Data Quality in Data Mining Through Data Preprocessing
Data Pre-processing is a preliminary step during data mining. It is any type of processing performed on raw data to transform data into formats that are easier to use. In this article, DataEntryOutsourced provides an overview of how data preprocessing contributes to data quality and data cleansing.
Why Is Data Preprocessing Important?
In the real world, data is frequently unclean – missing key values, containing inconsistencies or displaying “noise” (containing errors and outliers). Without data preprocessing, these data mistakes will survive and detract from the quality of data mining.
Tasks Involved in Data Preprocessing
The failure to adequately clean data is the number one problem in data warehousing. Some of the data preprocessing tasks are the following:
- Fill in missing values
- Identify and remove “noisy data”
- Resolve redundancies
- Correct inconsistencies
Data is available in several formats such as static, categorical, numerical and dynamic forms – examples include Meta data, web data, text, video, audio and images. These widely-varying data forms contribute to the regular data preprocessing challenges.
Handling Missing Data
In addition to handling missing data, it is essential to identify causes of missing data so that avoidable data problems do not keep recurring – for example, equipment malfunctions and misunderstandings. Solutions for missing data include manually filling in missing values and filling in automatically with “unknown.”
Tackling Data Duplication
Data duplication can be a major problem because it often loses business, wastes time and is difficult to deal with. A common example of typical data duplication problems includes multiple sales calls to the same contact. Potential solutions involve software updates, third-party vendors and changing how your business tracks customer relationship management. Without a specific plan and the right software, it is difficult to eliminate data duplication.
Another common source of data duplication is when a company has an excessive number of databases. As part of your data preprocessing, you should regularly review opportunities for reducing and eliminating multiple databases. Without doing this, data duplication is likely to be a recurring problem that you have to deal with over and over again.
Achieving Data Quality
Most companies want to make better use of their extensive data but are unsure about where to start – data cleansing is a prudent first step along the path to improved data quality.
Data quality can be an elusive goal without an effective methodology for accelerating data cleansing:
- Acknowledging the problem and identifying root causes
- Creating a data quality strategy and vision
- Prioritizing data importance
- Performing data assessments
- Estimating the ROI for improving data quality vs the cost of doing nothing
- Establishing accountability for data quality
- Hiring an experienced outsourcing partner such as DataEntryOutsourced to help
One of the most compelling reasons for relying on data management experts like DataEntryOutsourced is to avoid the need to “reinvent the wheel” – DEO is already familiar with how companies of all sizes can cost-effectively tackle common challenges associated with data mining and data cleansing.
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