data cleansing

The approach to de-duplicating and making sense of such information is one example of applying the data cleansing pattern.

As described earlier, the data cleansing pattern can turn inconsistent (and hence, useless) data into a valuable strategic asset.

WebSphere QualityStage is one of the core components of this portfolio that provides data cleansing technology.

A computer algorithm for data cleansing, no matter how cleverly constructed, can only address a very small subset of data problems.

First, data cleansing and repository building are almost always carried out on a project by project basis.

As you might imagine, design is the most critical and complex phase in the data cleansing process.

The specification of data cleansing rules needs to be based on guidelines that are approved by business experts and that are largely stable.

After briefly describing the value of this approach, you'll learn the context in which the data cleansing pattern should be applied.

Many implementations of the data cleansing pattern provide sophisticated tools to develop, test, and deploy the cleansing rules.

Therefore, the data cleansing server needs to be able to scale in order to process large data volumes.

The potential revenue benefits and customer experience are realized by deploying data cleansing as a service.

IBM Information Server is a high-volume data integration platform for data cleansing, transformation, and relocation.

The SOA context for data cleansing allows for standardization and matching of individual request strings.

In many cases, the data cleansing pattern is applied together with the data consolidation pattern.

An important aspect of the data cleansing pattern is its focus on reusability at an enterprise level.

It is fairly common to apply the data cleansing pattern against sets of bulk data as well as individual records.

The data cleansing service receives data with an undetermined level of data quality as input.

More traditionally, data cleansing has been also applied in many data warehousing projects.

Figure 1 illustrates the high-level architecture of applying the data cleansing pattern in the traditional context.

The data cleansing pattern specifies a recommended practice for how to improve the data quality of persistent data either at entry or later.

The traditional context of the data cleansing pattern is the database layer, which is where it is most often applied.

Product implementations of the data cleansing pattern vary in the range of formats they can support for input data.

There are two primary skill sets and experiences required for data cleansing.

The development costs depend largely on the complexity of the data cleansing task.

Data cleansing using a rule-based system for a new data source requires manual intervention for adapting rules from one dataset to another.

Sample selection and data cleansing for quantitative analysis of patents

Web Data Cleansing for Effective Information Retrieval

A Human-Centered Process Model for Data Cleansing under Data Warehousing

A Combined Data Cleansing Strategy Under ODS Environment

The Discussion about Data Cleansing of Data Warehouse

Improved Algorithms for Data Cleansing Based on Clustering Analysis

Focus areas to apply the data cleansing pattern

Rules Engine Based Data Cleansing

An incremental algorithms of data cleansing based on clustering tree