7 Ways To Use AI For Better Business Intelligence Services
It is no surprise that businesses have always collected massive amounts of data. However, the real challenge lies in making sense of this data and deriving actionable insights.
Enter Business Intelligence Services.
BI helps businesses collect, analyze and transform their data into valuable insights that can quickly help them get through their long as well as short-term decision-making process.
But more than BI alone can not help gain the required edge and maximize efficiency.
Here comes AI. Artificial Intelligence is helping make BI automate its primary yet tedious and time-consuming processes, which were more painful to carry manually. From data collection to improving predictive business analytics solutions, personalizing BI services, and much more, AI in BI is everything businesses could dream of.
As we look to the future of Business Intelligence trends, one thing is clear - AI will play a vital role in shaping the landscape of BI services. This article will explore seven ways to use AI for better BI services.
Let’s head straight toward it.
How isAI powering Business Intelligence Services?
1. Automating Data Collection And Analysis
As long as AI wasn’t extensively used, data collection and analysis have been done manually, which was time-consuming and error-prone. However, with the help of AI, businesses can automate the entire process. AI algorithms can collect data from various sources, clean and process it, and then analyze it to derive insights. A PwC survey states 75% of businesses are using AI-powered automation for data analysis, and 67% of these businesses have reported increased efficiency and productivity.
2. Predictive Analytics
Predictive analytics uses statistical algorithms and machine learning techniques to analyze historical data and predict future events.
As per a study by Gartner, conversational artificial intelligence (AI) installations within contact centers will cut labor expenses for agents by $80 billion by 2026.
AI can significantly improve the accuracy of predictive analytics. The science behind it is that AI algorithms can continuously learn from new data, improving the accuracy of predictions over time. The algorithm can refine its predictions as more data is collected, leading to better Business Intelligence services. For example, AI can analyze customer data and predict which customers will likely churn.
3. Personalization
According to a report by Accenture, businesses that use AI for personalization can increase their revenue by up to 15%. Personalization is the process of tailoring products or services to meet the individual needs of customers. AI can analyze customer data to personalize BI services like Business Intelligence reports and dashboards.
For instance, AI can suggest personalized recommendations to customers based on their browsing history. It can also help businesses recommend customized pricing based on the individual's willingness to pay.
4. Natural Language Processing (NLP)
NLP is a branch of AI that focuses on the interaction between humans and machines using natural language.
Evaluating customer feedback and identifying whether the sentiment is positive, negative, or neutral is a hallmark of NLP with AI. This can help businesses understand customer preferences and improve their products or services accordingly. In this way, besides adding speed, accuracy, and greater personalization, AI-powered NLP can also render sentiment analysis efficiently.
5. Fraud Detection
It is the use of AI to identify fraudulent behavior. AI-powered fraud detection in Business Intelligence services can analyze vast amounts of data and identify patterns or anomalies indicating breaches in standard data patterns. For example, AI can analyze credit card transactions and identify fraudulent transactions, saving efforts from the manual investigation of crime scenes.
By reducing the costs associated with fraud detection, businesses can allocate resources more efficiently and focus on other areas of their Business Intelligence services.
