In the ever-evolving world of technology, two terms have become inextricably linked — data and artificial intelligence (AI). As someone who’s worked extensively in the field of data analytics, I can attest that the synergy between these two entities has given rise to more effective and efficient analytical approaches. By combining data and AI, organizations can uncover hidden insights, make informed decisions, and drive innovation like never before.

In this article, I’ll look at the powerful collaboration between data and AI, and how it’s changing the data analytics landscape. First, I’ll start by reviewing traditional data analytics approaches and then detail the collaborative dynamics between data and AI. Also, you’ll learn about three innovative approaches to harnessing AI’s power to supercharge data analytics. Additionally, I’ll detail 11 compelling use cases that demonstrate AI’s potential. Finally, I’ll share some essential cautions to consider when leveraging AI in data analytics.

1. Types Of Data Analytics Approaches Before AI and Fast Computing.

Previously, traditional data analytics, based in statistics and mathematics, required highly skilled analysts to interpret complex data sets using techniques such as descriptive and predictive analysis. However, these manual and time-consuming approaches were limited by significant setup costs and a lack of capacity to handle vast amounts of data, ultimately restricting business analytics scope and effectiveness. At the same time, traditional data analytics is still a very valuable tool, now supercharged by massive increases in computing power, high-speed internet, and commercially viable AI. To summarize, below is a list of the four types of traditional data analytics to include the questions each answer.

Four Types of Traditional Data Analytics

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• Descriptive Data Analytics: What Happened?
• Diagnostic Data Analytics: Why Did It Happen
• Predictive Data Analytics: What Is Most Likely To Happen?
• Prescriptive Data Analytics: What Action Should We Take?  

For more detailed discussion on data analytics approaches to include on-demand analytics and AI-powered analytics, see my article, A Data Analytics Perspective To Better Empower Supply Chain Managers.

2. Understanding the Dynamics Between Data and AI in Analytics.

First to better understand data analytics and AI’s synergistic relationships with each other, let’s start with some definitions. See definitions below:

“Data analytics converts raw data into actionable insights. It includes a range of tools, technologies, and processes used to find trends and solve problems by using data. Data analytics can shape business processes, improve decision-making, and foster business growth.”

AWS

“Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”

Britannica

So, the relationship between data, AI, and analytics can be likened to a synergistic ecosystem. Data serves as the fuel for AI, providing the raw material that AI algorithms need to learn and improve. On the other hand, AI can enhance data analytics by automating it, thereby making it faster, more accurate, and capable of handling big data sets. AI can also help to uncover hidden patterns and correlations in data that may not be apparent through traditional analysis methods. Hence, AI supercharges data analytics, resulting in profound and more actionable insights. 

“Data serves as the fuel for AI … AI can enhance data analytics by automating it … ,resulting in profound and more actionable insights”

3. AI-Powered Data Analytics Apps: 3 Approaches For Supercharging Business Analytics.

As we continue to explore the intersection of data and AI, the rise of Machine Learning (ML) and Large Language Models (LLMs) is revolutionizing data analytics. Specifically, LLMs, like OpenAI’s ChatGPT, excel at processing and generating human-like text, enabling the extraction of insights from unstructured data. Also, by identifying patterns, deciphering contexts, and uncovering hidden meanings, these models enhance our ability to interpret language and unlock the full potential of data. Additionally, LLMs provide a direct user interface for decision-makers, reducing the need to rely on IT departments or data analysts. As these technologies evolve, they promise to transform data analytics, ushering in a new era of AI-driven insights that all businesses can start leveraging today.

Below are three approaches for integrating AI-powered data analytics apps.

a. DIY AI-Powered Data Analytics App.

Use Retrieval Augmented Generation (RAG) Or AI-Agent Approach.

First, businesses can create AI-powered data analytics apps using a Retrieval Augmented Generation (RAG) approach. However, this approach requires a programmer or tech-savvy data analyst. Specifically, this method retrieves relevant knowledge from business data and feeds it to a Large Language Model (LLM) for comprehensive answers. Also with this DIY approach, businesses can use autonomous AI agents to refine results through multiple task executions such as conducting multi-hop queries and weighing out different options.

For more information on creating your own AI-powered data analysis app, see James Nguyen’s Forget RAG: Embrace agent design for a more intelligent grounded ChatGPT!. Also, see my article, The New AI Agent Business Specialist: You Need to Know Their Skill, Expertise, and Comparative Performance.

b. Leverage Cloud-Based Apps.

Build Your AI App Using A Data Intelligence Platform.

