You can’t talk about analytics without at least mentioning the importance of clean data. Right now, internet users are going through unprecedented amounts of data. Most of it, however, is unstructured and even irrelevant. Enter data cleansing, a core part of any modern analytics solution. This process weeds out unnecessary data according to your predetermined use.

How does an analytics solution work for a business? The term “data analytics” refers to how a company uses data. Every day, information collects from all sorts of activity—specific details from clients, employees, transaction histories, and more. Once it enters the system, analytics filters that data so unique stakeholders can use it to update processes, adjust business approaches, and make other helpful changes.

A misconception that people often have is that data analytics is only useful for big corporations. Good data benefits businesses of every size, and you could say that the higher the stakes of a potential decision—as in a small operation—the more essential it is to have the insights analytics tools provide. The key is having the right data analytics tools.

How does a business make data work harder and achieve more? This discussion will take you through a brief overview of data cleansing, from its basic definition to its potential uses and the ways modern businesses leverage it in their daily operations.

Analytics Tools Clean Your Data Automatically

What is Data Cleansing?

As defined by Techopedia, data cleansing is:

the process of altering data in a given storage resource to make sure that it is accurate and correct. There are many ways to pursue data cleansing in various software and data storage architectures; most of them centre on the careful review of data sets and the protocols associated with any particular data storage technology.

In other words, it consists of making sure that any data you use for analysis is complete, correct, relevant, singular, and properly formatted. That means thinking about the input process as more than just deleting irrelevant data. Establishing a proactive approach ensures any data used in analytics and business intelligence is actionable.

Not everyone calls this process by the same name. You might also see it referred to as data cleaning or data scrubbing.

The Importance of Data Cleansing in any Modern Business

Think about the sheer volume of data that flows into your business and systems every minute of the day. Then, think about how much you rely on that data to understand your audience, forecast revenue cycles, and make core business decisions.

What happens if the data is inaccurate or irrelevant to the stakeholder reading it. The learnings, insights, and decision-making flowing out of it will naturally become flawed, as well. Moreover, dirty data may lead to potentially significant compliance issues in industries where compliance is vital.

Good data operations try to ensure clean, well-formulated data intake. Some inaccurate or wrong data will inevitably slip through or become erroneous over time. That’s why every business needs to have data cleansing processes and encourage feedback from those who rely on it to do their jobs.

5 Areas Organisations Can Target To Boost Data Success

Automated analytics solutions are a universal need across the business. There are a few areas where implementation becomes especially important and should be a priority:

  1. Advanced Analytics. We’ve touched on this concept above. Modern analytics goes far beyond simply looking at historical trends, seeking to become predictive in its abilities to forecast revenues and make core business decisions. Clean data helps these predictions and insights become more accurate.
  2. The Internet of Things (IoT). The IoT has become one of the largest data sources, but much of that can be irrelevant or even faulty. An efficient data cleansing process scrubs incoming data screams, reducing irregularities and improving the validity of information flowing into the system.
  3. Smart Processes. Especially in manufacturing, smart processes have drastically improved the efficiencies of factory floors. The only way to ensure accuracy and actual efficiency improvements is through clean data that will enhance, not disrupt the process.
  4. Artificial Intelligence. Increasingly a core part of modern business intelligence, artificial intelligence is impossible to implement or execute without clean data. Any business looking to leverage AI needs to have sufficient cleansing processes in place.
  5. Machine Learning. Closely related to artificial intelligence, machine learning leverages data trends to draw new conclusions and self-improve over time. Again, the need for clean data in successfully executing these concepts is self-evident.

A Basic Data Cleansing Process to Begin Implementation

The nuances of data cleansing are complex and go far beyond the scope of this introduction. Still, it’s beneficial to have a basic idea of what those processes look like as you begin to look for implementation within your organisation. At its core, that sequence consists of 5 necessary steps:

  1. Analyse your incorrect data. When you find inaccurate information, keep track of where it’s entering into the system. That way, you can identify trends and fix problems at their core, not just the symptoms.
  2. Streamline your data intake. The fewer ways you have for information to enter the system, the more quickly you can check your intentional bottlenecks and ensure you catch errors at the gate.
  3. Eliminate duplicates. Ensure you have systems in place that check for duplicate entries, so you don’t double-count them.
  4. Validate your data continually. Look for tools that help you scrub your information and cross-check it against other sources or within testing algorithms regularly.
  5. Build test reports. Before you rely on your analytics, make sure that your reporting solutions don’t output questionable data that might lead to flawed decision-making or outcomes.

Data cleansing, at its core, is a data management issue. The above steps should not be completed once but on an ongoing, real-time level to keep your information accurate and actionable. That’s how you optimise your processes and improve your business intelligence in the process.

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