Information Quality
Information Management has to break down silos, to keep information flowing smoothly.
Information is the soft asset of the business. Flawless, actionable, and autonomous data refers to high-quality information that can be used to drive automated decision-making and actions. Information does not live alone but permeates everywhere in businesses. Several factors contribute to making data "flawless" and suitable for autonomous systems:
Data Collection and Processing: For data to be analyzed, it must first be collected, stored, and processed into a usable format, and cleaned to minimize errors and inconsistencies. The extraction step occurs when you identify and copy or export the desired data from its source, such as by running a database query to retrieve the desired records.
The transformation step is the process of cleaning the data so that it fits the analytical needs for the data and the schema of the data warehouse. Finally, the clean data are loaded into the data warehouse, where they might join vast amounts of historical data and data from other sources.
Relevance and Reliability: Useful information should be relevant and reliable. Relevant information helps improve predictions of future events, confirms the outcome of a previous prediction, and should be available before a decision is made. Reliable information is verifiable, representationally faithful, and neutral.
Ethical Considerations: Information Management systems developers have the ethical responsibility to prevent unauthorized access, use, disclosure, disruption, modification, or destruction of data. The AI model collects and processes only the minimum data that is necessary. Your data is used transparently and only with your consent. Data storage and transmission are encrypted to protect against unauthorized access.
Access controls and authentication mechanisms strictly control data access. Users are granted as much control as possible over their data.
Autonomous Systems: Machine learning and artificial intelligence are foundational elements of automated vehicle systems. Through machine learning, vehicles are trained to learn from the complex data that they receive to improve the algorithms that they operate under and to expand their ability to navigate the road.
Artificial intelligence enables vehicles’ systems to make decisions about how to operate without needing specific instructions for each potential situation encountered while driving.
Information Management has to break down silos, to keep information flowing smoothly, and apply an integrated and holistic information life cycle management solution to conquer challenges and generate business value rather than try to “control” it.
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