Companies are using AI in analytics to manage data and enhance their business processes.
FREMONT, CA: Augmenting involves creating something greater in strength or value. Augmented analytics, often identified as AI-driven analytics, helps identify hidden patterns in large data sets and introduces trends and actionable insights. To automate data management processes and help with the problematic parts of analytics, it leverages technologies like Analytics, Machine Learning, and Natural Language Generation.
HOW DOES AI IMPROVE ANALYTICS?
With the assistance of automation, the latest developments in Artificial Intelligence play an essential role in making business processes more prosperous and powerful. Due to AI, analytics is becoming more accessible and automated. Here are several forms of AI contributing to analytics:
• By using Natural Language Generation, AI automates report generation and makes data easy to understand.
• By automating data analytics and generating insights and value faster, AI helps in streamlining BI.
• AI systems can automatically analyze data with the help of machine learning algorithms and discover hidden trends, patterns, and observations that employees can utilize to make better-informed choices.
• Using Natural Language Query (NLQ), AI helps streamline BI by automating data analytics and providing information and value faster. Using Natural Language Query (NLQ), AI allows everyone in the enterprise to find answers intuitively and derive insights from data, increasing data literacy and freeing data scientists' time.
AUGMENTED ANALYTICS FOR ENTERPRISES
By collecting and processing data, Business Intelligence can help make enhanced business decisions and drive better ROI. A useful BI tool collects crucial data and offers actionable insights from internal and external sources. Augmented analytics strengthens company intelligence and assists businesses in the following ways:
Accelerates data preparation
Data analysts typically spend much of their time extracting their data and cleaning it up. Augmented analytics eliminates all the tedious procedures that data analysts need to do by automating the ETL data process (extract, transform and load) and delivering valuable data that can be significant for analysis.
Automates insight generation
When the data is prepared and ready for processing, it is automatically used to extract insights through augmented analytics automatically. To automate analyses and produce insights instantly, it utilizes machine learning algorithms that would take days and months if performed by data scientists and analysts.
Allows querying of data
Augmented analytics makes it simple for users to ask questions and connect with a knowledge. It takes queries in the form of natural language with the aid of NLQ and NLG, converts them into machine language, and then generates concrete results and observations in the context of easy-to-understand language. It makes data analytics a two-way conversation in which organizations can ask their data questions and get feedback in real-time.