Data Quality Challenges using Artificial Intelligence during Software Feature Prioritization for New Product Development
Pysyvä osoite
Kuvaus
Opinnäytetyö kokotekstinä PDF-muodossa.
Artificial intelligence is increasingly used by organizations during software development for creating data-driven decisions. This supports prioritizing software features during new product development. Organizations use AI systems for analyzing large volumes of information, which depends on their ability to handle extensive data sets. This is also practised using technologies enabled with AI. The decision-making process becomes ineffective when organizations face data-related challenges due to low data quality.
The study examines how the data quality issues associated with using artificial intelligence are impacting the software feature-prioritization decision-making process during the new product development stage. The study focuses on existing research based on AI-supported data quality and decision-making process for prioritizing software features.
The research involves a qualitative methodology, following semi-structured interviews with ten professionals who have experience in AI-supported feature prioritization. The research used thematic analysis to study the data, which reveals patterns about decision-making and data quality problems. The findings indicate lower quality data, resulting in the use of AI-generated insights, leading to problems in decision-making for software feature prioritization outcomes, feature ranking, resource allocation and product schedules. The results indicate how AI systems function for decision-making, which involves human inputs.
The study demonstrates the effectiveness of AI-based decision-making as it is required in the organizations for maintaining higher data quality standards. The focus at organization level should be on having strong data governance and regulatory systems for serving strict guidelines and rules for the maintenance of data produced by AI used for feature prioritization.