The use of AI in business analytics has enabled modern automation in data discovery processes. The outcome is AI analytics that does the data analyst's task without the same human constraints.
Recently, AI-driven analytics has advanced rapidly, driven by advances in AI technologies such as machine learning and natural language processing.
OpenAI, a top AI research company, has made major advances in the field with the introduction of GPT-3 and GPT-4, large language models capable of generating natural language text.
Today, generative AI and AI analytics have greater potential to augment employees, enabling business professionals and analysts to obtain better, quicker data insights whenever required.
What is AI Analytics?
AI analytics is the process of combining AI technologies, such as deep learning networks and ML algorithms, into business intelligence solutions to change how end users implement, inspect, and share their insights and data.
In short, AI automates intensive and time-consuming tasks. AI can quickly inspect every possible data combination to identify hierarchies of bonds between data points.
If the objective of the analysis is to know data so that companies can make decisions, artificial intelligence data analytics is similar to a data scientist's team working regularly, responding to inquiries on demand with unmatched depth and speed.
In addition to structured data origins, AI is progressively able to inspect unstructured data through AI analytics tools like computer vision, speech analytics, transcription, and natural language processing (NLP) for video and image analytics.
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How Does AI Analytics Work?
Due to significant advancements in computing power and the complexity of data platforms, today's businesses are deploying modern statistical models, decision trees, and rules to generate the latest insights, automate processes, and boost operational efficiency.
These modern models, decision trees, and rules are together called "artificial intelligence."
When AI is used to generate new insights —such as outliers, seasonality, anomalies, clustering, and time-series forecasting —it is collectively referred to as "AI analytics."
From a business user's perspective, these latest insights provide detailed insights into audience behavior, automate processes, and enable instant and effective reactions in dynamic market conditions.
Importance of AI Analytics
Consider an example of an online eCommerce or retail company that operates in line with dynamic market trends and offers its audience unlimited competing options.
Monitoring sales, audience satisfaction, or churn after the fact would be too late in this kind of situation.
By the time you identify the root causes and potential issues, it might be too late, and the audience might have moved on.
Retaining the audience is both a bottom-line and a top-line benefit because, for new revenue, audience-acquisition costs can be higher.
Almost all sectors face challenges associated with digital transformation, making it crucial for them to stay up to date with trends and causal factors through real-time, in-depth data analysis.
In this case, the potential of AI analytics can significantly impact the company's long-term survival.
The end goal of an AI analytics platform is to support data-powered decisions at the business level.
These platforms facilitate this process by converting business data into insights and visualizations, providing users with a starting point for proactive action.
The role of AI analytics in businesses includes:
- Drastically boosts the speed of analysis, allowing business owners to get their queries answered instantly while simultaneously reducing the volume of labor it requires to get them.
- Offers answers that are typically more focused and useful than a person might produce in the same period.
What are the Benefits of AI Analysis?
The ability of AI systems to inspect data autonomously has many business benefits. Let's see the other benefits:
Inventive products: AI tools for data analysis can lead to the creation of new products and the upgrading of existing ones.
Customer engagement: Companies use artificial intelligence analysis to understand what customers want—retain them, acquire them, and develop their loyalty.
Targeted marketing strategies: Create specified and more focused marketing strategies with analytics from present client purchases.
What are the Pillars of AI Analytics?
AI analytics relies on four major AI sub-tasks to offer insights: neural networks, deep learning, machine learning, and natural language processing.
These technologies work together to extract helpful information from diverse raw data.
Neural networks: These are algorithms that learn connections between data points through machine learning.
Inspired by the functionality of the human brain, they contain internally connected layers of neurons, or nodes, that remember and learn from data.
Consequently, the AI tool becomes increasingly wise and more perfect over time, making it more affordable.
Deep learning refers to modern neural networks that process complex, sensitive data using various layers of interconnected nodes.
Machine learning: AI analysis tools use machine learning to automatically discover data trends, which can then be integrated for actionable insights.
It helps process big data effectively to identify related data points for predictive analytics.
