security risks.
Solutions: Ensure proper access control across the organization and contractors so that information is only accessible to authorized users.
Dirty data may seem like a minor inconvenience, but it can have serious consequences for your business. Identifying and addressing these issues is the first step in unlocking the true potential of your data. In the next section, we’ll explore the tools and strategies that can help clean up your data and put it to work for you.
The Path to Clean Data
If the data is clean, the rest is easy! Too often, data cleaning efforts are bespoke and treated as one-off tasks—done for a specific purpose at a single point in time. This approach is not only expensive and inefficient, but it can also expose an organization to security risks due to the lack of standardized processes. Achieving data health isn’t a quick fix—it requires a systematic change and the establishment of ongoing procedures to keep data clean.
To build a solid, sustainable data foundation that will create a virtuous cycle and save your organization time (and money), build trust, and set you up increased speed to market, there are several critical steps to follow in order to reduce, and even overcome, dirty data. Here are the key actions to take:
- Inventory: Assess what data you have, where it is stored, who has access, and the overall quality. This will help better understand what options str available to better leverage the data you do have. As we heard on a Gartner webinar, “Gathering data is all about context,” and establishing your inventory with a focus on what questions you want answered will set the contextual foundation for your clean data journey.
- Data Governance: Establish business processes that result in good data (and define what “good data” means to your organization!)
- Storage: Create an aggregated repository for the ingestion of your organization’s datasets that will serve as the source of truth when generating insights
- Map and automate: Replace bespoke processes with consistent and standardized automated processes
- Iterate: Focus on one thing at a time with a goal of continuously improving
The level of effort will vary depending on each organization’s unique business processes, systems, and configurations, but cleaning up dirty data is entirely achievable. In fact, it’s crucial for any organization that wants to stay ahead, especially when considering the future capabilities of AI and other advanced technologies. The task can seem daunting, however, with the right partner, you can turn your real estate data into action. Embracing clean data today ensures that you’ll be ready to leverage these innovations tomorrow.
The Value of Refined Data
It’s often said that “data is the new oil,” and much like oil, its true value is only realized once it’s been mined, refined, and properly distributed. You do have data—it’s just raw and dirty, locked in silos, or poorly structured. This is why you’re unable to draw meaningful insights from it. As we move towards a future dominated by machine learning (ML) and artificial intelligence (AI), the power of these technologies can only be harnessed with clean, refined data. Without organized data systems in place today, the promise of future insights will remain unfulfilled.
Don’t let your next big decision be based on dirty data! Clean, well-organized data is key to making confident real estate decisions and you don’t have to handle it all on your own, real estate data partners can handle the complex tasks making your data accessible when you need it. As the pace of business accelerates, waiting too long to build agile CRE workflows will slow you down. Now’s the time to get your data in shape so your real estate assets are ready to move at the pace of your business, allowing you to act quickly and strategically when it counts. The future of intelligent decision-making depends on it.
Learn How to Get your Data Prepped for Actionable Real Estate Insights