AI and Data Analytics: How Machine Learning Is Shaping the Future of Business

Data analytics powered by machine learning can deliver powerful insights and deep analysis reports to decision-makers, helping you find quicker solutions to complex questions and potentially unleashing a competitive advantage in your industry.

But AI is not just a new buzzword. It’s been around for many decades, though in the past it was very technical and required significant investments in computational power. More recently, commercial computers have sufficient CPU capacity at reasonable prices, allowing new scientific disciplines like machine learning to flourish while making data analytics more mainstream.

Data analytics is a practice that encompasses a variety of AI and statistical techniques from data mining, machine learning, and predictive modeling that analyzes current and historical facts to produce insights or to make predictions about future or otherwise unknown events.

If configured with the right data management and analytics platforms and modeled by a team of data SMEs, including analysts, stewards, and data scientists, organizations can produce meaningful insights and amazingly accurate predictions. 

What is machine learning in the context of business analytics?

Machine learning (ML) in business analytics refers to the use of advanced algorithms and statistical models that enable systems to automatically learn from data, identify patterns, and make decisions with minimal human intervention. 

Unlike traditional analytics tools that rely on preset rules and human-led analysis, ML algorithms can adapt and improve over time as they are exposed to more data. This ability to continuously learn and refine makes machine learning a powerful tool for businesses seeking to leverage their data for deeper insights and more accurate predictions.

In business analytics, machine learning is used to automate data-driven tasks, uncover hidden trends, and generate predictive insights that help organizations optimize their operations, enhance decision-making, and drive growth. From customer behavior prediction to supply chain optimization, machine learning models are applied to a wide array of business challenges. For example, in finance, machine learning is used to detect fraudulent transactions, while in marketing, it can help identify the most promising customer segments and personalize campaigns at scale.

Machine learning also plays a crucial role in business intelligence (BI) tools. Many modern BI platforms now integrate ML capabilities, allowing data analysts and engineers to create predictive models directly within the analytics environment. These tools can automatically recommend actions based on past behavior, forecast future trends, and even suggest the best way to visualize the data.

By incorporating machine learning into business analytics, companies can go beyond traditional descriptive analysis and enter the realm of predictive and prescriptive analytics, where they can not only understand what has happened but also forecast future outcomes and prescribe actions to achieve desired results. This transition helps businesses stay competitive, innovate faster, and make more informed, data-driven decisions.

Real-world examples of AI and ML transforming businesses

AI and ML are revolutionizing industries across the board, driving innovation, improving efficiency, and uncovering new business opportunities. Below are a few real-world examples of how companies are leveraging AI and ML to transform their operations:

Fraud detection in financial services

One of the most impactful uses of AI and ML is in fraud detection within the financial sector. Banks and financial institutions are using machine learning algorithms to analyze massive datasets in real-time, identifying potentially fraudulent transactions and minimizing risk. By using historical data to detect anomalies, these models can prevent fraud before it happens, saving billions in potential losses. For example, JPMorgan Chase employs AI and ML technologies to monitor and detect fraudulent activity in its vast array of transactions.

Supply chain optimization in retail

Retail giants like Amazon are utilizing machine learning to optimize their supply chain and logistics. AI algorithms predict demand patterns based on customer purchasing behavior, seasonal trends, and inventory levels, ensuring products are stocked and delivered efficiently. This dynamic management helps reduce excess inventory and optimize warehouse operations, cutting costs while improving customer satisfaction. Amazon's use of ML in demand forecasting has been one of the major drivers behind their operational excellence.

Customer personalization in e-commerce

AI and machine learning are playing a key role in transforming customer experiences in e-commerce. Companies like Netflix and Spotify use ML algorithms to analyze user preferences and behavior, delivering personalized content recommendations. 

Netflix’s recommendation engine is a powerful (and effective) example of how ML can drive customer engagement.

These recommendation algorithms improve user engagement by constantly learning from each interaction to better predict what customers want to see next. Personalization through AI has been a critical factor in retaining users and growing revenue in these companies. In fact, in 2020, Netflix CEO Reed Hastings revealed that 80 percent of the content consumed on the platform comes from recommendations. 

Predictive maintenance in manufacturing

In the manufacturing industry, AI and ML are being applied to predictive maintenance, which helps reduce downtime and improve productivity. By analyzing sensor data from machines, AI models can predict when equipment is likely to fail, allowing companies to schedule maintenance before costly breakdowns occur. General Electric (GE) is a leader in this space, using machine learning to monitor and maintain industrial equipment across various industries.