Also, an organization can develop an AI LLM app using a platform like Databrick or Snowflake. To do this a business would first need to set up their workspace on the platform. Then, they import their data and use Databrick’s built-in functionality to manage and manipulate the data. Next, they create, tune, and deploy their own generative AI model within the Databrick environment. As a result, the business can start leveraging its machine learning capabilities for its needs. Once the AI model is ready and tested, the business can build an app interface around it. As a result, this approach allows for a seamless integration of data management, AI modeling, and app creation within a single platform.

c. Subscribe to an AI-Data Analytics Chatbot.

Sign Up for a Chatbot Tailored For Business Analytics, No Coding Needed.

Lastly, businesses can opt for an AI-data analysis platform like Writer or DataGPT, which caters directly to business users. For instance, DataGPT helps users monitor and investigate changes in key metrics, visualize data, and connect to both cloud and internally hosted data sources. Additionally, the setup involves connecting your data set, defining a data schema, and customizing a Data Navigator with key metrics and dashboards. Indeed, the key advantage is its user-friendly interface and a language model optimized for end-user data analysis, making it accessible to non-technical users. Also, other analytical chatbot subscription options include DataChat, which uses a spreadsheet interface, and Julius, a file-based AI-data analysis app.

4. Eleven Business Analytics Use Cases When Data And AI Work Together.

So, when data and AI come together, they bring forth many new capabilities in business analytics. Below are 11 examples of new business analytics use cases that AI can provide:

Example AI Use Cases For Business Analytics

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• Generate On-Demand, Real-Time Insights. Here, AI algorithms can process large datasets in real-time, enabling businesses to make quick, informed decisions based on the latest data trends.
• Work With Unstructured Data. Also, AI and data science tools can analyze and interpret unstructured data such as text, files, images, and audio, providing a broader scope for business analytics.
• Interpret and Generate Code. Additionally, AI can understand and write code, enabling it to create custom analytics tools or modify existing ones based on business needs.
• Incorporates Feedback Loop for Knowledge Acquisition. Primarily through Machine Learning (ML), incorporates feedback to identify new patterns without explicit programming.
• Digital Fusion. AI and other emerging tech such as Knowledge Graphs and Computer Vision enable the rapid fusion, sharing, and unified view of information from multiple sources.
• Automates Exploratory Data Analysis. Here, AI can automate the exploratory data analysis process, quickly identifying patterns, correlations, and outliers in large datasets. This is a major labor-saving advantage.
• Automates Analysis and Natural Language Communication. Also, AI can automatically analyze data and present findings in natural language, making insights more accessible to non-technical stakeholders.
• Streamline Presentation Preparation. AI can use data analysis results to create clear, concise presentations, reducing the time and effort required to communicate insights.
• Generate Synthetic Data Generation. AI can also generate synthetic data that mimics real-world data patterns, providing a valuable resource for testing and validating analytics models.
• Augment Stress-Testing and Risk Analysis. Also, AI can improve stress-testing and risk analysis by simulating a variety of scenarios and assessing potential outcomes, helping businesses to prepare for different contingencies.
• Autonomous Agents. Lastly superior data sets and learned patterns enable intent-based AI agents to operate independently, making decisions and taking actions.

5. Beware Of AI Weaknesses.

AI Impact On Business Decisions – Know AI’s Unique Challenge To Overcome Its High Number Of Weaknesses

Indeed, it is incredible the impact that AI can have on the realm of data analytics and decision-making. At the same time, we need to be aware of its weaknesses. For a more detail discussion on AI weaknesses, see the following references.

• AI Weaknesses. AI Impact On Business Decisions – Know AI’s Unique Challenge To Overcome Its High Number Of Weaknesses. In this article, I discuss 11 key pitfalls to watch out for. Indeed, we need to fully comprehend AI impact on business decisions – the good and the bad.
• Digital Garbage-In, AI Garbage Out (GIGO). Moreover, AI will not work if it does not have good data. To illustrate this weakness, see my article, Poor Shipping Data Analytics – Here Are The 4 Reasons Impeding High Tech Visibility And Optimization .

More References.

• IIBA’s How AI Is Rewriting the Rules of Data Analysis
• SC Tech Insights’ article, Business Automation AI Remake: First Just Tech To Empower Processes And Now Operates Autonomously

Business Automation AI Remake: First Just Tech To Empower Processes And Now Operates Autonomously

Need help with an innovative solution to make your supply chain analytics actionable? I’m Randy McClure, and I’ve spent many years solving data analytics and visibility problems. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in launching new analytics-based strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your analytics or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

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