Most of us now realize that data can be a true catalyst for business growth. Again and again, we see that data-driven companies tend to have increased revenue, better customer service, highly optimized operations, and greater profitability.
Unfortunately, data inaccuracies are common, and this can make it difficult to get the most out of the valuable data you've collected. The scourge of inaccurate data will come as no surprise to anyone who has worked in this space, but the scale of the issue only continues to grow. Organizations report losing an average of $15 million per year due to “poor data quality,” according to Gartner, and this may just be the start.
Given that data environments will only continue to increase in complexity — due to use of multi-cloud, hybrid databases, or legacy systems — it’s vital for companies to find a solution to this issue as soon as possible.
But before we move on we want to make you an offer you can't refuse. 🙂 If you need an extra brain to bounce ideas off of or talk strategy, we want to help. Our team at Shipyard has seen (almost) every data problem under the sun. We've helped build plenty of solutions from scratch for Fortune 1000 brands.
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Understanding and Fixing Data Inaccuracies
To empower your teams, you need to give them the best and cleanest data possible. That means you need to root out bad data by improving any systems and processes that routinely collect bad data.
What is the first step to actually fixing this?
It all starts with identifying why they occur. Only once you know why this keeps happening and why your database is full of unusable clutter can you make the necessary moves to root out the problem.
Assuming your organization is already collecting data — whether manually or with digital tracking software — these are five common ways data inaccuracies could arise.
1. Poor Data Entry
Human error is the biggest source of data inaccuracy, specifically when manually keying in data.
In some cases, it comes down to “lazy” practices, such as entering an estimate, instead of accurate figures. But even if your team members have the best intentions, they may not always have the resources on hand to keep up with data entry requirements. A time-strapped sales representative in a rush, for example, might key in a customer’s name with typos or add an extra "0" in one of the data fields.
Process problems can also arise. Data entry may be only one of the many tasks an employee has to fulfill, and especially during busy periods, this work can get delayed for prolonged periods. Even if the information was recorded properly on paper or through a more manual capture method (in Excel or Word, for example), there may be errors introduced when transferring the data into a digitized system.\
It's important to make sure that whether your teams are entering data in their CRM system, a Google Sheet, or even a form, that you have validation in place to prevent the bad data from getting submitted in the first place. Tracking down and solving data entry issues at the source is always better than fighting a losing battle of building rule sets to clean the data of potential errors after the fact.
2. Non-standardized Data Practices
Another reason for inaccurate data is poor data practices.
If there are no standard processes in place for how data is collected, formatted, or accessed, it becomes more likely for inaccuracies to arise.
Without data formatting standardization, for example, there can be confusion, and information like dates will be collected in multiple formats. One analytics tool might track December 2 as 2/12/2022 while another might track it as 12/2/2022. This error carries on into data processing and creates a large pool of data that isn't uniform.
This can be made even worse if data accessibility is not regulated. If every person in the organization has the same access to the same databases, it’s possible for even more errors to creep in. This is common when someone makes an accidental change or reads the data inaccurately and keys it incorrectly into another database.
3. Insufficient and Incomplete Data
Not having enough data often leads to inaccuracies.
If your tracking system is not set up to require that all the properties are filled, it can lead to poor data analysis later down the road. Without sufficient data, analyses may have to be conducted with assumptions or extrapolations that will be less accurate.
This can also happen if you have set up your tracking system incorrectly, which prevents complete data collection. This creates gaps in the data and, ultimately, will lead to inaccuracies.
4. Duplicate Data and Overtracking
Sometimes, having too much of the same data is just as bad as not having the data at all.
This typically happens due to multiple tracking codes on the same page, too many third-party plugins, multiple tag management systems, or data being continuously appended without timestamps. Regardless of how it occurs, having duplicate data can lead to all sorts of problems.
Specifically, it can lead to inflated numbers or even false results during analysis. For example, if an online store records double the number of sales in a certain month, it could lead to an increased average customer spend value. While the data might make it seem like the store is performing well, this false information could lead to poor decision-making.
5. Outdated and Inefficient Systems
Believe it or not, some companies still rely on paper forms. It should go without saying that this presents a constant source of errors.
It even comes with its own set of data recording issues, including the use of acronyms and shorthand or illegible handwriting. When keeping data on paper, there is even the potential for physical damage, missing forms, or complete loss — all of which translate into errors when converting it to digital.
Even in digital format, poor form design can affect the quality of the data collected. For example, dropdown fields might not have been updated to include all the possibilities that might occur or filter settings might prevent certain data from being entered.
Or maybe you are using outdated tracking systems that do not follow the latest best practices for data collection. This can get in the way of collecting data accurately — or could even halt the data collection process without you being aware of it.
Making Sure Your Data Is Accurate
Once you’re aware of the common ways that data becomes inaccurate, you become better equipped to fix the issue. The good news is that, with some effort, these problems can be easily solved.
If the biggest issue in data is human error, simply automating and standardizing the data collection process can make a huge difference. Next would be to ensure that all your systems, tools, and architecture are regularly updated.
If you don’t have the technical skills needed to build these automated data workflows, Shipyard's modern data orchestration platform can help. We give data engineers and other data people a way to launch, monitor, and share workflows with the rest of your team without having to worry about infrastructure setup.
This way, data processing will remain up to date and keep running optimally — working every day to keep your data accurate.
Start the process of fixing your data right away by signing up for our free Developer plan. With our free plan, you can start building workflows to process and test your data, reducing overall data inaccuracies.
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