The impact of business intelligence system (BIS) on quality of strategic decision-making

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 This study aims to investigate the impact of Business Intelligence Systems (BIS) on the quality of strategic decision-making in top-level management. The independent variables in this study are Data Quality, Data Visualization, and BI Management, while the dependent variable is the Quality of Strategic Decision-Making. Additionally, the study explores the moderator variable, BI Scope, to further understand the relationship between BIS and the quality of strategic decision-making. By providing valuable insights into the relationship between BIS and the quality of strategic decision-making, this study contributes to the existing body of knowledge on business intelligence and strategic decision-making. The findings show that BI Management, BI Scope, Data Quality, and Data Visualization have substantial and favorable correlations with the quality of strategic decision-making. Effective BI Management techniques contribute to higher decision-making quality, emphasizing the necessity of ...
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Business Intelligence Trends: 10 Innovations for 2026

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 The biggest BI challenge is no longer collecting information—it’s making sense of complex, scattered datasets and turning them into strategic decisions at scale. As data volumes grow and speed becomes a competitive advantage, BI teams must adapt to new technologies and evolving business needs.

This article breaks down the key business intelligence trends shaping 2026, from AI-powered analytics to new approaches in data governance. Staying ahead of these developments will help businesses streamline reporting, enhance decision-making, and maximize the value of their data.

Augmented Analytics

Augmented analytics is an approach to data analytics that employs advanced technologies such as AI and machine learning to automate data preparation, insight generation, and insight sharing.

A primary distinction between augmented analytics and traditional analytics methods lies in the former's emphasis on automation and accessibility. Traditional analytics typically involve manual processes and require specialized skills in areas such as data science and statistics. These prerequisites can lead to bottlenecks in the analysis process, particularly when dealing with large volumes of data.

In contrast, augmented analytics automates many of these processes, making data analysis faster and more efficient. Additionally, it removes the barrier of technical expertise, allowing individuals without extensive data science backgrounds to understand and utilize complex datasets. 

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a confluence of computational linguistics and artificial intelligence, that provides machines with the capability to understand, interpret, generate, and respond to human language in a meaningful and contextual manner. An example of a program that utilizes natural language processing is ChatGPT.

NLP’s integration within the business intelligence landscape brings a significant shift, particularly in how decision-makers interact with data. Traditional interaction methods require command-based queries, coded instructions, or complex interfaces. With NLP, these interactions become as simple as typing or voicing a question in plain English, or any other language for that matter. This shift introduces a new level of intuitiveness and accessibility, enabling individuals to communicate with data analysis tools in the same way they would with another human.

From a marketing perspective, NLP can revolutionize the understanding of customer sentiments, market trends, and brand perception. It aids in analyzing unstructured data sources such as social media posts, customer reviews, and call center transcripts, providing a wealth of insights about customer preferences, behaviors, and sentiments.

Natural language processing is also pivotal in the rise of conversational analytics. With the aid of chatbots and voice assistants, decision-makers can ask direct questions or give commands to their data analysis software in natural language. The software, in return, provides the required insights in a digestible, conversational manner. This two-way natural language interaction significantly simplifies the data exploration process, allowing executives to focus more on decision-making rather than navigating complex data interfaces.

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