One of the major issues faced by enterprises today is data quality. Even after having huge amounts of data and advanced techniques to analyze this data, business data demands a lot of cleaning and preparation before it can be used. The business intelligence and analytics systems necessarily require cleansed data for providing actionable insights.
Moreover, in the world of automation, analyzing complex data without the knowledge of bad and good data again raises the concern of data quality. It simply means that we can’t utilize data as effectively as we can if we are not able to solve the glitch posed by low-quality data.
How Enterprises Are Affected By This?
The advancement in technology leading to petabytes of business data, which is also increasing every day, has raised the issue of cleaning this data. Today, businesses are at the ease of collecting more data than ever. But, the manual efforts to clean and utilize this data for actionable business insights is just too high.
All this unrefined data simply leads to money wastage in terms of IT investments, lost work hours, and poor business decisions. Although automated systems have emerged for data analytics, the issue of data quality has not reduced. It still remains. The end customers will not be able to utilize insights generated through incomplete and unrefined data.
How Can Machine Learning Help?
For machine learning and business intelligence to give us accurate results, the concerning data should be complete and precise. We all know this by now.
However, a recent case study experiment shows that the use of ML algorithms in cleaning data can improve insights. The study utilized ML algorithms through an analytics solution to improve the data quality of an inventory. This model used data-centric, quality, and innovative rules to clean out bad data. As a result, the model rectified approximately 30% of records, which is a lot better doing it all manually.
Consequences Of Bad Data
Bad data has a huge impact on enterprises as it can directly impact business decisions. If your data is bad and unrefined, you will never receive accurate insights and your users will not like to use these insights. Hence, lost of productivity, bad decisions, and extra overheads. Your enterprise would be spending a lot of money in just correcting the mistakes or glitches caused due to bad data.
Data quality is as essential as data analytics and insights. All the data that you are collecting on a daily basis would be for nothing if it can’t be utilized for the desired outcome.
Conclusion
Many data scientists are now researching how machine learning algorithms can be used to filter out bad data to improve the efficiency of analytics. This would reduce human efforts, improve the effectiveness of the analytics, and lead to enhanced business decisions.
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