The term big data stands for huge amounts of data on the Internet, which grow by an estimated 70 terabytes every second. This has the advantage that high-performance computers recognize patterns in this large amount of data that a person does not see. From these findings, studies for research and science are won in no time. However, part of big data is also duplicated, incomplete or incorrect. For example, for products, resulting in significant problems for dealers and manufacturers. In order to compete in e-commerce with competitors, there is no way around to clean up incorrect data: This is done through the data matching software.
It is desirable to improve product data and delete duplicates. This is hard to do manually with a large volume of data. For example, if you listed 1,000 different pairs of sneakers in your assortment, you would have to make 49,500 comparisons. And in this example, the number of sneakers is a small amount. Especially in times when the customer makes quick purchase decisions with just a few clicks and the help of price comparison portals, retailers must prepare product data clearly and unambiguously. Matching is suitable for this.
Five Steps Of Data Matching
1.Data pre-processing: The first step is the process of data pre-processing, in which the data from both sources have the same layout. The goal of this step is that the properties used for the matching have the same content and structure.
- Indexing: The second step is to provide an index. This aims to reduce the quadratic complexity of the data by structuring the use of data.
- Record pair comparison: The third step is where the actual pair comparison happens.
- Classification: Classification is then the fourth step. Here the pairs are classified into one of the three possible groups: matches, non-matches or potential matches. All pairs are classified within potential matches, and then a manual administrative assessment is required.
- Evaluation: In the final step, the quality and completeness of the adjusted data are evaluated.
- The software extracts the files. As a customer, you first provide the matching software with a product list in which the software carries out the matching. It does not matter if it’s hundreds or tens of thousands of products. You then specify the sources. Using a query strategy, the software generates a list of all sources’ offerings – this step is called crawling. The system adapts to the varying URL and page structures of the sources.
- It standardizes the attribute values. Before it goes to the actual matching, the software does a preprocessing. With the help of these added attributes, products can be precisely identified.
- It compares the datasets with each other by means of matching. Using the attributes, the software compares the product data with each other. To make the result efficient, the software combines multiple attribute values. It uses a machine learning method for this. These training data provide feedback to the system and show them where allocation errors must occur and be corrected. The system remembers these corrections, learns with each pass and achieves a very high accuracy. Thus, the obtained data enjoy a high validity.
- In the last step, the software will provide you with the data. The software offers the possibility to use the results for further analyzes and for various reports.
The Benefits Of Data Deduplication
Data deduplication can be an effective tool for minimizing the cost of data usage for a server application by reducing the amount of disk space occupied by redundant data. Before you enable deduplication, it’s important that you understand the characteristics of your workload to make sure that your storage gives you the maximum performance. Deduplication systems work differently than classical compression methods, which use only a few comparison patterns, mostly at the so-called “block level”. The files are considered to be decomposed into a number of blocks of equal size. An important function of deduplication is fingerprinting. Here files are broken down into segments of different sizes. At the byte level, it is then analyzed which segments offer the highest repetition rate in order to offer the greatest possible data reduction by referencing to the source element. Data deduplication helps storage administrators reduce the cost associated with duplicated data. Large datasets often have a high degree of duplication, increasing the cost of storing data. Example: User file shares may contain many copies of the same or similar files. The savings in disk space that you can achieve with data deduplication are dependent on the dataset or workload on the volume. For high-duplication datasets, an optimization rate of up to 95% or a 20-fold reduction can be achieved.
General characteristics Of Data Deduplication:
- Inline Data Deduplication Compression Backup – Deduplication compression helps to keep the required disk space as low as possible, reducing the processing time compared to typical offline deduplication because no post-processing is required.
- Block-based imaging – provides rapid block-based imaging for both total and incremental backups and easily handles terabytes of data blocks during backup. And that’s not all: even restoring files or folders is quick and easy.
- Virtualization Tool – These tools enable data migration from legacy systems to new hardware and to physical or cloud-based environments.
- Stable and easy to use – Graphical user interface guides users through backup and recovery processes quickly and easily
With data matching software, retailers and manufacturers are watching the competition in e-commerce and are thus one step ahead of their competitors. The business intelligence software collects all relevant data from the Internet and presents it to the user. With these deep e-commerce insights, targeted sales can be increased, margins can be optimized and resources can be saved. It shows dealers and manufacturers exactly where they are in global competition. Price, product and provider data are recorded on the internet on a daily basis and evaluated in a tailor-made manner. This allows you to recognize opportunities and risks without losing time. You will receive your own database and tailor-made reports with all relevant information about the market.