Wednesday, September 9, 2020

Data Quality Attributes and Assessment

Data Quality doesn't mean you pursue the perfect data but means clean, organized, actionable data from which to extract relevant, good enough data being transformed into useful information, business insight, and ultimate wisdom.

The business operates in the real world, and the real world is muddy and chaotic, with the exponential growth of information and the increasing pace of change. In fact, nowadays, information is one of the most invaluable assets of the company. You will not be able to make effective decisions or understand the performance of your company or measure it without good quality data or information. 

In specific, by “quality data,” it means clean, organized, or actionable data from which to extract relevant information and insight. Quality data is like the Holy Grail, businesses all want to achieve it; but not sure if it’s very doable. Data Quality management ensures the right people getting quality information with the following attributes to solve large or small problems and build a real-time information-savvy organization.

Accuracy: Data does not live alone but permeates everywhere in the businesses. Data is scattered and needs to be collected, processed, and refined into business insight. Data cleansing, transformation, and sorting are vital in the data world and the Data Management lifecycle is the overall business process of aligning the use of information through information accuracy and assurance management. It helps to put things in multiple perspectives for business to read between the lines with accuracy and clarity of information that is needed for making effective decisions and solving problems.

Keep in mind though, you might have very clean data on your customer profiles, but necessarily that data is incomplete. No matter how "clean" your data is, it suffers from the limitations of chaos theory on its accuracy and applicability to the "real world." The accuracy and compromise will continue to coexist across the span of information management.

Consistency: Consistency remains a favorite mantra for data management. Often, inconsistent or broken business processes result in significant information inconsistencies which causes decision ineffectiveness. Streamlining and standardization of information management processes is an essential prerequisite to providing effective information integration solutions and achieving information consistency and synchronization.

Information synchronization is the process of establishing consistency among data from a source to the target storage and vice versa, making continuous harmonization and refinement of the information to improve business intelligence. Information synchronization occurs on multiple levels of the organization, it presupposes the ability of each 'link' to improve business decision cohesiveness and presents the recognizable digital rhythm to keep the business not only spinning around but also moving forward at the right paces.

Reliability: Reliability is the ability of the business to consistently deliver quality data to achieve business value. Data management should clarify some critical concerns by making a set of inquiries such as: what data do you have, in what format and the location & method it is held, information accessibility or reliability, as well as information exploitation to ensure that the information refined can collaboratively enable and fully support the business objectives. It’s about putting profound knowledge, processes, and tools actually used in data management and deliver reliable information to improve organizational manageability.

To improve data reliability and quality, work hard on consolidating, modernizing, integrating, optimizing data, and refine it into business insight. Information assets and resources need to be centralized, reallocated, updated, or replaced if needed to improve business effectiveness and efficiency. Information management applies information solutions to meet the business needs of the organization and act as the bridge between the bits and bytes of information and the mission and objectives of the business.  

Contextualization: The value of information is not isolated. Data can be accurate, consistent, timely, but data can also be shared among many different business groups. The quality data reveals deep and essential truths about not only the business domain it covers but also about the systems that capture it. Data can be transformed, aggregated, derived for various business needs, each with possibly their own views on what the expected definition and quality of the data should be. The business context is an important attribute of data quality.

In a business scope, there are some of the important bits and bytes of information needed to forecast the business growth opportunities or prevent risks. Information quality doesn’t end with managing the incorrect entry of information, but the logic of data has to be taken into account and figuring out an enterprise-level information management solution. Information contextualization helps business leaders or professionals understand information from different perspectives, also share information across functions, and improve decision maturity.

Timely: In the dynamic digital era, timing is always important to leverage the right information for making effective decisions by the right people. The power of information today is to empower the business with real-time insight across the organization in ways never possible before, enable organizations to see the future clearly; enhance business competency, re-imagine growth, delight customers, harness innovation, and further elevate the digital business maturity.

The real-time information management involves the use of technologies and processes of capturing, developing, sharing, and effectively using organizational information timely, with the aim of optimizing the value that is generated for improving business decision effectiveness. It is a multidisciplinary approach to achieving organizational objectives by making the best use of its invaluable asset -information in a timely manner and building a real-time digital organization.

Poor information management implies not understanding what raw data they have to play with; not understanding the importance of information/knowledge; poor management or poor measurement, not applying worthwhile evaluation to it to reveal the inherent value. Highly effective data management walks you through the various dimensions of data quality such as accuracy, consistency, timely, contextuality, etc. Data Quality doesn't mean you pursue the perfect data but means clean, organized, actionable data from which to extract relevant, good enough data being transformed into useful information, business insight, and ultimate wisdom.


Your article on "Data Quality Attributes and Assessment" is a comprehensive and enlightening read. As a firm specializing in data management, we deeply value the importance of data quality attributes. We believe that data accuracy, completeness, and consistency are the cornerstones of robust data governance and informed decision-making.

Your insights on assessing data quality resonated with our own approach to delivering top-notch data quality solutions to our clients. By focusing on data profiling, data cleansing, and ongoing monitoring, we ensure that our clients' data meets the highest quality standards.

Thank you for sharing such valuable expertise on this critical aspect of data management. We look forward to further discussions and knowledge-sharing within the data quality community.

Best regards,

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