Experian’s 2022 Global Data Management Research shows that 85% of businesses acknowledge their operations suffer from incorrect customer data, and 77% experience challenges in market responsiveness because of this. [1]
More than just a bother, dirty data is a major problem that every company is currently dealing with. Inaccurate information undermines efficiency and trust at every level, from increased expenses to lost opportunities.
Automation cannot be successful if the data that powers it is faulty. In this blog, we’ll examine why precise, clean data is the cornerstone of automation and the secret to achieving long-term success.
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Why is clean data the unsung hero of automation?
Advanced workflows, AI, and machine learning are frequently the first technologies that come to mind when someone thinks of automation. But behind all of this, data quality is the one element that subtly determines success.
In contrast to 47% of organisations where data quality remained the same, 75% of those that improved their data quality in the previous year were more likely to surpass their goals, according to Experian’s 2022 Global Data Management Research report. Automation produces accurate insights, prompt actions, and dependable results when the data is clean. [1]
The report also emphasises several issues with dirty data:
– 42% of businesses experience wasted resources and increased costs.
– 39% report a negative impact on customer experience.
– 38% say it undermines trust in their analytics.
Automation exists to expand business operations and speed up decision-making processes, but when inputs contain errors, the system will multiply both inefficiencies and mistakes.
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How clean is “clean data”?
The term “clean data” seems similar initially since it refers to data that contains no errors. The actual definition extends beyond this simple description. Clean data requires trustworthiness alongside concise, complete, and consistent information ready for confident action.
Truly clean data can be defined by five essential factors:
– Accuracy: Real-world values are reflected in data; even the best automation can be derailed by inaccurate information, such as an out-of-date phone number.
– Validity: Data follows proper guidelines and structures, avoiding mistakes that can disrupt automated processes.
– Completeness: Effectiveness is limited by missing fields; complete data provides automation with all the context it needs to function properly.
– Consistency: Decision-making is slowed down by fewer silos and confusion when records are kept consistent across systems.
– Uniformity: Because values share a common format or unit, automation can compare and process data with ease.
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When these features work together, they establish a base where automation can achieve high performance through faster, smarter, more precise, and dependable results. When data is clean, it minimises mistakes and builds confidence, empowering automated systems to do what they do best – elevating the user’s efficiency.
Tips to clean your data for automation
In fact, data cleaning is not a one-off task. It is an ongoing process that requires clear strategies and consistent effort. Organisations can build a dependable foundation for automation by implementing continuous data quality practices, which transform scattered information into trusted data sets.
Speak the same language
Mismatches are unavoidable when data originates from different sources. Establishing common standards first and foremost, such as how names, dates, or addresses should be recorded, can serve as a single source of truth for the future. How smoothly the automation solution processes the company’s information depends on this common “language.”
Data quality training
Tools and standards achieve their maximum potential only when employees have proper knowledge about their correct application. The organisation should conduct ongoing training sessions about data quality to help staff members grasp the significance of clean data and the impact of their daily work on it. The practice leads to fewer initial mistakes while creating an environment in which every team member treats data as an essential business resource.
Keep an eye on quality
Data naturally “decays” over time because customers relocate, preferences change, and system modifications occur. Organisations can detect problems early in automated workflows by monitoring essential performance indicators, such as accuracy and completeness.
Let technology do the heavy lifting
Today’s data volumes are too large for manual checks to handle. Automation solutions help identify duplicates, validate inputs, and update records, all done automatically, so organisations always have accurate data on hand.
Make ownership clear
Data cleansing operation demands collaborative work, yet establishing responsibility remains essential. When you assign the data quality caretaker role to specific professionals, they guarantee the continuous protection of your information integrity. Establishing ownership maintains the strategic value of data assets while avoiding mistakes in your organisation.
Clean data becomes more than just a maintenance task when it is viewed as a continuous process as opposed to a temporary solution; it becomes a strategic enabler that makes it possible for automation to provide long-term benefits.
References:
[2] https://www.tableau.com/learn/articles/what-is-data-cleaning
[3] https://www.ibm.com/products/tutorials/6-pillars-of-data-quality-and-how-to-improve-your-data
