Data quality management tools (DQMs) are growing significantly as data volume increases and the dependence of automated tools depends on the high degree of accuracy of the data to avoid delays and delays in processes. Because customers and other trading partners are growing in terms of automation and speed, they are increasingly dependent on high quality data to be able to implement such processes, with a direct impact on both revenue and organizational costs.
What are the requirements for evaluation criteria for a data quality tool, and what are the open holes that actually implement these types of tools still result in data cleaning and quality projects failures. From a technical point of view to the DQM application:
(1) Extracts, Analysis, and Data Connection
The first step in such an application is to connect to the data or to receive data loaded into the application. There are several ways to load data into the application or the ability to connect and view data. This includes the ability to analyze or share data fields.
(2) Data Profiling
As soon as the application accesses or has the data, the first step of the DQM process is to execute a certain level of data profiling, which includes running data statistics (min / max, average, number of missing attributes, including This should include checking the accuracy of certain columns, such as e-mail addresses, phone numbers, etc., and the availability of spelling accuracy for reference libraries such as postal codes
3 Cleaning and standardization
data cleanup identifies both seed automated cleaning functions such as date standardization, site termination, copy functions (such as M instead of F 1 and M 2), calculation of values, and identification of incorrect location names. outside standardization of standard rules sets and data that help to identify missing or incorrect information. This also includes the ability to manually modify the information.
Deduping records using a variety or combination of fields and algorithms to identify, merge and clean records. Parallel records may include poor data entry procedures, application aggregation, corporate mergers, or many other reasons. You should make sure that not only addresses are deducted, but all data can be estimated for copying. Once a suspicious parallel record has been identified, the process of actually increasing the record, which may include automated rules to determine which attributes should be treated as a priority, and / or a manual procedure to clean up duplication should be clarified.
(5) Load and Export
The ability of the application to export data in different formats can be linked to databases or data storage to allow for complete data entry.
New emerging skills in DQM applications.
DQM devices are generally designed and built by engineers. The successful implementation of the data quality project is not only a technical aspect of data analysis and clarification, but also a number of other aspects. Some of the new DQL applications in the Application Toolkit contain areas that are more relevant to project and process management, either on a one-off or a continuous basis. These new capabilities can be as important to the successful completion of data cleansing or quality projects:
(1) Managing Stakeholders and Data Owners Automated Tasks
These types of processes or projects are usually large numbers of internal and external stakeholders . Managing it through tables and emails can be a daunting and complex matter. Applications that can automate parts of the process can bring significant value and predictability to the success of the project. This is as simple as checking compliance with standards and putting exceptions / tasks on specific users or data owners, if they are violated, or coordinating large-scale validation directly with external parties, such as updated tax exemption certificates or addresses directly.
(2) Data flexibility – ability to handle any data
Some DQM applications are highly specialized to handle only address control or part / SKU cleaning. The DQM application must be able to handle any type of MDM (master data) or transaction data with flexible rule definitions.
(3) Big Data Cleaning
Big Data files are created in a structured, semi-structured and completely unstructured format. Standardizing and automating the cleaning of these data can be continuous. The process required to clean up large amounts of data requires automated automation rules that can be used in unstructured formats.
4 Data Management and Compliance Control
Data management and compliance monitoring are key to maintaining data accuracy and purity. Many applications are unable to implement business rules that we want from a structural point of view. Some DQM applications can be used to track data management processes to request new attributes or values and to monitor exceptions to achieve a higher level of information.
(5) Project Status Report
A typical data quality management or conversion project involves a series of steps and expressions that involve a large number of stakeholders. Proper allocation of responsibility, purification and interdependence of tasks is a complex process, and some applications are starting to use this type of collaboration.