If you’ve been following the technological developments of AI over the past few years, you know that it’s having an impact on nearly every industry, including business analytics.
Once driven by centuries-old statistical modeling techniques, AI and machine learning are now transforming the field of analytics. They are doing this by offering a level of speed, scale and granularity that’s humanly impossible.
AI analytics is the product of automating data analysis—a traditionally time-consuming and people-intensive task—using the power of today’s artificial intelligence and machine learning technologies. In addition to structured data sources, AI is increasingly able to analyze unstructured data, via the use of AI analytics tools such as natural language processing (NLP), speech analytics transcription, and computer vision for image and video analytics.
What is Analytics?
Analytics refers to the process of identifying, interpreting and communicating meaningful patterns of data. Business analytics refers to applying this process to answer business questions, make predictions, discover new relationships and ultimately make better decisions.
In essence, analytics is the process of taking raw data and applying some form of analytical technique in order to find meaningful patterns in the data. The analytical techniques that we can use vary, although a few of the most popular methods include:
- Applied mathematics
- Statistical analysis
- AI and machine learning
The field of analytics can be further broken down into several stages as highlighted in the Gartner Analytic Ascendancy model:
As you can see from the image above, the Gartner Analytic Ascendancy model is divided into four stages of increasing difficulty and value, these include:
- Descriptive analytics. The first stage of analytics is hindsight-based and asks the analyst to determine what has already happened in the data.
- Diagnostic analytics. The next stage is more insight-driven and asks the analyst to identify why a particular event or change in the data occurred.
- Predictive analytics. As we move past insights, the next step in analytics is based on foresight and determining what will happen next.
- Prescriptive analytics. Finally, often the most difficult and valuable stage in analytics is determining how exactly we can make the desired outcome become a reality.
Now that we have an idea of what analytics is and how it can help answer business questions, let’s discuss what AI analytics is.
Understanding the concept of AI analytics
AI analytics uses Artificial Intelligence (AI) tools like machine learning and natural language understanding to identify trends, provide insights, and predict outcomes. AI analytics is a way to automate data analysis tasks and avoid long lead times and inaccuracies.
At its core, AI in data analytics is the application of artificial intelligence (AI) to analyze large sets of data. This allows data analysts and scientists to uncover trends and gain insight into the behavior of consumers or other datasets. Using powerful machine learning algorithms, AI can help make sense of massive amounts of information quickly and accurately.
Organizations use AI analytics to organize structured and unstructured data from various sources. Dashboards can be created to help inform decision-making in real-time or during long-term projects. You can also apply AI analytics to various business tools and products — increasing the value of big data sets by improving how they can be analyzed.
Common AI analytics use cases include:
- Vision and optical character recognition to help manage the images your business collects.
- Speech and natural language processing to convert text to speech, recognize speakers and evaluate sentiment.
- Classification and clustering to create visualizations that help inform decisions.
- Assess scenarios by applying modeling to find the best course of action.
Artificial Intelligence
AI is a broad field of computer science that refers to any sort of intelligence demonstrated by machines. Often, this term refers to machines mimicking cognitive functions such as learning, problem-solving, reasoning, and representation. AI can be applied to everything from understanding human speech, self-driving cars, playing games, and of course analytics.
Several approaches to solving problems with AI include statistical techniques, search optimization and artificial neural networks.
AI Analytics
AI analytics refers to a subset of business intelligence that uses machine learning techniques to discover insights, find new patterns and discover relationships in the data. In practice, AI analytics is the process of automating much of the work that a data analyst would normally perform.
While the goal is certainly not to replace analysts, AI analytics often improves a data analyst’s capabilities in terms of speed, the scale of data that can analyzed and the granularity of the data that can be monitored.
Machine Learning
Machine learning is a subset of artificial intelligence that combines algorithms, statistical models, and data in order to perform a specific task, without being explicitly programmed. A key part of machine learning is that instead of providing explicit instructions for how to perform a task, the models rely on patterns and inference instead.
In particular, in order to perform machine learning this involves creating a model that is trained using training data and then can be fed new data in order to make predictions.
