There are many maxims out there about how data has become one of the most critical resources to businesses and other organizations. At SafeGraph, we agree that institutions can make better decisions when those decisions are driven by data. However, we also offer the caveat that simply having more data to work with rarely, if ever, increases the likelihood that the right decisions will be made.
In fact, we would argue it’s much more important for data to be accurate than abundant. Basing a decision on incorrect or irrelevant data is often worse than not having enough of the right data to support a decision. As a way of explaining how, we’ll look at why accurate data is important through each of the following sections:
Before we get too deep into things, let’s answer a fundamental question: what makes data accurate?
Accurate data refers to information that reflects reality or another source of truth. That is, it can be tested against a fact or other evidence to determine that it represents something how it actually is. This could include things like a person’s contact information or a place’s location on Earth.
Accuracy is often confused with precision, but there is a slight difference between what these two terms mean. Precision refers more to how similar or dissimilar values are compared to one another, usually measured against some other variable. So data can be accurate, precise, both, or neither.
So why is accurate data important? On a macro level, it’s part of a group of interrelated factors that affect how reliable data is for various use cases. This is referred to as “data quality”.
Here are explanations of the other attributes that contribute to data quality:
Next, let’s look at the following question through a corporate lens: “Why is it important to have accurate data?”. Modern businesses are integrating data into more and more of their operations. While this carries the promise of greater competitive advantages if done correctly, it also means there’s much more to lose if the data is wrong. The following points will illustrate why having accurate data is critical to various facets of your company.
Businesses can be more confident in the decisions they make if they have accurate and relevant data as evidence to base those decisions on. This has a number of benefits, including decreasing risk and making it easier to achieve consistent results.
More accurate data makes your business more efficient for a very simple reason. The fewer inaccuracies your company’s data has, the less time employees will have to spend finding and correcting these errors. That frees up more time for employees to work on the tasks and projects your organization wants to prioritize. It also makes it easier for your business’s various departments to work together efficiently.
WIth accurate data on your company’s customers, it becomes easier for your marketing team to know exactly what your target audience is. Accurate data also helps your business expand its advertising efforts through appealing to consumers with similar traits to those in your core customer base. It can even inform your organization’s content or product design in order to keep existing customers engaged.
Accurate data builds trust in your business from both inside and outside. Internally, quality data that helps make a more productive, reliable, and successful company can smooth the adoption of cutting-edge data-driven technologies and systems. Externally, quality data – when it’s properly managed – helps to show customers that your organization is responsive to their needs, takes their security seriously, and provides reliable information. It also simplifies compliance with ever-changing industry regulations.
In helping your business do all of these positive things, it also follows that accurate data helps your company avoid a number of pitfalls. At base, it reduces the need to spend time and money finding and fixing errors in the data. This is a resource-intensive task, and if it isn’t done properly, it can lead to further problems – especially because data errors tend to compound on top of one another.
For example, bad data can lead to mistargeted marketing efforts. This means your organization is wasting time and money advertising to demographics that aren’t likely to yield customers. Worse, this can make existing patrons feel your company is no longer catering to what they want or giving them useful information, and so they may start searching for alternatives. Poor quality data can also cause your business to run afoul of industry regulations, resulting in further damage to its credibility – not to mention expensive fines.
The above reasons list why accurate data is important to a business, but there are other benefits too:
We’ve spent much of this piece answering the question “Why is it important to ensure that data is accurate within a company?” Now, let’s approach the question from a more fundamental angle: how does data become inaccurate in the first place?
Things are always changing, so it’s impossible to get data 100% right, 100% of the time. However, there are certain processes and systems (or a lack thereof) within organizations that tend to cause data to be further away from reality than it should be. Here are five examples (along with explanations) of how to manage them to avoid data quality degradation.
Human error is a common cause of inaccuracies in data. No matter how detail-oriented and careful someone is, they are still at risk of making mistakes when transcribing data. This risk increases with the more data a person has to manage, as well as with the number of people who are allowed to access and edit data.
Solution: Install systems in your organization’s databases to check for common input errors. Spell checking is a key one, but so are validation rules for making sure data is entered in the correct format and measurement. Note that even these aren’t immune to human error, so be sure to test them regularly to make sure they work properly.
It’s also a good idea to put controls in place to manage who can access and edit certain data in your organization. This reduces the risk of someone who shouldn’t be editing your company’s data tampering with it.
