The Anatomy Of Data Quality Management
Many databases are not error-free, and some contain a surprisingly large number of errors .
A recent industry report, delivered by Richard Y. Wang, Director of the MIT Chief Data Officer and Information Quality (CDOIQ) Program, notes that more than 60 percent of the surveyed firms (500 medium-size corporations with annual sales of more than $20 million) have problems with data quality.
Data quality problems, however, go beyond accuracy to include other aspects such as completeness and accessibility. A big New York bank found that the data in its credit-risk management database were only 60 percent complete, necessitating double checking by anyone using it
Almost all of the businesses are exposed to numerous amount of critical data that grows and multiplies over the passage of time. Companies that are inundated with data on a regular basis, this data normally contains essential information of every type including budgetary figures, product details, customer details and a lot more. Data management indeed requires a lot of dedication and supervision and most of the businesses fail due to having lack of knowledge, Here, we throw some light on complicated data quality issues and share tips on how to excel in resolving them.
“TO IMPROVE DATA QUALITY, WE NEED TO UNDERSTAND WHAT DATA QUALITY MEANS TO DATA CONSUMERS (those who use data)”
Based on our best practice over 20 years of developing data quality solutions, can include the term of “ Data Fitness “
the concept of fitness for use from the quality literature, and our experiences with data consumers, we recommend conceptual framework for data quality that includes the following aspects:
• The data must be accessible to the data consumer. For example, the consumer knows how to retrieve the data.
• The consumer must be able to interpret the data. For example, the data are not represented in a foreign language.
• The data must be relevant to the consumer. For example, data are relevant and timely for use by the data consumer in the decision-making process.
• The consumer must find the data accurate. For example, the data are correct, objective and come from reputable sources.
Now, Why poor data quality is a problem ?
first of all, let`s spot the 5 major characteristics of poor data quality, on a different term, how can you say that this or that piece of data is a poor one!
1- Incomplete Data
2- Ambiguous Data
3- Duplicated Data
4- Outdated Data
5- Late Data Entry/Update
Ok, the experience is totally different when using our DNX ( Datability Ninety X ) as we define each and every item of the data processed to ensure they are matching the ultimate qualities with detailed attributes score
Where making use of poor quality and badly managed data takes place, it leaves a huge impact towards management decisions. Believing that the big data changes quiet dramatically, a lot is sometimes gathered in an unstructured form such as in distinctive incompatible formats, through social media updates and a lot more. When the data recorded on a regular basis is incomplete, incorrect or contradictory despite the fact what industry a firm operates in, reducing the usefulness of data leaves a major impact. With regards to the banking sector, maintenance of poor quality data may let the customers avail poor experience. This is why it is essential to have your data receive perfect data quality platform made a complete ease of plug and play experience for banking.
“Where the use of poor quality data takes place, it is said that businesses suffer up to 25% during the decision-making process”
Again it`s seamless experience if you are using DNX ( Datability Ninety X ) as it provides the organization with both diagrammatic and numeric charts that consists of lots of indicators helping you to figure it all out such as ( data issue type, data quality severity level, data quality score summary and data quality dimensions …. Etc )
Finally Gartner itself named the main features your organization should look for when they are seeking to acquire Data Quality management platform and proudly we can address DNX ( Datability Ninety X ) as a platform that full-marked by Gartner, and here`s Gartner full list :
Parsing and standardization to break the data into components and bring them to a unified format.
Cleaning tool to remove incorrect or duplicated data entries or modify the values to meet certain rules and standards.
Matching tool to integrate or merge closely related data records.
Profiling tool to gather stats about data and later use it for data quality assessment.
Monitoring tool to control the status-quo of data quality.
Enrichment tool to bring in external data and integrate it into the existing data.
How Datability Ninety X from Knowledgenet works?
Having in mind data quality platform, DNX is referred to as one of the useful products or an end to end platform which adds more of the flexibility to be adapted by any business needs and works in any environment. It comprises of a software engine which combines with the predefined quality data rule book, integrated with the processes for top-quality management along with monitoring dashboard and alerting service which aims to maintain the quality of data, making availability via both the analytical and operational system. This data quality management system is further referred as an excellent one for banks as it has already made rule book and scenarios dictionary. Hence, referred as a plug and play platform. The software requires minimum development which is required to run and then be maintained