AI analytics has become a crucial component of modern businesses. With the explosion of big data and the increasing demand for data-driven decision-making, traditional methods of data analysis have become too slow, too limited, and too prone to error. Artificial intelligence (AI) offers a solution to these challenges, with its ability to analyze large datasets in real time, identify patterns, and provide insights that would be impossible for human analysts to discover.
Traditional data analytics has been around for decades, and it involves a technical team of data analysts. The process typically involves a business challenge, such as a decline in sales, and the team of analysts would try to identify the potential causes for the problem. They would then write a report that summarizes their findings and present potential next steps for the business to take.
While this process has been effective in the past, it is time-consuming, limited, and prone to errors. Analysts can only work with the data they have been given, and they are limited by their biases and pre-existing assumptions about the likely causes of a change in revenue. In contrast, AI-based analytics can analyze large quantities of data objectively and test infinitely more hypotheses than traditional analytics in seconds, not weeks.
What is AI Analytics
AI analytics refers to the use of artificial intelligence and machine learning algorithms to analyze large datasets in real time, identify patterns, and provide insights that would be difficult or impossible for human analysts to discover. It offers benefits such as scale, speed, and accuracy over traditional methods of data analytics.
AI analytics is the product of automated/Augmented data analysis using the power of AI and machine learning technologies. AI-based analytics constantly monitors and analyzes large amounts of data using machine learning algorithms to identify unusual patterns and anomalies in real time. For example, an AI-based anomaly detection solution learns the normal behavior of the data without being explicitly told what to look for, and it leverages clustering and correlation algorithms to provide a root-cause analysis. This means that any issues can be remediated as soon as possible, reducing remediation time by orders of magnitude.
AI analytics offers a few key benefits over traditional analytics, including:
• Scale: AI-based analytics can analyze data at any granularity, such as revenues per country, products, channels, etc.
• Speed: AI analytics can identify unusual drops in revenue and alert the appropriate teams in real-time, reducing remediation time by orders of magnitude.
• Accuracy: AI analytics is based on machine learning algorithms that can learn many different patterns of normal behavior very accurately and provide correlations between anomalies in a way that is nearly impossible for an analyst to perform.
What is Augmented Analytics
Augmented analytics refers to the use of low-code/no-code tools, often leveraging machine learning, to automate various tasks required during the analytics process. Products in this category leverage technologies to automatically discover, visualize, and contextualize insights like correlations, exceptions, drivers, and predictions. The general purpose is to democratize analytics by delivering insights at every level of an organization rather than limiting its power to dedicated data science and business intelligence teams.
Machine learning capabilities are at the core of any augmented analytics toolset. ML reduces and, in some cases, eliminates much of the manual work associated with generating insights and decision support for organizations. In addition, augmented analytics tools can acquire industry or organization-specific knowledge over time, creating more relevant and effective business results for its users. This automation and localization are critical to democratization and data-driven digital transformation efforts.
Forrester defines augmented business intelligence (BI) platforms as enterprise reporting and analytics software augmented with AI to provide descriptive and diagnostic analytics, data visualization and exploration, reporting and dashboarding functionality, data integration, and advanced predictive and prescriptive analytics based on statistical analysis and machine learning. These platforms support both graphical and conversational (cognitive) user interfaces.
Read Scikiq is ranked among the top 34 global augmented business intelligence platforms
Examples & Use cases for AI analytics
Organizations can leverage AI to improve data analytics in various ways, such as:
- Real-time Data Analysis: AI-based analytics can analyze large amounts of data in real-time and provide insights that would be impossible for human analysts to discover.
- Predictive Analytics: By leveraging machine learning algorithms, organizations can predict future trends and outcomes based on historical data.
- Natural Language Processing (NLP): NLP enables organizations to analyze unstructured data, such as social media data, customer reviews and surveys, and provide insights based on that data.
- Automated Anomaly Detection: AI-based analytics can automatically detect unusual patterns in data, enabling organizations to identify potential issues and take action quickly.
- Augmented Analytics: AI-powered tools can automate various tasks in the analytics process, making it more accessible to non-technical users and democratizing data-driven decision-making throughout the organization.
One example of the application of AI analytics is fraud detection. Traditional fraud detection techniques can only detect known patterns of fraud, making them vulnerable to new types of fraud that have not been encountered before. In contrast, AI-based fraud detection systems can detect previously unknown patterns of fraud by analyzing large amounts of data and detecting unusual patterns or anomalies. This can help organizations to detect fraud more quickly and accurately, potentially saving millions of dollars in losses.
Another example of AI analytics is customer experience analysis. By analyzing customer interactions with a company, such as through phone calls, emails, and social media, AI-powered analytics can identify patterns and trends in customer behavior, such as common complaints or reasons for customer churn. This information can be used to improve customer service and retention, leading to increased customer satisfaction and revenue.
In summary, AI analytics offers businesses the ability to analyze large amounts of data in real time, identify patterns and anomalies, and provide insights that are impossible for traditional data analytics to discover. With its ability to improve decision-making, reduce errors, and increase efficiency, AI analytics is becoming an essential tool for businesses looking to stay ahead of the competition in the digital age.