Another frequent cause of poor data quality is a lack of validation standards. Data could be correct, but could still cause sorting and analysis problems if there are formatting changes between similar records or multiple versions of the same record. Examples include uppercase vs. lowercase letters, punctuation, abbreviations, units of measurement, and date formats (e.g. 4/3/2022 could be April 3rd or March 4th, depending on if month-day-year or day-month-year formatting is used).
Solution: There should be organization-wide norms on how to classify different types of data, and what format each one should be in. Set out clear guidelines so there’s no ambiguity as to when a certain kind of data is being referenced and how it should be represented.
Data decay is the opposite of data timeliness. It occurs when the status of something in the real world changes, making data that refers to it no longer accurate or relevant. This usually happens when certain data is not used or accessed for an extended period of time. And that is often a symptom of a company investing too heavily in data collection instead of tools to clean, sort, and manage data in a timely manner.
Solution: Have a diligent data team that stays on top of potential changes to data and revises it regularly. Investing in automated data management systems and/or dedicated data quality tools can help as well. A more general way to address this problem is to focus on collecting relevant and accurate data for your business, rather than try to collect as much data as possible.
Data siloing refers to a problem where data someone within an organization needs is somewhere inside that same organization, but the person cannot access it. They may lack the proper authorization credentials for that space, or they may not even know the data exists there. This can prompt an employee to try and find comparable data from outside sources. And that can cause data consistency issues due to duplicate records, especially if the outside data is different in content or format than what an organization already has on file.
Solution: Similar to with data standardization, having a well-defined system of validation rules and categorization for what certain types of data are (and aren’t) can help reduce inconsistencies. Another step that can be useful is to invest in a dedicated data catalog solution. This can help people in your organization know what data is available to them, evaluate its relevance to a particular use case, and seamlessly gain access to it.
A general reason why data inaccuracy can occur at an organization is employees have not been trained to pay attention to data quality. This is because, traditionally, it’s been thought to only be important to IT teams and BI specialists. Other employees typically focus on their tasks without even realizing they may be causing data accuracy errors, and address incorrect data only after it results in a costly mistake.
Solution: It’s critical that all members of a business – not just the IT and BI people – be educated on why data quality is important. They should be taught how to maintain data accuracy in the course of their work, including how to use modern data quality tools to clean and manage data. This is especially paramount as data becomes increasingly essential to modern business decisions, and as business intelligence tools become more accessible for any type of employee.
Let’s digress one more time from the question of “Why is detailed and accurate data important for my business?” In the previous section, we discussed some reasons why an organization’s data may not be as accurate as it should be. Here, we’ll look at the other side of the coin and share some guidelines on how to keep your company’s data quality from degrading.
A big part of why SafeGraph is able to deliver some of the highest-quality data in the industry is because it’s our sole focus. Many of our competitors curate geospatial data as just one part of a larger suite of services, including things like data management platforms, data visualization software, and other data analysis tools. SafeGraph doesn’t have any of these other things; we devote our entire operation to sourcing, cleaning, and distributing the highest-quality data we can, as fast as we can.
To illustrate, our point of interest dataset – Places – is curated through three main steps. First, we crawl public web domains and use publicly available APIs for accurate and up-to-date information about all different types of POIs and information about them. Next, we license third-party datasets to fill in any gaps we find in the public information we collected. Finally, we pass the metadata for all of the places we find through a rigorous de-duping and merging process. This allows us to standardize address formats, merge or remove duplicate records, and assign relevant place subcategories.
And since data is our entire business, we can complete these processes for all of our datasets to remain fresh on a monthly basis. This allows us to not only expand our datasets more frequently, but also ensure they maintain their accuracy and completeness for longer periods of time. In contrast, other companies in our industry publish updates to their data only quarterly or semiannually on average.
Merely analyzing any and all data your business can gather won’t necessarily lead to better decision-making. On the contrary, your company could be hurting itself if it draws the wrong conclusions from the data – and there are many reasons this could happen. The data could be irrelevant to your organization’s goals, significantly outdated, or simply not indicative of how things really are.
That’s why having accurate data is a vital part of building a solid foundation for your business’s operations and strategies. The importance of accurate data in healthcare, finance, urban planning, retail, marketing, and many other industries cannot be overstated. Even otherwise correct decisions, when guided by incorrect data, can leave your organization no further ahead – or, in a worst-case scenario, even further behind.