What is data preparation?
Data preparation is the process of cleaning, reformatting and combining raw data to prepare it for analysis. This crucial step creates a foundation for accurate and meaningful insights from your data.
Why is data preparation important?
For example, insurance companies collect a large amount of data in the form of insurance policies and claims. The raw data can be in different formats and structures as it comes from different sources, including online forms, paper applications and telephone reports.
During data preparation, these different data sources are harmonized in order to obtain consistent information. This can include converting date formats, correcting typos and standardizing data types. In addition, missing information is added, for example if some damage reports contain incomplete information.
Without data preparation, this unstructured data could lead to misinterpretations and impair the quality of the damage analysis. With sensibly prepared data, for example, you can gain more precise insights. In the insurance industry, this would mean identifying trends in claims, detecting fraud, optimizing claims processing and improving rate design.
This shows how critical data preparation is to ensuring the quality and consistency of your data, extracting valuable information from it and making informed business decisions.
endless possibilities.
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Advantages of data processing
Data preparation offers decisive advantages for you:
- Produce high-quality data: Data preparation ensures the cleansing of data sets so that all data used in data analysis is of high quality.
- Increased productivity: Data preparation makes your employees more productive as they can access error-free data.
- Make better business decisions: Data preparation leads to high-quality data that can be processed and analyzed faster and more efficiently, which in turn leads to timely and efficient business decisions.
- Fix errors quickly: Data preparation helps you identify errors before data transformation. After the data has been removed from its original source, it is more difficult to understand and correct these errors.
- Cost savings: Data preparation reduces your data management and data analysis costs.
- Avoiding duplication of work: Data preparation prevents redundant work when adapting data for different applications.
Data processing steps
The specific steps of data preparation may vary depending on the industry, type of company and requirements, but the basic process remains essentially the same.
The key tasks involved in data preparation include
- Collecting data sets: gathering relevant data from various sources such as operational systems, data warehouses and other data sources.
- Data discovery and profiling: The first stage to improve data quality and prepare for subsequent data analysis. This involves identifying patterns, relationships and other data attributes, as well as uncovering inconsistencies, anomalies and missing values.
- Data cleansing and plausibility check: Correction to create complete and accurate data sets. This includes removing or repairing incorrect data, filling in missing values and harmonizing inconsistent entries.
- Data structuring for statistical data analysis: Organizing the data to make it accessible to statistical methods for data analysis. This can include converting data into tabular formats for access by analysis tools.
- Data transformation and enrichment for better data quality: Transforming data into consistent and usable formats. This may involve creating new fields or columns that aggregate values from existing data. If necessary, the data is enriched by adding information.
- Data validation and error correction: Checking data for consistency, completeness and accuracy using automated routines.
- Data publication for efficient data analysis: Storing and making the prepared data accessible for effective data-based methods of data evaluation within the company.
How does machine learning help with data preparation?
Data scientists often spend a lot of time collecting and preparing data instead of analyzing it. Efficient data preparation allows you to spend more time on the actual data analysis, which increases the business value.
A few examples:
Machine learning (ML) can help you intelligently automate data preparation. It recognizes patterns from repeated use and can start cleaning records as soon as they arrive. For example, the algorithm recognizes that you regularly need to bring phone numbers into a consistent format and make automatic adjustments.
Similarly, ML can detect that you want to continuously integrate product reviews from various online platforms into your records and automatically add them as new data enters the database.
Since these patterns are simple and repeatable, but labor-intensive due to the sheer volume of data, ML can save you significant time and effort in the data preparation process. In addition, it helps you reduce accuracy issues caused by human error, such as typos or items missed by human eyes.
The synergy of IDP and data preparation in data optimization
Intelligent document processing (IDP) and data preparation are two key terms in the field of data processing and data analysis.
IDP focuses on the automated processing and extraction of information from unstructured documents such as text, images and even handwriting. The main goal of IDP is to capture data from various documents, classify it and put it into a structured form. This enables companies to gain valuable insights from documents without having to go through them manually.
Data preparation, including data cleansing and plausibility checking, refers to the process of preparing raw data to make it usable for data analysis and modeling. Here, structured data from various sources is collected, cleansed, transformed and harmonized to ensure data quality. Data quality is crucial for successful data analysis and evaluation, as errors in the data sets can influence the statistical data analysis and the results.
The two concepts, IDP and data preparation, flow into each other, as IDP can be part of the data preparation process. When companies extract data from documents and integrate it into their data sets, this is an important step in data transformation and preparation. IDP facilitates the collection of data, which can then be fed into the data preparation process to be linked and cleansed with other data sources.
Conversely, data preparation can also support IDP by providing clean and structured data sources for intelligent document processing. This improves the accuracy and efficiency of IDP systems, which is very important for companies when analyzing and evaluating data. Applying statistical data analysis methods to high-quality data sets helps companies make informed decisions and minimize errors.
Overall, IDP and data preparation build on each other, as they are closely linked in the modern data world. If you want to learn more about IDP and the benefits of IDP, be sure to check out our article.
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