Businesses often ignore the common problems that can cause data to be unreliable. Unreliable data leads to incorrect insight and faulty predictions. Research shows that a majority of businesses make faulty predictions and suffer irreversible consequences because they depend on unreliable data. As a result, businesses suffer from missteps that can prove to be expensive in the long term.
It is extremely important to ensure the trustworthiness and reliability of content and data. However, to do so, one needs to identify common problems that make data unreliable. Once you identify these problems, it becomes easier to rectify them. In this article, let us take a look at six common problems that make your data unreliable and how you can fix them easily.
Biases can originate from various sources, including human bias. Biased data is the result of cherry-picking information and data that proves your point. For example, if you want to believe that your customers are happy with your after-sales support, you are more likely to read through reviews that present you positively.
Unfortunately, this results in biased data and faulty insights.
How to fix: Make sure to audit your data for biases and prejudices, and seek the help of a neutral party (an external agency) to objectively look at your data sets.
Data generated and stored by software programs are extremely vulnerable to external threats. Threats and vulnerabilities can cause data to become corrupt or inaccurate.
There are several malicious programs that can make your data vulnerable to hacking and corruption. Some of these can surreptitiously lurk in your systems and continue to corrupt your data.
How to fix: Seek the help of data security professionals to fix vulnerabilities in your data management protocols.
Data is generated from a number of sources, across online and offline channels. Many online sources of data may not be accurate.
For instance, only 70% of online reviews were found to be reliable in a particular study, while the rest of the comments and reviews left by users were actually paid for by malicious sources. These kinds of data are neither reliable nor valid, and will lead to incorrect predictions.
How to fix: Make sure that the online sources from which you collect data are reliable and valid. If you can't do this, seek external help.
A lot o data that organizations store are not up to date. Both online and offline data can quickly become outdated, and analyzing such data is useless. Data revision is very crucial to keep the veracity of information reliable.
Outdated data ranges from inaccurate contact information to research results that are very old and invalid. Research shows that most businesses do not update their data and continue to use what they collected years ago.
How to fix: Constantly update all your data to reflect current trends and situation.
Most businesses still engage in manual entry of data across different software programs. This is not only tedious and time-taking, but is also prone to human errors and lack of precision.
Software integration reduces inaccuracies of data by syncing data real-time across software programs and eliminates multiple entries of data. Automating data entry is also crucial because these tedious tasks are best left to software programs. Your employees can focus on more task-critical endeavors.
How to fix: Try to automate data entry and integrate different software programs in order to streamline data syncing. This way, all your data is updated simultaneously and you can also avoid human errors.
Data can quickly become corrupted if it is not cleansed and maintained regularly. The root cause of corruption is using outdated hardware that cannot store data for a long time.
Similarly, not cleansing data regularly and not updating it with the latest information can cause data rot. While updating hardware and storing information in a safe and secure cloud storage location are equally important, so is ensuring that all your data is cleansed and audited regularly.
How to fix: To avoid data corruption, make sure that you subscribe to a cloud-based data storage service, and seek data cleansing services.
As you can see, the accuracy of data can be diminished and eroded quite quickly for a variety of reasons. One of the main reasons why data is not reliable is due to human biases. In addition, data can be affected by bugs and malware or be tampered with by malicious entities.
Businesses often use data that is outdated and irrelevant. In such scenarios, it is important to update data and verify for inaccuracies and redundancies. Human errors can also result in unreliable data, for which software integrations and automation of data entry are perfect answers.
Finally, data veracity can diminish due to data corruption and data rot. To avoid this, businesses should regularly cleanse their data and ensure that it is all stored on encrypted cloud-based storage locations.
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