Top Uses of Data Analytics for Business Success

Data analytics refers to the process of collecting, analyzing and interpreting raw data to uncover insights.¹ A 2022 survey by S&P Global reveals the extent of data analytics applications: 26% of organizations said they use some types of data analytics to gain insights and identify trends to shape nearly all their strategic decisions, while 44% said they use it for some decisions.²

This article explores how organizations utilize data analytics and analytics applications to inform data-driven decision-making and help ensure their success.

Data analytics can provide businesses with meaningful insights for informed decision-making. A methodical approach, beginning with exploratory data analysis and combining descriptive analytics with predictive analytics to generate recommendations, minimizes the risk of making decisions based solely on intuition.³ The use of data analytics can provide a clearer understanding of the potential impact of any business decision. It can also help pinpoint trends, identify patterns and uncover hidden opportunities.

For example, Providence St. Joseph Health, a 51-hospital healthcare system, provides detailed insights based on its data collection on quality and cost to identify best practices and reduce waste. This drives care quality improvements and enhances the experience for patients and providers. The practice was key to lowering care costs by $20 million in the first year across 10 clinical conditions.⁴

Data analytics can be used to analyze operations. It can help optimize processes by identifying operational inefficiencies and streamlining processes to boost productivity.⁵ This same approach to diagnostic analytics also helps business leaders identify unnecessary costs and make appropriate adjustments to economize.

Walmart, for example, leverages artificial intelligence and advanced analytics to increase efficiency. It monitors inventory levels, and forecasts demand across its distribution centers and suppliers network.⁶ This real-time data allows the company to ensure that the right products reach the right locations at the right time.

Data analytics helps gain insights into customer needs and preferences by analyzing their behavior and interactions with a brand. The insights can be used to tailor products, services and marketing efforts to meet customers’ specific needs and preferences. Personalized product recommendations drive sales, promote loyalty, enhance customer satisfaction and increase the chances of repeat business.⁷

Amazon provides an exceptional example of the success a business can have by focusing on customer needs and using data science to identify customer preferences. It leverages data to recommend products to individual customers by analyzing insights from their previous purchases and search behavior patterns. In 2013, McKinsey estimated that Amazon’s recommendation system influenced 35% of consumer purchases.⁸

Predictive analytics uses historical data to create models that forecast future events or trends. This enables businesses to forecast market shifts using statistical models to study patterns in historical data about customer behavior, industry developments and economic indicators. Analyzing these patterns enables companies to predict customer demand, anticipate potential churn (when customers stop doing business with an organization), and anticipate market trends, which helps them make more informed decisions about future strategies.

The Public Service Enterprise Group Inc. (PSEG) Long Island utility company utilizes weather forecasts and predictive analytics to anticipate the location and scope of potential power outages. This same data analysis process helps the company prepare crews and resources to minimize the risk of large-scale disruptions.⁹

Organizations face risks from internal factors, such as inefficient processes and external factors, like geopolitical shifts. Advanced analytics techniques like data mining and predictive modeling help uncover hidden patterns and correlations that may signal threats. Financial systems use algorithms to analyze data and detect unusual patterns or transactions that deviate from the norm, which may indicate fraudulent activities. Machine learning models are commonly employed to identify anomalies and take corrective actions.

Allstate Insurance uses data and statistical analysis of a driver’s age, gender and past driving history to assess their risk level and determine the optimal price for a policy.¹⁰ The company even created a new subsidiary (Arity) dedicated entirely to data analytics.

American Express uses a machine learning model that integrates various data sources, such as spending patterns, card membership details and merchant information. This system allows the company to track, identify and prevent fraudulent activity in real time.¹¹

Customer behavior analysis helps product teams measure how effectively products meet user expectations. It also helps businesses understand their audience’s preferences and predict future trends. These insights help businesses adjust their products and services to meet market demands.¹²

Market insights uncovered by data analysts may even allow a business to create new products specifically for an audience. Netflix accessed data from 30 million daily plays and user ratings, which it used to learn about viewer preferences. This data informed the development of the hit TV series House of Cards.¹³

Businesses can mine sales data and customer data to reveal potential new target markets for marketing campaigns. Data analytic techniques enable companies to gain insights into competitors’ pricing, performance, marketing strategies and social media presence. You can use these data insights to refine your marketing strategies and assess your marketing campaign’s effectiveness.

As part of its broader application of analytics, Starbucks uses data analytics to understand regional culinary preferences and individual customer preferences to offer menu items tailored to local tastes. Leveraging multi-layered customization helps Starbucks maintain a competitive edge over rivals and boosts customer satisfaction.¹⁴

Applying data analytics in compliance helps identify compliance trends and deviations from them.¹⁵ Data analytics techniques also aid in categorizing compliance risks and measuring compliance outcomes. Additionally, data analytics enables organizations to filter through digital noise, detect hidden threats and anticipate future data security attacks. It also allows faster incident detection and accurate threat assessment.

Professional services network Deloitte uses its Deloitte Compliance Monitor (DCM) as the core technology within its compliance infrastructure.¹⁶ DCM captures and consolidates compliance data, enabling oversight and management of Deloitte’s compliance requirements. This system enhances visibility into compliance across the organization, reduces the risk of fines and penalties, and strengthens the ability to detect, prevent, correct and respond to non-compliance events.

Data analytics continues to shape how organizations make decisions, optimize operations, and respond to market demands. From enhanced customer personalization to advanced risk management, organizations implementing data-driven strategies gain distinct advantages in their respective markets. As a data analyst, you can work across industries to help teams uncover patterns in customer behavior, forecast market trends, and develop data-backed solutions for complex business challenges.

The Online Master of Science in Business Analytics at Santa Clara University's Leavey School of Business prepares professionals to lead data-driven initiatives and generate meaningful insights from complex datasets. Build your expertise with a curriculum covering data mining, database management, machine learning, business analytics, cloud computing and data visualization. Connect with an admissions outreach advisor to learn how this program aligns with your professional goals in the growing field of data science.