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New Trend: What are AI Analytics?
Before AI became accessible to all, business analytics were the most common way to interpret collected data. There are four stages in business analytics to analyze and interpret data.
The first stage is descriptive analytics, where an analyst has to determine what has already happened in the data based on hindsight.
The second stage is diagnostic analytics, where the analyst needs to identify why a particular event or change in the data has occurred based on insight.
The third stage is predictive analytics, which moves away from past insights to focus on foresight. The analyst has to determine what will happen next in the data.
The final stage, prescriptive analytics, is the most vulnerable and difficult stage as the analyst has to determine how exactly to turn a desired outcome into a reality through the data available.
AI analytics differ from business analytics by using machine learning and algorithms to constantly monitor and interpret huge amounts of data. AI analytics identifies many patterns of normal behavior very accurately. They provide correlations between anomalies in a way no other analyst could perform. It automates the time-consuming work normally done by human analysts. It often improves an analyst’s capabilities in terms of speed, the scale of the data being analyzed, and the granularity of monitored data. AI’s anomaly detection solutions are created by understanding the data’s normal behavior without being explicitly told what to look for. For example, the speed of an AI model identifies unusual drops in revenue and alerts the appropriate teams in real time. It leverages clustering and correlation algorithms to provide a root-cause analysis to remedy any issue as soon as possible.
Current businesses prefer to use AI analytics over traditional business analytics as it’s easier for AI analytics to forecast demand, predict maintenance, or monitor business conditions. For example:
To forecast demand, AI will first determine if there are any errors in the supply chain networks.
Predicting maintenance refers to AI-based techniques designed to predict the condition of a company’s equipment in order to estimate when maintenance will be performed.
Business monitoring is a part of diagnostic analytics to monitor various departments, such as customer experience and revenue/cost. Here’s an example of companies that use AI to improve customer satisfaction and increase revenue.
AI analytics has proven to benefit many businesses around the world. According to Annette Chacko, she states that “AI tools have become more affordable and user-friendly. They are enabling innovation and face the competition more confidently. Companies can gather business intelligence from varied data sources easily for a deeper undering of their market. Which in turn can be used to personalize customer interactions, enhance brand satisfaction and expand into new markets with foresight and precision.” By interpreting the data clearly, it helps businesses determine the best strategies to take in moving their companies forward.
There are several ways in which AI analytics shows the benefits of AI’s processing and interpreting data.
AI can generate code and debug errors through completing complicated tasks like visualizing large datasets and building machine-learning models.
AI tools like Tableau GPT can quickly explain a specific data point on a chart to determine its behavior and provide deeper insights for business professionals.
AI can create interactive dashboards and reports to quickly aggregate data from multiple sources by selecting data for visualization in a chart or graph.
AI can automate data entries into images without putting in the information manually.
AI analytics is a wonderful development for many businesses. It processes and interprets data better and faster. It also helps store and monitor data to keep track of how it behaves over a long period of time. AI analytics will keep on expanding and growing with various models for many different companies to use.
New Insight: the Best Models for AI Analytics
Many businesses have used several models and platforms to help decompress their data. There are many AI tools and platforms currently available to make data easier to analyze and find new insights.
1.Tableau
This platform is the easiest one to use. An analytics and data visualization platform allowing users to interact with the data without the need for coding. Users can create and modify dashboards along with reports in real time. It effortlessly shares them to any user or team. Its features include supporting databases of all sizes, offering multiple visualization options for users to analyze their data, etc. Users are also allowed to run the platform on the cloud or on-premise. It’s great for anyone looking for a more hermetic environment for their datasets.
For non-coding users, this platform works well with AI data analysis features to visualize and rearrange any dataset. It comes with multiple AI-powered text analysis tools to instantly analyze and visualize data to any user’s needs. They can set up text classifiers and text extractors to automatically sort data based on the right topic or intent, and extract product features. Its tool comes with multiple integrations to work with whichever tools are already utilized, and a simple, intuitive UI matching the simplicity of the platform.
3.Sisene
This platform is great for both analysts and developers. It’s a data analytics platform that sorts through and visualizes their data. It’s easy to use with multiple drag-and-drop tools and a responsive UI mode to streamline the experience. It also allows users to access their “In-Chip” technology and choose between RAM and CPU to process the data, making computation faster when handling large datasets. Despite the platform having limited visualization capabilities, it’s a good option for users seeking basic visualization and reporting needs to handle smaller datasets.
4.Akkio
A great non-coding platform perfect for forecasting data. It’s a business analytics and forecasting tool for users to analyze their data and predict its potential outcomes. It has lead scoring features for users to qualify and segment their lead lists. It prioritizes hot leads and offers quick results. The forecasting features allow users to use the data to get future predictions on practically any dataset. It’s intuitive and comes with a few useful integrations to get data to and from other tools.
5.QlikView
This platform uses the best of AI technology to analyze data. It’s a business intelligence and data visualization solution allowing users to tap into AI technology to analyze any data. It comes with multiple data exploration features and a versatile platform for both technical and non-technical users. Teams can seamlessly collaborate on the platform using simple drag-and-drop editors and workflows to manipulate their data to their liking. Although the platform is highly functional, the high cost and comparatively low AI feature set make it a great choice for users looking to utilize it to their full extent.
6.Looker
Another non-coding data analysis and business intelligence tool with strong capabilities and multiple integrations. It’s capable of processing large databases, allowing users to make multiple dashboards and reports, and condensing all their data sources into one. It also has advanced data modeling features backed up by Google. It doesn’t have as much flexibility, but it’s easy to navigate and builds on reports from scratch.
This platform is great for less-technical users to analyze, visualize, and report on their data. It works smoothly by integrating the rest of the SAP suite. Users have access to AI and machine learning technology for data modeling and visualization, enhanced reporting, and dashboarding. It also allows users to access predictive forecasting features to gain deeper insight into their data.
These tools have greatly benefited many companies in need of processing their data. As of now, AI analytics has proven itself to be a wonderful resource in delivering clear and precise data.
New Action: How to use AI Analytics Now
AI analytics has started to take over in the business analytics field. It has helped businesses analyze and interpret their datasets. It has given them instant results and given them the opportunities to decide which direction to go moving forward.
Traditional business analytics can only go so far. With AI, analysts can process datasets to gain more insights much faster.
AI analytics comprehend data in a brief and efficient manner to help businesses provide the information needed to advance.
While there are many ways, models, and tools to use AI analytics, they all depend on the application and the kind of results needed.
Now is the time to evaluate AI platforms that are currently available to make data analytics easier with faster time to insights.
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