Transforming BI with AI and Power BI
Business Intelligence (BI) and Data modeling
A discussion of the basic principles of Business Intelligence (BI) and data modelling and the role of AI in these fields.
Business Intelligence
Business Intelligence is the process of collecting, analyzing, and transforming data into actionable insights that can be used to make informed decisions.
Data Modelling
Data modelling involves the creation of a conceptual or logical representation of data objects and their relationships.
Data-Driven Decision Making
Business Intelligence and Data modeling enable organizations to gain a deeper understanding of their data to make informed, data-driven decisions.
AI features of Power BI
A detailed overview of the various AI features of Power BI, such as natural language processing and visual recognition, and how they can be used to enhance data analytics.
Process Automation with AI
Artificial Intelligence (AI) has revolutionized the field of Business Intelligence (BI) by automating many of the processes involved in data analysis. AI-powered BI systems can quickly identify patterns and trends in large data sets, enabling organizations to make data-driven decisions more quickly.
Predictive Modeling
AI can be used to create predictive models that forecast future trends and identify potential risks and opportunities.
AI in Data Modeling
Automating Data Modeling
AI can help automate the process of identifying patterns and relationships in data, making it possible to create accurate models quickly and easily.
Identifying Hidden Patterns
Machine learning algorithms can analyze data sets to identify hidden patterns and correlations, making it easier to create accurate models.
Natural language processing
Natural language processing is an AI feature in Power BI that enables users to interact with data in a conversational way using natural language input.
Visual Recognition
Visual recognition is an AI feature in Power BI that enables users to extract insights from images, videos, and other visual elements in their data.
Machine Learning Algorithms
Machine Learning Algorithms is an AI feature in Power BI that enables users to identify patterns and trends in their data and make predictions based on that analysis.
Natural Language Processing in Power BI
Power BI includes natural language processing (NLP) capabilities that allow users to ask questions about their data using plain language.
Benefits of Natural Language Processing
NLP can help organizations gain deeper insights into their data by making it easier to identify trends and patterns.
How NLP Works
NLP technology works by breaking down language into its component parts, analyzing those parts for meaning, and then using that information to provide answers.
AI Image Analysis
Visual Recognition is an AI feature that enables users to analyze images and identify the objects contained within them. This feature uses machine learning algorithms to analyze images in real-time.
Identifying Trends
Visual recognition can be used in Power BI to identify trends in images, such as product trends in e-commerce. This feature can help businesses identify emerging trends and adjust their marketing strategies accordingly.
Regression Analysis
Regression analysis is a statistical method that can be used to identify the relationship between a dependent and independent variable and create predictive models to forecast future trends.
Cluster Analysis
Cluster analysis is a machine learning algorithm that can be used to group similar data points together and identify patterns and trends in large datasets.
Best Practices for AI in Power BI
A discussion of the best practices for using AI in Power BI and the benefits it can offer.
Responsible Use of AI
AI can be a powerful tool for enhancing data analytics, but it is important to use it responsibly in a way that aligns with organizational goals. This requires clear communication and responsible use of the technology.
Data Governance
Effective use of AI requires a strong data governance strategy, including data quality control and data security and privacy measures. This ensures that the data used is accurate and relevant and that it is kept secure and confidential.
Continuous Improvement
Continuous improvement is key to successful use of AI. This requires regular evaluation of the technology and its effectiveness and the identification of opportunities for improvement and optimization.
This sample is part of a series that shows how you can use Power BI with business-oriented data, reports, and dashboards.
What are your top contributors for wins and losses?
Start by reviewing the top contributors that resulted in a status of win using the Key Influencers visual in the center of your report. From the visual, notice that the top contributor is when the discount goes up 2%; you’re 2.76x more likely to win new revenue.
Select the 2.76x indicator. Power BI displays a scatter chart next to it, showing the correlation between your discount and the percentage of this influencer.
As you interact with slicers, filters, and other visuals, the Key Influencers visual reruns its analysis based on the updated choice. From the Close % by Product category stacked bar chart, select the Furniture category to generate new insights based upon the updated selection. You see that when the product category is furniture and the salesperson is Molly Clark, you’re 1.50x more likely to win new revenue.
To reviews the top contributors for when your status changes, from the dropdown, select the Lost option to generate new insights based upon the updated selection. You can now answer the question, “What is the top contributor when a loss occurs?”
Decomposition Tree
Root cause and ad hoc analysis to understand the effect of sales opportunities across the different fields within your data.
Artificial intelligence splits to determine the path to the next highest sales opportunities in your data. Select the + symbol next to Tablets and the High Value option.
Benefits of AI in Power BI
Faster and More Accurate Data Analysis
AI-powered analytics can help organizations analyze data faster and with more accuracy than traditional methods, enabling them to make data-driven decisions more quickly and efficiently.
Improved Decision-making
AI-powered analytics can help organizations make more informed decisions by providing them with real-time insights and predictions based on data analysis, enabling them to respond more quickly to changing business needs and improve their decision-making processes.
Increased Efficiency
AI-powered analytics can help organizations improve their efficiency by automating manual processes, reducing the need for human intervention, and enabling them to focus on more strategic tasks that require critical thinking and creativity.
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