As a data strategist, you are the champion of change at your organization—driving business value, breaking down silos, establishing a data-driven culture, and so much more. But these efforts can fall flat if you don’t have the right technologies and tools. In fact, Accenture research shows that 88% of companies struggle to turn data into insights due to a lack of mature data capabilities.
AI-powered technologies such as machine learning and predictive analytics are revolutionizing how data leaders handle data, moving beyond static dashboards into a new era—The Data Renaissance. And that’s just the beginning.
Let’s explore how these technologies can improve the way businesses make decisions and discuss their distinctions so you, too, can put data at the center of your organization’s decision-making.
Table of contents:
Machine learning is a subset of artificial intelligence (AI) that uses big data and statistical modeling to create self-learning models capable of making decisions. Unlike traditional programming, which often depends on explicit instructions and rigid coding, ML introduces smart, adaptive models that autonomously learn rules and patterns from data without human intervention.
The scope of use cases for ML is vast and constantly expanding, allowing you to automate routine data analysis tasks and apply intelligent algorithms to uncover relationships and patterns within data that were previously unknown.
With the rate of data generation accelerating every day, analyzing and interpreting it all would be extremely difficult and time-consuming without machine learning. By deploying ML algorithms, you can streamline your data-handling process and enhance the overall efficiency of data-driven decision-making. To give you a glimpse of how data leaders are using this technology to drive their businesses forward, we’ve listed some popular use cases across different industries:
1. Offering personalized recommendations
Ever wonder how streaming giants like Netflix know exactly what you might want to watch next? Behind the scenes, advanced ML algorithms analyze your viewing history, preferences, and search patterns to offer personalized watch recommendations. Here’s what Elizabeth Stone, VP of Data & Insights at Netflix, told us on an episode of “The Data Chief”:
“We use analytics, experimentation, causal inference, machine learning, data engineering, and consumer-facing research to make informed decisions about the product experience and the content offering. And there's a lot of different flavors of data work that goes into that machine learning, experimentation, analytics.”
Beyond recommendations, these advanced models also can predict customer behavior based on past interactions, such as identifying customers at risk of churn. Such insights can help marketers build these highly targeted, customer segmentations, leading to more effective promotions and enhanced overall customer satisfaction.
2. Detecting fraudulent transactions
Fraud can dramatically impact banks and financial institutions, eroding customer trust and eating into revenue. By analyzing transaction data and identifying patterns indicative of fraudulent activity, ML algorithms can help businesses implement appropriate measures to protect against cyberattacks and financial losses. These AI-powered fraud detection systems are even more powerful when combined with a business intelligence (BI) platform. This consolidation of capabilities allows risk managers and credit risk teams to explore data, visualize findings, and share relevant information with other teams.
For instance, ThoughtSpot’s interactive Liveboards allow users to add notes, highlight critical data points, or share insights across the organization. These live dashboards give real-time insights into your data, allowing you to quickly uncover hidden causes and deliver engaging data stories to stakeholders.
3. Enhancing patient outcomes
Advanced data modeling techniques and machine learning are helping healthcare professionals identify patterns and correlations in their data, leading to more accurate patient diagnoses. This information is crucial for improving patient care, resource allocation, and overall healthcare management.
Consider the example below. By leveraging ThoughtSpot—an augmented analytics solution—healthcare professionals can drill into specific areas or use SpotIQ to gain automated insights into readmission rates. This analysis will enable them to assess the efficiency of their care program and create positive outcomes for consumers.
Predictive analytics is an advanced data analytics technique that forecasts outcomes, finds patterns, and identifies risks or growth opportunities. This type of data analytics uses statistical modeling, data mining techniques, and machine learning to analyze large datasets and predict the likelihood of an event occurring.
Just as a GPS combines your current location with historical traffic data to suggest the optimal route, predictive analytics uses past data and advanced modeling to anticipate future business outcomes. But that’s not all. Let’s dive into how predictive analytics is used across a wide range of industries and roles:
1. Optimizing inventory management
To maximize profitability, retailers should have a firm understanding of where their inventory is, who wants to buy it, and where it needs to go. With big data and AI, retailers can create demand forecasts to identify seasonality patterns and predict future demand for a product or service. Armed with such insights, they can make more accurate predictions about what their customers might need in the future, reduce lead times, and adjust prices and offerings accordingly.
