Top Key Characteristics and Approaches of Data Quality

Henry is an Investment Banker in Canada for a decade. All his present & future decisions on the investments are now based on the data which might be collected from many sources, following many processes and procedures. As an Investment Banker, he might be using past, present data to ascertain his future investments. During Covid-19, it was a challenge for Henry to assess the impact and design a strategic response accordingly as he used to receive datasets from various sources globally.

He wanted to eliminate duplicate, multiformat, incomplete, non-standard data and in this process, he was very much concerned about the quality of the data. His main mission was to bring out the best from the available data. To achieve this, he has opted to initiate the process by understanding the characteristics and approaches of Data Quality. This enabled him to achieve accurate, reliable, and trustworthy data that adds more value in making sound decisions.

Let us now understand the important Characteristics of Data Quality

  • Accuracy – Accuracy is a vital characteristic of data quality. It ensures whether the data is free of errors and mistakes at the first instance.
  • Completeness – Completeness in the context of Data Quality refers to how comprehensive the available information is. This feature helps to check whether the present data is complete to run the applications and use it.
  • Reliability – The sources of the collected data should be reliable without unwarranted variance and contradiction.
  • Timeliness – Data collected too late or too soon could drive inaccurate decisions and entirely misrepresent a situation. Hence the data should be collected at the right moment in time.
  • Relevance – The data should relate to the applications. This characteristic helps you to know why you are collecting the information and should consider whether to collect or not or whether the data collected is relevant
  • Conformity – Focus on types, codes, keys, domain ranges stored in the required formats
  • Consistency – There must be a stable mechanism which collects and stores the data without conflicting facts and contradictions.
  • Continuity – Historical and current data in unbroken and non-overlapping
  • Uniqueness – Data should be appropriate with no duplicate data elements
  • Redundancy – Every business object has a unique identifier and data is only stored once
  • Duplication – Each entity reflect for a single master record
  • Reference Integrity – All foreign keys can be used to access related information.

Important Approaches of Successful Data Quality.

  • Define – Based on the needs, goals, and requirements of the enterprise business terms & sources should be not only created but also defined. The business requirements must comply with the viable sources accordingly.
  • Discover – At this stage of the approach, it is vital to discover what data contains. It helps the enterprise to initiate the DQM process and sets the standards regarding the improvement of their data quality.
  • Design – At this approach stage, based on the business objectives and goals, Quality rules should be created and defined with which the respective data should comply to be considered viable.
  • Execute – Embed the business rules and Data quality services monitoring into your data integrity processes and operational systems
  • Evaluate – You can monitor and measure actual Vs expected track trends and allocate the tasks accordingly. Data Quality rules collaborate with analytics and these rules enable reporting analytics and predicting trends
In this Data-Driven era, every enterprise strives to improve its profitability, achieve competitive advantage and desired insights, by getting accurate, consistent, and complete quality data. An Ideal Data Stewardship Process
  • Improves Data Quality, accessibility, and reusability
  • Enables concise, clear data policies and processes.
  • It enables to use of the data to its fullest capacity
Know more about Data Stewardship and its benefits by visiting this link. Amruta DIP provides data quality automation with AI- driven data quality rules for effective and Data Quality implementation for enterprises looking for data quality improvements.  Furthermore, Amurta’s DIP Data Quality Metrics module enables you to achieve high data quality.
You can request a demo to explore Amurta’s Data Insights Platform, the Industry Prime Data Governance Tool by just clicking this link. To know further details and for any queries please feel free to contact +1 888 840 0098 and you can email us at, we will be happy to assist you.

Top Key Characteristics of Data Quality

Data Quality

Successful Data Quality