According to research from Experian, 95 percent of business leaders report a negative impact to their business due to poor data quality. What’s more, more than half lack trust in their data assets, preventing them from becoming fully data driven.
Whether it’s a misplaced number in a business-critical Excel spreadsheet, or a skewed dataset that impedes your data pipeline, data errors occur all the time. But here’s the thing about most data errors: they’re avoidable. Human error, for instance, causes 60 percent of them.
But avoiding data errors is about more than robust policies and internal data awareness campaigns. It’s about knowing the specific vulnerabilities your business faces and proactively addressing them before they affect performance.
In this blog, we showcase some of the common scenarios caused by data errors. We explain how you can avoid them to guarantee optimized, clean and – most importantly – actionable data.
Trust is an earned trait, and as such, it is quickly lost. When it comes to data management, one false move can lead not only to lost trust in the data itself, but also in the team responsible for it.
The knock-on effects caused by data integrity challenges are massive, too. Making informed and effective decisions across the organization, for example, becomes almost impossible. What’s more, the integrity of high-level metrics – the metrics that can impact things like buy-in from investors – becomes moot. In this scenario, business leaders are left making decisions based on speculation and assumptions, not data-driven insights.
Build a ‘bad data first’ pipeline with clearly stated data validation rules that immediately and automatically reports violations of those rules. Watch our webinar to find out why building your pipeline with bad data in mind helps you effectively target and fix data quality issues, leaving you with only high-quality data for accurate analytics.
More than 80 percent of companies rely on stale data for decision making. Unlocking current (and accurate) data trends then, is the difference between being a first mover in the market and arriving late to the game. It’s also the difference between securing further investment and long-term growth and making painful cutbacks.
Inaccurate and outdated data causes a multitude of problems. It damages brand reputation, for example. It also impacts customer experience, decreases customer support services, and creates inefficiencies that ripple across the organization.
Prioritize data accuracy over data efficiency. Many organizations buy into the idea that data efficiency is the answer to revenue retention and business growth. However, data profiling and data validation are far, far more important.
Prioritising these areas over efficiency prevents you from processing inaccurate data. It also allows you to set up workflows so you can receive automated and actionable error messages and fix issues quickly. In the end, it’s accuracy like this that helps you guarantee correct and useful data for your business.
According to Digital Journal, office workers spend 30 percent of their time using Excel. And 37 percent of the people who spend more than half of their day working in Excel have never received any formal training.
Even in today’s automated workplace, Excel continues to be a primary tool used by a variety of teams. But here’s the thing about Excel: it’s a manual tool that’s prone to human error. And not only does each Excel error take an average of 8 minutes to fix, but 98 percent of people have seen an Excel error cost their employers’ money.
Revenue loss is a common consequence of data errors. Whether it’s a hit to sales performance, or an increase in administrative costs that impacts your margins, Excel (and other manual tools) will impede your growth. It’s a matter of when, not if.
Avoid manual data entry. Instead of relying on Excel, deploy a centralized data quality solution that helps you automate your data quality workflows. This helps you can save time and money in the long run.
In the first half of 2022, General Data Protection Regulation (GDPR) fines hit nearly €100 million, an increase of approximately 92 percent compared to 2021.
Whether it’s a data breach, misguided data-sharing policies, or simple human error, data errors create non-compliance. And non-compliance leads to fines. Many fines have a direct impact on bottom line performance, too. In 2021, for example, Amazon faced a record fine of $888 million for breaching data privacy regulations (regulators can fine up to four percent of a company’s revenue). In almost all cases, poor data management is a predominant cause of non-compliance fines.
Put robust data policies in place. Moreover, ensure that all your teams adhere to them. Everyone at your business is responsible for correct data management. After all, this is a business concern, not a cybersecurity concern. To avoid potential data errors that result in hefty fines, it’s up to you to ensure compliance. It’s also up to you to spread awareness and educate your teams – from your interns to your executives.
Data errors occur far, far too often. And the consequences speak for themselves. Business leaders can quickly lose trust in data, for instance. They can also suffer from inaccurate forecasting, which can impede revenue and cause non-compliance. All these scenarios are easily avoidable.
To ensure trustworthy and accurate data across your organization, take these essential steps:
To find out how CloverDX can help you navigate avoidable data errors, book a demo with us today. We’d love to help you make better business decisions using trustworthy and accurate data.