Data preprocessing is a preliminary step throughout the process of data processing. It's about any type of processing that is done with the raw data to transform it into data that has more user-friendly formatsr.
In the real world, data is often not clean, missing key values, contain inconsistencies and, often, show noise, contain errors and outliers. Without data preprocessing, these data errors would survive and decrease the quality of the data. data processing.
Lack of proper data cleansing is the number one problem in data storage.. Some of the data preprocessing tasks are as follows:
- Fill in missing values
- Identify and erase data that may be considered noise.
Data is available in various formats, as static shapes, categorical, numerical and dynamic. Some examples include metadata, web data, text, video, audio and images. These various data alternatives help data processing to continually face new challenges..
Treatment of missing data
At the same time handling missing data, It is essential to identify the causes of missing data to prevent those avoidable data problems from reoccurring. Solutions for missing data include manually filling in missing values and auto-filling the word “unknown”.
How to address data duplication
Data duplication can be a major hurdle in data mining.since it often causes loss of business, wasted time and difficulty treating. A common example of a typical data duplication hurdle includes multiple sales calls to the same contact. Possible solutions involve software updates or changing the way your company handles customer relationship management.. Without a specific plan and the right software, hard to clear duplicate data.
Another common source of data duplication is when a company has a excessive number of databases. As part of the pre-processing of your data, should periodically review the possibilities to reduce and delete some of those databases. If not done, data duplication is likely to be a recurring hurdle that you will have to deal with over and over again.
Achieve data quality in data mining
Most companies want to make better use of their extensive data, but they're not sure where to start. Data cleansing is a smart first step a long way to boost data quality. Data quality can be a difficult goal to achieve without a effective methodology that accelerates data cleansing:
- Recognize the problem and identify the root causes.
- Create a strategy and vision of data quality.
- Prioritize the relevance of the data.
- Realization of evaluations of data.
- ROI estimate to boost data quality versus the cost of doing nothing.
- Determine the rresponsibility for data quality.
- Hiring an external consultant experienced who can help us.
One of the most compelling reasons to trust an outside consultancy is the need to avoid reinventing the wheel. An experienced consulting firm is already familiar with how companies of all sizes can profitably address common challenges associated with data mining and data cleansing...