Natural language processing: NLP enables artificial intelligence analysis tools to understand and adapt to human language, allowing them to process and inspect it for helpful information.
Natural language processing is crucial for accurately analyzing blog posts, social media listening data, news articles, customer reviews, and other text-driven data, including emojis and numbers.
What are the Components of AI Analytics?
Familiarity with the business domain
Any number of complex AI models won't perform well if they are implemented with the wrong goals or used for the wrong business use cases.
Having a clear understanding of independent and dependent variables, along with sensible goals, is essential given the level and quality of data before the development of artificial intelligence analytics.
AI models and the required infrastructure
AI models are available in numerous forms, including standard machine learning models, neural networks, and deep learning models that provide recommendations and pattern identification.
A workstream must be produced in advance to combine and organize all necessary data through suitable data pipelines in a unique datastore.
Also, it is crucial to ensure that compute conditions and suitable cloud data stores are provisioned before initiating any production workstream or training.
AI analytics usage
When working on AI analytics projects, many companies make the mistake of disregarding the business owner.
How a final business user uses AI data analysis insights is a critical issue to address in advance.
One crucial element in this region is the NLP search for a typical business user to access data and all the associated modern AI data analytics insights.
The entire front end of analytics and traditional BI is evolving into an NLP-based search interface.
Inventive audio-visual stories combining AI-driven and traditional insights are one more potential avenue to explore.
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What is the Difference Between Traditional and AI Analytics?
Let's review the difference between traditional analytics and AI analytics:
Traditional analytics is usually handled by a technical group of data analysts.
The following instance describes how a group of data analysts might traditionally try to address a business issue:
- An event, trend, or incident occurs in an organization over a long duration — for instance, a decrease in sales for the quarter.
- Data analysts then create theories regarding the possible causes of the decrease in sales.
- Later, these theories are examined against the data for that duration until sufficient evidence is identified to support a specific theory.
- The data analysts then prepare a report summarizing their observations and will frequently present possible upcoming plans for the businesses to follow.
As you can see, the entire process, from the first change to determining the root cause, is time-consuming.
Apart from this, there's no guarantee that the responses identified by the analysts are correct, given their inherent limitations.
AI analytics, whereas ML algorithms continuously track and inspect large volumes of data. The outcomes differ from traditional data analytics in:
Scale
An AI-powered anomaly identification system learns the usual behavior of the data without being particularly taught what to look for.
It performs it at all levels: channels, products, and revenues per country.
Speed
The AI model will detect unexpected revenue declines and notify the relevant teams in real time.
Additionally, AI-powered analytics solutions use correlation and clustering algorithms to provide practical analysis, enabling immediate problem-solving.
This decreases rectification time by orders of magnitude because the inspection is conducted regularly and in real-time, rather than quarterly, monthly, or weekly at most, as with traditional data analytics.
AI-powered anomaly detection identifies anomalies quickly.
Accuracy
Based on ML algorithms, AI-based analytics can learn various patterns of typical behavior and identify relationships between anomalies in a way that is hard for an analyst to replicate.
The accuracy of ML algorithms depends on how they were developed; they must learn numerous patterns independently, which requires the use of various algorithms.
Let's summarize the differences between traditional analytics and AI analytics:
Traditional analytics
- Analysis needs human intervention to test its theories
- Reports on data commonalities, including trends
- Data scientists and/or data analysts use this
- Hours, days, or weeks — a lengthy process
AI analytics
- Machines can perform analysis and test countless combinations of theories
- Identifies reasons for data commonalities
- Allows business people, data scientists, and analysts to discover insights
- It takes only a few seconds
What is the Difference Between Augmented Analytics and AI Analytics?
The subject of AI analytics goes by many names, depending on the industry and customers.
The most commonly used term in the BI sector over the past few years has been augmented analytics, which refers to a suite of AI-enabled technologies combined into BI (business intelligence) and analytics solutions.
An end-user who uses BI tools and analytics with AI-driven features is referred to as an "augmented customer," a term commonly used in the analytics industry.
What are the Applications of AI Analytics?