Difference between AI Analytics & traditional analytics
Traditional data analytics is generally undertaken by a technical team of data analysts. Here’s an example of how a team of analysts might traditionally attempt to solve a business challenge:
- An event, incident or trend occurs in the company over a period of time – for example, sales are down for the quarter.
- Data analysts then form hypotheses about what the potential causes might be for the sales decrease.
- These hypotheses are then tested against the data for that time period until they find enough evidence to support a particular hypothesis.
- The analysts then write a report that summarizes their findings and will often present potential next steps for the business to take.
As depicted, this whole process from the initial change to determining the underlying causes is extremely time-consuming. In addition, there’s also no guarantee that the answers the data analysts find are the right ones due to their inherent limitations.
AI analytics, on the other hand, based on machine learning algorithms constantly monitors and analyzes huge amounts of data. The results stand apart from traditional analytics in:
Scale
An AI-based anomaly detection solution learns the normal behavior of the data without being explicitly told what to look for. It does it at any granularity: revenues per country, products, channels, etc.
Accuracy
AI Analytics is based on ML algorithms that are able to 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 (correlations between millions of time series in some cases). Of course, the accuracy of the ML algorithms depends on how they were designed – they need to autonomously learn many different patterns accurately – which requires the use of multiple types of algorithms.
Also, unlike data analysts, these algorithms don’t have any bias towards the business questions at hand. For example, instead of having pre-existing assumptions about the likely causes of a change in revenue, AI analytics can analyze large quantities of data and provide a completely objective analysis of the situation. This means that AI analytics can test infinitely more hypotheses than traditional analytics — often in seconds instead of weeks.
Speed
The AI model will identify unusual drops in revenue and alert the appropriate teams in real time. In addition, an AI-based analytics solution leverages clustering and correlation algorithms to provide a root-cause analysis so that any issues can be remediated as soon as possible. This reduces remediation time by orders of magnitude since the analysis is done constantly, and in real-time, instead of quarterly, monthly, or weekly at best, as done with traditional analytics.
AI-based anomaly detection also finds anomalies faster.
Importance of AI in Data Analytics
To give a better understanding of why using AI is important in data analytics, let’s have a look at some benefits it provides.
Speed and efficiency
AI tools can process data much faster than humans, meaning that the insights you gain from your analyses are quicker and more accurate. This makes it easier for organizations to make and act on decisions quickly.
Humans also cannot fully remember all commands or library syntaxes of the different data analysis libraries. An AI assistant can help you quickly look up these commands and even suggest alternative approaches to your analysis.
Fact-checking and validation
With AI tools, you can quickly detect inconsistencies in your data.
For example, if the analytics results are out of sync with what you expected, an AI chatbot can help investigate why this might be. Additionally, some AI models can even highlight errors or potential issues before they occur.
Data democratization
Moreover, AI can provide more democratized access to data. You can learn more about data democratization in a separate article.
By using natural language processing (NLP) in AI chatbots, businesses can allow average non-data users to analyze large data sets and quickly extract key insights.
This makes it easier for less experienced users to gain access to complex datasets without having to be conversant in the technical language associated with data science.
Automated report generation
Using AI, businesses can automate the process of report generation. Instead of manually creating reports for each individual analysis, AI tools can generate reports automatically and quickly.
These automated reports ensure that all employees have access to the same information in a timely manner, regardless of their location or background.
Business benefits of AI analytics
The ability of AI systems to analyze data autonomously has multiple business benefits. Chief among them is reducing the labor cost of data scientists and other highly paid and limited-availability analytics professionals. Other benefits of using AI in analytics include:
- Risk management. AI analytics can improve the effectiveness of risk management models and create smarter strategies.
- Innovative products. AI analytics tools perform big data analysis that can drive updates to existing products and create new ones.
- Turbocharged supply chain. Supply chain executives recognize AI in analytics as a disruptor that empowers them to apply data-driven knowledge to solve previously unsolvable challenges.
- Customer engagement. Use AI analytics tools to determine what customers are looking for—acquire them, retain them, and cultivate their loyalty.
- Successful marketing campaigns. Create focused and targeted campaigns with AI analytics from current customer purchases.
