I will do ai based data analysis and insights
I will do ai based data analysis and insights
Discover the future of data and analytics: The latest trends changing the D and A landscape. Attend Gartner Data and Analytics in Sydney, Covering Strategy, Machine Learning, AI and more!
Get ai based data analysis and insights
AI-based data analysis and insights refer to a subset of business intelligence that uses machine learning techniques to discover insights, find new patterns and discover relationships in the data1.
It helps in identifying hidden patterns in large data sets and uncovers trends and actionable insights2.
It leverages technologies such as Analytics, Machine Learning, and Natural Language Generation to automate data management processes and assist with the hard parts of analytics23.
Augmented analytics: the combination of AI and analytics is the latest innovation in data analytics. For organizations, data analysis has evolved from hiring “unicorn” data scientists – to having smart applications that provide actionable insights for decision-making in just a few clicks, thanks to AI.
Augmenting by definition means making something greater in strength or value. Augmented analytics, also known as AI-driven analytics, helps in identifying hidden patterns in large data sets and uncovers trends and actionable insights. It leverages technologies such as Analytics, Machine Learning, and Natural Language Generation to automate data management processes and assist with the hard parts of analytics.
How does AI improve Analytics?
The latest advances in Artificial Intelligence play a significant role in making business processes more efficient and powerful with the help of automation. Analytics, too, is becoming more accessible and automated because of AI. Here are a few ways in which AI is contributing to analytics:
With the help of machine learning algorithms, AI systems can automatically analyze data and uncover hidden trends, patterns, and insights that can be used by employees to make better-informed decisions.
AI automates report generation and makes data easy-to-understand by using Natural Language Generation.
Using Natural Language Query (NLQ), AI enables everyone in the organization to intuitively find answers and extract insights from data, thereby improving data literacy and freeing time for data scientists.
AI helps in streamlining BI by automating data analytics and delivering insights and value faster.
So, how does it work?
While traditional BI used rule-based programs to deliver static analytics reports from data, augmented analytics leverages AI techniques such as Machine Learning and Natural Language Generation to automate data analysis and visualization.
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- Machine Learning learns from data and identifies trends, patterns, and relationships between data points. It can use past instances and experiences to adapt to changes and improvise on the data.
- Natural Language Generation uses language to convert the findings from machine learning data into easy-to-decipher insights. Machine Learning derives all the insights, and NLG converts those insights into a human-readable format.
Augmented analytics can also take in queries from users and generate answers in the form of visuals and text. This entire process is of generating insights from data is automated and makes it easy for non-technical users to easily interpret data and identify insights.
Augmented Analytics for Enterprises
Business Intelligence can help in making improved business decisions and driving better ROI by gathering and processing data. A good BI tool collects important data from internal and external sources and provides actionable insights out of it. Augmented analytics simply improves business intelligence and helps enterprises in the following ways:
- Accelerates data preparation
Data analysts usually spend most of their time in extracting and cleaning their data. Augmented analytics takes away all the painstaking processes that data analysts need to do by automating the ETL (extract, transform and load) data process and providing valuable data that can be useful for analysis.
2 ; Automates insight generation
Once the data is prepared and ready for processing, augmented analytics uses it to automatically derive insights. It uses machine learning algorithms to automate analyses and quickly generate insights, which would take days and months if done by data scientists and analysts.
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