Google, Intel, IBM, NVIDIA, Amazon, PwC, and the list can go on for the big brands adopting AI in data analysis. 

How Is AI Used in Data Analysis?

The term “artificial intelligence data analysis” refers to the application of data science and AI methods to improve data cleansing, inspection, and modeling. The ultimate aim is to find useful data that can back up conclusions and decisions.

AI streamlines operations by automating repetitive tasks. Companies can save time and effort by training a computer program to do repetitive tasks instead of humans. Artificial intelligence (AI) can be programmed to mimic human intellect, which allows it to recognize patterns and produce reliable results.

While learning about this issue, it’s crucial to understand that data analytics and analysis are not the same thing. Data analytics, a branch of BI, is all about mining data for hidden patterns and trends using machine learning.

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Examples Of AI Data Analysis

• Sentiment analysis. An examination of public opinion. Looking at information about a topic online and analyzing its consumer feedback is called sentiment analysis. With the help of AI, businesses can track the success of their brands and products by identifying positive, negative, and neutral sentiments. Netflix is one example of a firm that uses AI for sentiment analysis; the service uses AI to find problems and fix them so users have a better experience while watching.
• Predictive analytics and forecasting. Forecasting and predictive analytics. To foretell future sales and purchase habits, AI analytic systems can examine historical data, market data, and other elements. For instance, to generate highly focused presentations, Bank of America utilizes predictive analytics to comprehend the connection between equity capital markets (ECM) agreements and investors.
• Anomaly detection and fraud prevention. Identifying suspicious activity and preventing fraud. To detect fraud, businesses need to sift through mountains of data. However, there is simply too much data for humans to handle manually, especially given the proliferation of online scams and schemes. In this case, AI can be useful. Spotify, for instance, uses AI to identify instances of fraudulent streaming behavior. By analyzing data such as listener behavior and IP addresses, their AI system can detect and prevent acts such as bot-generated plays.
• Image and video analysis: Analysis of images and videos. Artificial intelligence (AI) can analyze movies and photographs and provide the user with information about what’s happening in the image. People can be located, patterns may be found, and diseases can be identified in patient scans. To better monitor inventory and prevent theft, Walmart, for instance, employs AI for video and picture analysis. Artificial intelligence technologies at Walmart can detect theft, monitor inventory levels, and identify products on shelves.
• Natural Language Processing (NLP): NLP techniques enable AI systems to analyze and derive insights from unstructured textual data, including emails, social media posts, customer reviews, and documents. NLP is used for sentiment analysis, topic modeling, text summarization, and other text analytics tasks.
• Clustering and Segmentation: AI techniques like clustering algorithms group similar data points together based on certain characteristics. Segmentation helps in understanding customer behavior, market segmentation, and personalized marketing campaigns.

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The 10 Best AI Data Analysis Tools in 2024

Here are some of the best AI tools to analyze data that are trending in 2024.

1. Polymer

With PolymerSearch.com, an easy-to-use business intelligence (BI) tool, you can make professional-quality data visualizations, dashboards, and presentations. And all that without ever touching a piece of code. Many different types of data sources can be easily integrated with Polymer. Integrate data sources such as Google Analytics, Facebook, Google Ads, Google Sheets, Airtable, Shopify, Jira, Stripe, WooCommerce, BigCommerce, and more with ease. You may also upload datasets using XSL or CSV files. After you’re linked, Polymer’s AI will automatically evaluate your data, provide insightful suggestions, and create visually appealing dashboards.

2. Tableau

With Tableau, customers can engage with their data without knowing how to code, thanks to its analytics and data visualization capabilities. The user-friendly platform facilitates the real-time creation, modification, and seamless sharing of dashboards and reports among users and teams. As one would expect from a tool of its kind, it supports databases of varying sizes and provides users with several visualization choices to help them make sense of their data.

3. MonkeyLearn

Another tool that doesn’t require coding is MonkeyLearn, which allows customers to see and reorganize their data with AI data analysis features. Depending on the user’s requirements, the platform’s built-in text analysis capabilities may quickly assess and display data. Automatic data sorting by topic or intent, feature extraction from products, and user data extraction are all within the user’s control with text classifiers and text extractors.

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4. Microsoft Power BI

One well-known business intelligence product, Microsoft Power BI, also lets users visualize and filter their data to find insights. Users can begin making reports and dashboards right away after importing data from almost any source. In addition to using AI-powered features to analyze data, users can construct machine learning models. Despite its higher price tag, the platform offers native Excel integration and a user interface that is quicker and more responsive than competing options. It also comes with many integrations.

5. Sisense

Another data analytics software that helps developers and analysts organize and display data is Sisense. The platform’s dynamic user interface and many drag-and-drop capabilities make it simple to use. When working with huge datasets, Sisense’s “In-Chip” technology makes calculation faster by letting users pick between RAM and CPU to handle the data. Users with basic reporting and visualization needs who are working with smaller datasets may find the platform to be a decent fit, despite its restricted visualization features.

6. Microsoft Excel

Back when it was first released, Microsoft Excel stood head and shoulders above the competition when it came to data analysis. Quickly process and analyze data, make various basic visualizations, and filter data with search boxes and pivot tables—all with Excel’s Data Analysis Toolpak. Machine learning models, cluster data calculations, and complicated neural networks can all be built in Excel using formulas, and the program even lets users avoid coding altogether. Even without the requirement to code, Excel’s spreadsheet paradigm and steep learning curve limit its potential.