See how Canadian Tire was able to use ThoughtSpot to forecast demand, growing sales by 20% despite 40% of stores closing during the pandemic.
2. Scrutinizing insurance policy applications
With the emergence of AI-powered analytics, insurance companies are discovering new ways to optimize policy pricing and streamline the claims process. Yet, managing risks, particularly in managing policy applications and underwriting, remains a challenge.
Fortunately, predictive analytics offers a powerful solution. By evaluating the characteristics and profiles of policyholders, these advanced models can accurately forecast the likelihood of future claims and even highlight potential risks that might have otherwise slipped by. This in-depth analysis guides insurers in making data-driven decisions regarding coverage terms, premiums, and contingencies.
3. Calculating future returns on investment portfolios
Wealth managers and financial analysts use predictive models to assess which assets are poised for future growth, allowing for more strategic resource allocation. These projections answer critical questions, such as which assets can minimize risk, how evolving market conditions might affect portfolios, and what strategic adjustments are needed to achieve long-term financial goals.
AI-powered features such as ThoughtSpot’s natural language search allow investors to effortlessly query portfolio data, create forecasts, and receive immediate insights. From tracking realized and unrealized gains from the sale of investments to modeling future financial scenarios, ThoughtSpot helps investors assess the impact of their overall investment strategy.
1. Data approach
While both machine learning and predictive analytics rely on data to perform optimally, the latter primarily uses structured data to forecast future outcomes. These models leverage historical data from various sources to simulate scenarios, providing objective forecasts that offer deeper insights and context.
On the flip side, machine learning models work with structured and unstructured data to automate tasks, identify trends, make recommendations, and more. They also factor in real-time market data, customer data, and ongoing interactions to analyze large amounts of data, learn from the insights, and then make informed decisions. The logic of machine learning models is simple—the greater the exposure to high-quality data, the more accurate and refined these models become.
2. Autonomous vs collaborative approach
Machine learning gives computers the ability to learn from data without the need for explicit programming. It functions as an autonomous system, teaching a machine how to perform a specific task, much like mimicking human behavior.
Predictive models, on the other hand, take a more collaborative and structured approach. Creating such models typically requires human input to select relevant data, define parameters, and fine-tune algorithms to forecast specific outcomes.
3. Machine learning has a broader application
Machine learning and predictive analytics are subsets of AI, however, machine learning has a broader scope. It encompasses predictive modeling and extends to advanced techniques such as deep learning and neural networks for classification, clustering, and anomaly detection.
In contrast, predictive analytics is more focused on anticipating the likelihood of an event occurring. It relies on predefined data models and has limited flexibility when it comes to adapting to new data.
💡Note: While ML techniques can be used to perform predictive analysis, not all ML techniques are suited for it. Similarly, not all predictive analysis relies on ML-based methods. Ultimately it depends on the use case.
The decision to choose between machine learning and predictive analytics ultimately depends on your specific use case. If your focus is on gaining the answer to the question ‘What will happen next?’, predictive analytics should be your go-to approach. However, it alone does not provide the necessary details for making an informed, in-the-moment decision.
To ensure you’re making the most of your data, it’s crucial to choose an AI-powered analytics solution that combines the capabilities of both approaches—one that not only has advanced machine learning capabilities, but also supports self-service analytics, empowering users to interact with data, create forecasts, and gain personalized insights. This integrated approach would position your business to drive innovation and make strategic decisions effectively in a dynamic environment.
In today’s data-driven, competitive business landscape, it’s no longer enough to know what has happened—you need personalized, real-time data experiences to figure out what is happening and what will happen next.
With ThoughtSpot’s unique, AI-powered search experience, uncovering hidden insights and predicting outcomes is effortless and intuitive. Our augmented analytics approach puts users in the driving seat, empowering them to personalize insights, perform root-cause analysis, and even set automated alerts to stay on top of KPI changes—all without the need for any specialized training or technical know-how.
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