"The Trends & Challenges in Data Driven Decision Making"

The use of geographical or location-based information in data analysis to uncover patterns, trends, and insights. Spatial analytics adds context to data, particularly useful in understanding relationships and making location specific decisions.

 

• Privacy and security

• Spatial analytics

• Data democratization

• Data quality

• Data integrity

The accuracy, consistency, and reliability of data throughout its lifecycle. It ensures that data remains unchanged, complete, and trustworthy, contributing to the overall quality and reliability of information used in decision-making.

 

• Privacy and security

• Spatial analytics

• Data democratization

• Data quality

• Data integrity

A systematic approach to managing and controlling the processes, policies, and standards for collecting, storing, processing, and ensuring the quality and security of data. It involves establishing frameworks that influence trust and confidence in data, including aspects like data lineage, stewardship, and access.

 

• Data Governance

• Spatial analytics

• Data democratization

• Data quality

• Data integrity

The process of providing access to data and insights across an organization, making data-driven decision-making accessible to a broader range of users, including those without technical expertise.

 

• Data Governance

• Spatial analytics

• Data democratization

• Data quality

• Data integrity

Organizations are ________ to tap into the immense value of available data but struggle with challenges in making data-driven decisions using data governance.

 

• analytics

• quality

• scrambling

• leading

• access

The majority of organizations recognize the need to facilitate a data-driven approach, especially for strategic decisions based on trusted data and ________.

 

• analytics

• quality

• scrambling

• leading

• access

According to the LeBow study, the top value-generating benefit of mature data governance programs is the ability to strengthen overall data ________.

• analytics

• quality

• scrambling

• leading

• access

The goal of data governance is to control various elements that influence trust and confidence in data, such as data lineage, stewardship, and data ________.

• analytics

• quality

• scrambling

• leading

• access

According to a 2023 survey by Drexel University’s LeBow College of Business, 77% of data and analytics professionals consider data-driven decision-making as a ________ goal for their data programs.

• analytics

• quality

• scrambling

• leading

• access