7. Akkio

To help businesses make informed decisions, Akkio provides a platform for data analytics and forecasting. You can qualify, segment, and prioritize your lead lists with the help of this no-coding platform’s lead-scoring tools. Using the data at their disposal, users can access future forecasts on nearly any dataset thanks to the forecasting features. Quick and easy to use, the tool has a small but helpful set of connectors for transferring data to and from other programs.

8. Qlik

Both technical and non-technical users will appreciate the platform’s adaptability and the many data exploration options it comes with. Teams may work together on the platform with ease, utilizing workflows and drag-and-drop editors to customize their data. Despite its robust functionality, QlikView is only a good fit for users who can make full use of the platform due to its costly price and relatively limited AI feature set.

9. Looker

Looker is an additional no-code tool for data analysis and business intelligence that is part of the Google Cloud. It has significant features and integrates with numerous services. Looker can consolidate all of a user’s data sources into one location, handle massive databases, and let users create numerous dashboards and reports. In addition to having Google’s support, the platform has powerful data modeling capabilities. The site is user-friendly, however it lacks customization options and makes report creation a tedious process.

10. SAP BusinessObjects

SAP BusinessObjects integrates well with the rest of the SAP suite and enables less technical users to analyze, visualize, and report on their data. It gives people access to AI and ML tools, which they may use for things like data visualization and modeling, better reporting, and dashboarding. Users can also get predictive forecasting features to go further into their data with this tool. Despite the platform’s price cuts, the solution’s overall cost—especially when purchasing platform licenses—can be too high for some. Users who are currently customers of SAP and can make use of an AI data tool that integrates with their existing SAP capabilities will find this tool to be more suitable.

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Exclusive Commentary

We had exclusive commentary from one of our AiThority guest in his byline from Arvind Rao is the Chief Technology Officer, Edge Platforms, EdgeVerve.

Companies are increasingly using Robotic Process Automation (RPA), easily among the most widely applied tools, to streamline all insurance processes, including marketing, renewals, and sales. A notable instance from the industry demonstrates that Connected Automation can significantly enhance operational efficacy, with one major insurance firm in the US reportedly achieving around 95% efficiency in its processes.

While admittedly RPA has its embedded advantages, it is also critical to leverage cognitive capabilities with AI and analytics for a greater degree of efficiency. The inclusion of cognitive software solutions, like natural language processing, can contribute to the transformation of the insurance business from a purely human-oriented domain to an intelligent business landscape.

Clearly, the technological options available at present can only address part of the challenge. Leaders of connected enterprises have the task of persuading insurance firms to move away from traditional methods, and also further raise the level of intelligent technology adoption. While AI is being used in the process, data of low relevance can have a debilitating impact on the decision-making process. Contextual data, incorporation of the organization’s policies, and historical interpretation of policy decisions, together with AI, can help throw up more intelligent and accurate recommendations to underwriters in terms of what kind of risk is acceptable.

Future Trends in AI Analytics

An estimated $154 billion was spent worldwide on AI research and implementation in 2023, marking the fastest-ever growth in AI expenditure.

Among artificial intelligence subfields, generative AI is booming. With the rise of chatbots and other forms of direct user interaction with AI, AI systems are rapidly becoming more collaborative.

Read: How to Incorporate Generative AI Into Your Marketing Technology Stack

According to reports, three billion individuals utilize Google’s AI assistant for email assistance and collaboration within the Google Workspace suite. Separately, in just a few months, ChatGPT (a joint venture between OpenAI and Microsoft) amassed more than 100 million users. Another development in artificial intelligence is the displacement of huge corporations by smaller generative models that may be run on desktop computers. Companies no longer need to depend on a third party to develop their AI applications; new approaches in deep learning and neural networks greatly improve the efficiency of running AI models on local devices. This is in contrast to traditional AI models, which consume a lot of resources.

FAQ’s

• How does AI handle unstructured data in analytics?

AI uses natural language processing (NLP) and other techniques to analyze unstructured data like text, images, and audio, extracting valuable insights.

• What is the difference between supervised and unsupervised learning in AI?

Supervised learning involves training an AI model on a labeled dataset, whereas unsupervised learning involves finding patterns and relationships in data without labeled outcomes.

• Can AI help in real-time data analytics?

Yes, AI can process and analyze data in real time, enabling immediate insights and timely decision-making.

• What are neural networks and how are they used in data analytics?

Neural networks are a type of machine learning model inspired by the human brain, used in tasks like image recognition, speech processing, and complex pattern recognition in data analytics.

• How does AI improve data visualization?

AI can automatically generate insightful visualizations, highlight key trends and anomalies, and personalize dashboards based on user preferences and behaviors.

• What is the role of AI in anomaly detection?

AI models can identify deviations from normal patterns in data, which is useful for detecting fraud, network security breaches, and other irregular activities.

• How does AI contribute to customer analytics?

AI helps analyze customer data to understand behavior, predict future actions, personalize marketing, and improve customer satisfaction.

• What are some ethical considerations in using AI for data analytics?

Ethical considerations include ensuring data privacy, avoiding biases in AI models, maintaining transparency in AI decisions, and preventing misuse of AI insights.

• How can businesses start integrating AI into their data analytics processes?

Businesses can start by identifying use cases, ensuring data quality, selecting appropriate AI tools, and hiring or training staff with the necessary skills.

• What is deep learning and how does it relate to data analytics?

Deep learning is a subset of machine learning that uses multi-layered neural networks to analyze large and complex datasets, enabling high-level abstraction and insights.