Unique ways to apply AI in data analytics
Here are some of the ways to use AI in data analysis:
Generating code & debugging errors
First up, you’ll most likely be using AI for generating code or debugging errors in data analysis. This is particularly helpful for complex tasks, such as visualizing large datasets and building machine-learning models.
Some common AI coding assistants you can use include DataCamp Workspace AI, Anaconda Assistant, Jupyter AI, and GitHub Copilot.
For generating code, you’ll be able to use them to create code blocks for analysis. For example, you can prompt the Jupyter AI from within your programming environments in Python as long as it runs the IPython kernel. Additionally, if you’ve written some more complex code by yourself and have some trouble explaining it in text, you can ask the AI to comment on your code for you. In this manner, you can quickly save time on the tedious documentation process.
You can also get the AI to provide you with code completions, where you can begin typing a function and have the AI complete the rest of the code based on the information you included in your comment.
These tools are also able to debug errors you encounter, so the process of searching for a solution is shortened greatly.
If you’re an Excel user, Microsoft 365 Copilot can also help with creating formulas and macros.
Creating synthetic data
Another useful application of AI in the field of analytics is the production of synthetic data. In fact, according to a Gartner report, it is predicted that future AI models will be mostly trained by synthetic data by 2030.
This can be especially useful for machine learning engineers and data scientists.
For example, training datasets can be generated and fed into machine-learning models. This can be done through either free tools like ChatGPT or paid tools like Mostly AI or Gretel AI.
This makes it easier to test out different models and see how they perform with the generated data. This is especially useful if you’re in need of some training datasets that are harder to obtain, like various forms of media, including images and videos.
Another way that AI can help with data analysis is through automated data imputation. Automated imputation can help fill out datasets with missing values or outliers more accurately and quickly.
Explaining analysis & insights
In data analytics, explaining insights and diving deeper into the data is sometimes necessary to extract true business insight. That’s where an AI can help.
Using AI tools for data analysis like Tableau GPT, you can quickly explain a specific data point on a chart is behaving a certain way and provide deeper insights into it.
For example, you can ask straightforward questions such as:
- “Why did the sales decrease in March?”
- “What could be the cause of the spike in users?”
- “Which is our best revenue quarter for the year?”
The AI chatbot will then scan through your datasets to identify trends and correlations that could provide you with answers to your questions.
This function could also be used for other purposes, such as exploratory data analysis when encountering a new dataset or database in your data analytics project.
Automated data entry from images
Finally, for all the data analysts who frequently use Excel or other spreadsheets, AI can be used to automate the process of data entry from images. This is especially useful for collecting large amounts of data from images or documents, allowing you to quickly enter the necessary information without having to do it manually.
Tools like “insert data from picture” feature on Excel can help with this function. Using computer vision on the backend, it can convert images of tabular data into digital datasets that can save you a lot of time and effort.
This feature is especially useful for businesses in the healthcare industry that need to process large amounts of images, such as X-rays or MRIs. AI can help these businesses quickly extract and enter crucial information found in these images with accuracy and speed.
Creating dashboards & reports
Next, AI can be used to create interactive dashboards and reports.
For example, you can use an AI-driven tool like Tableau GPT to quickly aggregate data from multiple sources into a user-friendly dashboard or report.
You don’t even need to have any prior experience in the field of data visualization. All you need to do is select the data you’d like to include in the visualization, and the AI will automatically format it into a user-friendly chart or graph.
In addition, another unique way to use AI to assist with creating beautiful charts is to use the Midjourney AI to generate some eye-catching ideas for dashboards relevant to your analysis.
Future trends in AI and data analytics
AI and data analytics are closely intertwined, and the future of both will likely be one of growing integration. AI will become increasingly important for making sense of data – from finding patterns in data to generating insights from large datasets.
We can expect to see further development in automated report generation, as well as more use of NLP to enable prompting in a natural language to make data more accessible and understandable.
Additionally, AI will become an even bigger force in data security as the amount of personal data stored by companies grows. AI tools can identify suspicious behavior or patterns that could indicate malicious activity, helping to protect user data from cybercriminals.
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