These real-world examples highlight the growing impact of AI and machine learning in various industries. Whether it’s improving operational efficiency, enhancing customer experiences, or predicting future trends, businesses across sectors are leveraging AI and ML to stay competitive and drive innovation.

AI versus ML: Examples

The first step in building a successful data analytics strategy is selecting the right data management and analytics platforms for your organization. To accomplish this, it’s important to understand the different options available in the market and the role that AI and ML play in their functioning.

While AI is a computational representation of how the human brain works and learns, ML is a subset of AI with an array of algorithms that can be used to train models that learn from data and produce valuable outputs. 

As an example, linear or logistic regressions are basic ML algorithms that can be executed in a spreadsheet. They use mathematics to model relationships between variables and predict outcomes. While linear regression is used for predicting continuous variables, logistic regression can be applied to implement a binary decision tree.

Advanced analytics platforms typically allow users to design, model, train, and test models to generate insights or predictions. Among the most popular predictive modeling techniques are artificial neural networks, support vector machines, and K-nearest neighbor. These techniques can address both classification and regression-type prediction problems. They are often applied to complex prediction problems where other techniques are not capable of producing satisfactory results.

Neural networks are another group of ML methods widely utilized in various business sectors for tasks such as pattern recognition, forecasting, prediction, and classification. They play a crucial role in data mining and are commonly applied in finance, marketing, manufacturing, operations, and information systems, among others.

Model learning and training in AI and data analytics

In the context of AI and data analytics, learning is inseparable from training. Machine learning models, like classification methods, learn from historical data—inputs and outputs—to classify future cases. Neural networks have shown remarkable potential in various business tasks, such as forecasting and classification, due to their ability to learn from data and generalize across different scenarios.

At the core of all these data analytics techniques is the process of machine learning, which happens when human users train models with clean, reliable data. However, obtaining high-quality data can be as challenging as finding drinking water in a swamp. 

Data often comes from multiple sources with varying formats and standards, leading to integration challenges and inconsistencies. These issues can undermine the effectiveness of machine learning models, making data quality a crucial factor in the success of AI initiatives.

Although data inconsistency is just one of many challenges organizations face, it plays a pivotal role in determining the success of AI and machine learning models. 

Low-quality data impacts AI and machine learning models in the following ways:

• Poor Model Performance: Low-quality data may cause the model to learn incorrect patterns, leading to inaccurate predictions.

• Overfitting: Models trained on inconsistent or irrelevant data tend to overfit, meaning they perform well on training data but poorly on new, unseen data.

• Bias and Variance Issues: Low-quality data can introduce bias, skewing predictions, and increase variance, making the model overly sensitive to minor input changes.

• Reduced Generalization: Models trained on poor data often struggle to generalize to real-world scenarios, limiting their practical application.

• Increased Computational Costs: Cleaning and preprocessing low-quality data is time-consuming and computationally expensive. Models trained on poor data may also require more complex architectures to compensate for the shortcomings.

• Misleading Insights: Decisions based on models trained with low-quality data can lead to incorrect business or operational outcomes, risking significant negative impact.

It’s important to note that high-quality data does not necessarily mean that data is completely free from anomalies. According to Gartner, AI-ready data must be representative of the use case. Let’s use an example to illustrate what this means. 

Suppose you’re trying to build an AI model to detect fraudulent transactions. To train that model, you’d seek out financial transaction data that included fraudulent (or erroneous) charges. For the model to learn how to detect fraudulent or anomalous transactions, it must be trained on data that includes such charges. Sometimes, “bad” data is important to include for training, as it represents the use case.  

How can Alation help to ensure data quality for ML?

Alation, the market leader in data catalogs, provides a comprehensive platform to help organizations validate the reliability of their data at every stage of its lifecycle. The platform provides tools for data lineage, governance, and monitoring. One standout feature is the Data Health Tab, powered by Alation’s Open Data Quality Framework. This feature helps organizations track and improve data quality, ensuring that machine learning models are trained on trusted data, leading to more accurate predictions and better business outcomes.

Are you curious to learn how a data catalog can bring the power of AI and ML to your business? Book a demo with us today to learn more. 

References

• Sharda, Ramesh; Delen, Dursun; Turban, Efraim; Aronson, Janine; Liang, Ting Peng. Business Intelligence and Analytics (p. 247). Pearson Education. Kindle Edition. 

• Witten, Ian H., et al. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2017.