Ensuring Data Mining Accuracy Leads to Precision in Business Data Records
In the “data first” age, businesses feel the need to hire more and more “data scientists” to handle data accurately. With big data being everywhere, there is a more significant concern about data and the techniques to control and monetize it. But another, most considerable concern here in the foreground is the data quality. Poor or redundant data is as useful as “scrap.” So, businesses must indulge some considerable time in the business data mining and its accuracy to convert their strategies to reality.
What role does data mining play?
Today data mining and research are incumbent to every business, starting from discovering valid information and capturing data from the data lakes to modeling and prediction. It summarizes the data in a way that is understandable to everyone and helps in drawing an inference around it based on the patterns being observed.
Data mining techniques are created using machine learning algorithms tailored to the particular goals and objectives of businesses. The algorithms also have the power to measure the accuracy of data mining and data enrichment.
Measuring data accuracy
Measuring data mining accuracy is significant after data collection. Perhaps this is the reason why mining data cannot reap the benefits of “addressing quality issues” at the very sources. Data mining mainly focusses on identifying the problems in data quality, correcting it, which is commonly called data cleansing, and deploying algorithms in detecting and enriching poor data.
Errors in data is a common facet of data collection. It can be due to various reasons like human errors, poor data collection, measurement error, and so on. There can be missing values in the data, redundant and duplicate data.
Let us discuss the different types of errors in data:
- Data Collection Error – It includes redundant or unnecessary data, incorrect data attributes, or objects.
- Noise – It refers to the distorted values or the addition of unnecessary data. It’s the random element of any measurement errors.
Here is a time series representing before and after disruption with random noise:
The noise removal is a complicated task, so data mining involves the usage of a robust algorithm to produce remarkable results.
- Outliers – It is either the data objects whose characteristics are different from the other data objects from the data set; or values that are uncommon from the other data attributes.
- Missing Data – It is whenthere are missing values in the data attributes which could be due to some information was not collected, or some of the attributes do not apply to all the objects in the dataset.
- Inconsistent Data Attributes – A data set is contradictory when the attributes do not match with each other. For example, if the fields contain address with the name of the city and the zip code, and the zip code does not match with the city, it’s inconsistency in the data. It might be because of wrong data entry, transposed digits, etc. The correction requires additional data.
- Duplicate Data – The data set may have duplicate data objects that must be detected and eliminated. It needs to be identified, if two different objects are referring to one attribute, the values of the corresponding object may be different from each other, which needs to be fixed.
We had been talking about the quality of data all this while, which is inseparable from the discussion of data accuracy. The data quality and the accuracy of the resulting data can be measured statistically through the following ways:
- Anomaly Detection – When there is a large data set, it is not possible to determine how the data look like. Anomaly detection helps in recognizing the remarkable difference in the pattern. For example – the IRS with a typical tax return model can use anomaly detection to figure out the specific returns that are different from the review and the audit.
- Association Learning – Often used by eCommerce companies to target coupons, this type of data mining helps in building recommendation systems and personalization. For example, it identifies customers who bought a recipe book and also serving bowls, leading to targeting for deals and coupons accordingly.
- Cluster Detection – With the cluster detection algorithm in the data mining model can itself determine similar data in the group, categories, sub-categories in a dataset accurately and significantly segregate from each other. It solves the issue of the unlabeled data. For instance, the machine learning algorithm can detect the purchasing habits of all hobbyists like a gardener, painter, fisherman, etc., and segment under the correct categories.
- Classification – If there is a pre-existing structure, classification can be used to determine the accurate grouping of the data under the pre-determined categories. The algorithm is powerful enough to detect the significant difference, in the case of the groups.
Spam filters are a classic example of identifying large sets of spam emails with filters to detect the word usage and differentiating between the spam and the legitimate emails while classifying the messages based on the classification rules with precision.
- Regression: In the regression, the sum of squared errors (SSE), root mean square error (RMSE), and mean average error (MAE) are used to measure the accuracy in data mining. The methods are appropriate for the output of continuous value.
When machine learning algorithms potentially detect the data quality, the next step is data analysis and interpretation. But does it end here? The answer is it should not. A data enrichment method is the next big thing to think about so that it keeps the data updated from time to time. Another essential facet is data anonymization which refers to safeguarding private and sensitive data by encrypting system that connects the stored data with an individual.
Data anonymization helps in detecting security threats and sharing the data externally while making it useful and efficient for the users. It can be used by companies to adhere to strict data privacy laws, protecting personally identifiable information (PII) through masking the private data attributes. Thus, data mining has immense potential to lead to accurate data analysis, provided the mining is reliable in turn. Machine learning algorithms are savior. It can classify the data correctly and determine the accuracy of a big data set, which is the fundamental step in data analysis. However, businesses must consider hiring companies providing data mining services to get an accurate result from the experts.
– BackOffice Pro