Overall
Over the past decade, big data has expanded rapidly, becoming an essential component of business decision-making, goal-setting, and competitive positioning. However, with this vast amount of data comes a significant responsibility. Businesses must establish strong privacy and security protocols to ensure that the data they collect is used responsibly and adequately protected.
Equally important is the quality of the data that businesses rely on. Alarmingly, 94% of companies recognize that the data they collect and store is not entirely accurate. Relying on poor-quality data—whether knowingly or unknowingly—can lead to disastrous business outcomes. A single error stemming from inaccurate data can trigger a chain reaction of costly mistakes, jeopardizing a company's success..
Common examples of poor data quality leading to major business issues
Inaccurate bank account numbers and sensitive customer/vendor data
Even one wrong digit in vendor master data can result in incorrect purchase orders, necessitating credit memos and manual corrections that cost both time and money. Beyond administrative costs, mistakes like these may incur additional bank fees and create inefficiencies. The key to mitigating such risks is implementing double-checking mechanisms or using automated tools that can validate bank details and detect potential errors early in the process.
Lack of established data governance
Without proper data governance frameworks in place, companies expose themselves to substantial risk, including non-compliance with industry regulations and potential lawsuits. Governance structures ensure that errors and inconsistencies are caught early, minimizing the possibility of costly fixes or breaches of sensitive information.
Data silos across departments
In organizations with multiple departments, isolated data can lead to inefficiencies. For example, the sales team may input critical customer data into their system, but the marketing department—unaware of this data—may conduct unnecessary customer research. The data already exists, yet marketing is spending time and resources gathering insights that could have been used from the sales team’s database. Breaking down these silos is essential for better collaboration and more informed decision-making.
Duplicate data entries
When multiple points of data entry exist, duplication can become a problem. For instance, an IT glitch might duplicate customer data, leading to incorrect sales figures, sending multiple promotions to the same customer, or generating misleading insights about business performance. Addressing this issue at the point of entry and setting up systems to flag duplicates is crucial to maintaining data integrity.
Human error in data entry
It's impossible to eliminate human error entirely, especially when it comes to manual data entry. Simple mistakes like entering the wrong shipping address can result in costly re-shipments or product replacements. Automating address verification through postal tools or using automated data entry systems can significantly reduce these risks.
While identifying poor data quality can be challenging, the use of automated tools and machine learning can help businesses manage and clean their data more efficiently. These tools can monitor data entry points and storage systems, alerting users to potential inaccuracies without the need for manual review.
For more insights on how high-quality data can enhance business success, explore our Data services of KPMG Lighthouse to get more information.
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