Let's have a look at some applications of AI analytics:
Cloud expense analytics
AI analytics enables you to gain complete visibility into a business's cloud expenses, a task that is challenging without the program's support, given the continuously evolving nature of the cloud ecosystem.
That has become very important as companies increasingly report moving towards creating multi-cloud environments, as suggested by the 2023 State of the Cloud Report.
AI also streamlines the connection of cloud costs with organizational KPIs and offers personalized recommendations for cost reduction.
Fintech analytics
An instance of prescriptive analytics in the fintech sector is identifying and preventing security-related issues.
AI analytics can track operational metrics to close security gaps and take proactive security measures.
By consolidating all data sources into a single platform, machine learning can be used to understand the behavior of these metrics, identify anomalies, and prevent issues in real time.
Telco analytics
An instance of AI analytics in the telecommunications sector involves responding to queries such as "Are customers encountering issues related to roaming services?" "Is the network steady?"
AI can automatically answer these queries by detecting service quality issues that can minimize churn and boost ARPU.
eCommerce analytics
An instance of a diagnostic analytics issue from the Gartner Analytics Ascendancy model is responding to the query, "What are the factors impacting conversion rates?"
As various data points can affect conversion rates, this is the best application for AI analytics in e-commerce.
Additionally, it is an ongoing challenge for eCommerce businesses; having a solution that consistently verifies data enables you to identify issues early.
This can ultimately save the business enough money.
AI Analytics Use Cases in Business
Let's look at various use cases of AI analytics that almost all companies can benefit from:
Demand prediction
Predictive analytics is a prime example of this and one of the most renowned applications of AI.
According to McKinsey Digital research, AI-enabled prediction can decrease errors in supply chain networks by around 30–50%.
Predictive maintenance
This includes AI-driven techniques to forecast the condition of the organization's equipment and determine when required maintenance is needed.
Business tracking
From tracking customer experience to cost and revenue tracking, this is an instance of diagnostic analytics that almost every business can use to enhance client satisfaction, minimize churn, and enhance revenue.
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Why Do You Need AI Analytics Tools?
Customized client experience
AI in data analytics can conduct deeper analysis of consolidated data using complex models, allowing companies to identify potential areas for development in advance.
Predictive customer analytics
Predictive customer analytics algorithms can generate a large amount of data to make reliable predictions.
Behavior sentiment analysis
Companies use AI and analytics tools to study customer responses. They can study this data to understand customers' sentiments at organizational levels and retain them.
The Role of AI in Data Analytics in the Future
Today, data analytics algorithms are being influenced by AI, so you can expect AI-enabled machine learning to influence the field.
As more and more companies want to implement AI, they have to improve their present algorithms across their businesses.
This requires them to discover multiple ways to streamline their data collection and inspection processes.
Whereas the use of AI-enabled algorithms will impact the lives of professionals and data analysts.
Data analytics has been used to help companies make strategic decisions. AI can leverage real-time data analytics to make better, smarter decisions.
However, only a few companies have adopted AI, and most are still in the process of implementation.
For this, they have to prepare for a culture shift and organizational change.
This involves focusing more on adapting to their business structure, as AI will transform how data analytics is done.
Another way to ensure a business is ready for a culture shift is to examine the types of algorithms it plans to use, how they will be integrated into the business, how they will affect current employment, and what new roles are needed to focus on AI algorithms.
As AI develops and becomes capable of understanding a wide variety of skills, it will become an essential element of the data analytics process.
Moreover, technology alone cannot do everything, so humans will continue to be a part of the whole analytics process.
As said, AI can perform numerous tasks, but that doesn't mean it can replace humans altogether.
So, there will be a need for experienced data analysts to ensure their algorithms are used accurately and function properly.
Thus, data analytics will become more accurate, quicker, and more accessible than it is now.
Conclusion
AI analytics is a subset of BI where software exhibits human-like behaviors, including learning and reasoning, during data analysis. Businesses aim to better understand data before conducting analysis and then acting accordingly.
Machine learning algorithms used in AI analytics are powerful; they can classify large volumes of data that large companies collect and identify the key drivers that grow business.
