Focusing on the information aspect of the role in the context of the business is part of the digital transformation.
Information flow streamlines idea flow, connects dots to create new ideas, visualizes the strategic perspective of information/knowledge management and focuses on building information-support solutions to solve complex problems. The quality of information directly impacts the quality of business. Here are a few parameters of assessing information quality.
Objectivity: Objectivity is the science we set standard to evaluate, leverage quality information to analyze, and examine the facts to overcome bias. There are objective data quality dimensions such as: Integrity, Accuracy, Validity, Completeness, Consistency, Existence. There are Subjective Data Quality Dimensions such as: Understandability, objectivity, timeliness, relevance, interpretability, trust
There’s no “perfect” data, the accuracy and compromise will continue to coexist across the span of information management. “Good enough” data can be more useful than perfect data, as long as the information is good enough for the recipient to make sound business decisions or solve specific business problems. It takes longer to make the data more accurate, and such time delay may actually diminishes its value rather than improving it. Information quality efforts need to be defined more as profiling and standards versus cleansing. This is better aligned to how big data is managed and processed.
Relevance: The analysts optimize decision making by obtaining useful data which are reliable, can be trusted, and relevant, it applies to the situation at hand. By “quality data,” it means clean, organized, actionable data from which to extract relevant information and insight. High-quality information has to be relevant, standardized and consistent. The better the classification and structure of your data, the better your search and analytical capabilities will be.
Incoherent or broken business processes result in significant information inconsistencies or irrelevance which causes decision ineffectiveness. If information is irrelevant - unavailable, inaccurate, lost, stolen, or compromised, it will cause poor judgment, hinder the achievement of business goals and even mislead the business in the wrong direction.
Interpretability: We all have different perceptions, There are varying tools that can be used to interpret data. One interpretation of data may differ greatly from another interpretation of that same data. It is always beneficial to communication, interpretation and accuracy of overall results when diverse teams interpret and define data sources and outcomes constantly..
Good information interpreters can leverage contextual intelligence and multiple perspectives with respect to make a positive influence on pulling progressive conversation ahead, focusing on commercial business outcomes, not just technical throughput. The more engaged the business professionals in accessing data and understanding where it is extracted and how it is compiled, the better they can interpret and understand the results.
Lack of objectivity or misunderstanding is the big cause of many human problems. Focusing on the information aspect of the role in the context of the business is part of the digital transformation. Keeping track of performance at the strategic level is useful only if its information content is evaluated for objectivity, relevance, and interpretability; digital contextualization helps to improve business agility and effectiveness, adaptability, innovativeness, intelligence, and customer-centricity.
Objectivity: Objectivity is the science we set standard to evaluate, leverage quality information to analyze, and examine the facts to overcome bias. There are objective data quality dimensions such as: Integrity, Accuracy, Validity, Completeness, Consistency, Existence. There are Subjective Data Quality Dimensions such as: Understandability, objectivity, timeliness, relevance, interpretability, trust
There’s no “perfect” data, the accuracy and compromise will continue to coexist across the span of information management. “Good enough” data can be more useful than perfect data, as long as the information is good enough for the recipient to make sound business decisions or solve specific business problems. It takes longer to make the data more accurate, and such time delay may actually diminishes its value rather than improving it. Information quality efforts need to be defined more as profiling and standards versus cleansing. This is better aligned to how big data is managed and processed.
Relevance: The analysts optimize decision making by obtaining useful data which are reliable, can be trusted, and relevant, it applies to the situation at hand. By “quality data,” it means clean, organized, actionable data from which to extract relevant information and insight. High-quality information has to be relevant, standardized and consistent. The better the classification and structure of your data, the better your search and analytical capabilities will be.
Incoherent or broken business processes result in significant information inconsistencies or irrelevance which causes decision ineffectiveness. If information is irrelevant - unavailable, inaccurate, lost, stolen, or compromised, it will cause poor judgment, hinder the achievement of business goals and even mislead the business in the wrong direction.
Good information interpreters can leverage contextual intelligence and multiple perspectives with respect to make a positive influence on pulling progressive conversation ahead, focusing on commercial business outcomes, not just technical throughput. The more engaged the business professionals in accessing data and understanding where it is extracted and how it is compiled, the better they can interpret and understand the results.
Lack of objectivity or misunderstanding is the big cause of many human problems. Focusing on the information aspect of the role in the context of the business is part of the digital transformation. Keeping track of performance at the strategic level is useful only if its information content is evaluated for objectivity, relevance, and interpretability; digital contextualization helps to improve business agility and effectiveness, adaptability, innovativeness, intelligence, and customer-centricity.
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