Big Data Analytics: Your Path to Data-Driven Decision-Making

Data has become an integral but often unseen part of our daily lives. You generate and share data whenever you use mobile applications, post photos on social media, or make purchases online. Companies collect and analyze this data for many reasons. For instance, Starbucks uses its mobile app to gather data about customers’ orders and provide personalized coupons for their favorite drinks. Spotify analyzes customers’ listening histories to identify artists’ top fans and offer exclusive merchandise. Both kinds of tactics encourage customers to engage more with the business.¹

Our growing reliance on technology allows companies to gather this data and much more from thousands or millions of sources. Experts refer to this metaphorical deluge of information as “big data.”² Vast quantities of information can give companies new insights into their operations, but harnessing the power of big data poses many challenges. This article provides a big data overview and considers its applications, benefits, and trends.

The U.S. Census Bureau defines big data as “data sources that are fast-changing, large in both size and breadth of information, and come from sources other than surveys. Big data examples include retail and payroll transactions, satellite images, and ‘smart’ devices.” This vast wealth of information is typically gathered passively instead of being actively generated through experiments or interviews.³

Big data has three defining characteristics, which researchers often refer to as the “3 Vs”:²

• Volume: Consists of vast amounts of data that humans can’t interpret with traditional methods 
• Velocity: Grows exponentially, requiring high-speed, real-time processing
• Variety: Comes from a broad range of sources and in many formats 

Other traits frequently associated with big data include complexity, value, and variability.²

Organizations collect big data from a wide variety of devices and sources, such as digital photos, electronic health records, email, GPS signals, machine sensors, online transactions, social media posts, weather reports, website traffic, and videos.²

Business analysts use many big data technologies to manage and analyze information. Popular tools include:⁴

• Hadoop, a framework which allows users to process and share vast data sets
• Spark, an open-source system which lets analysts collect and process data from multiple sources
• NoSQL, database management systems which allow users to store, manage and query unstructured data models
• Cloud computing, which allows big data storage on remote servers for greater accessibility and flexibility

Business analysts use many techniques to process information and extract valuable insights from raw data. Professionals often start by using data mining software to identify patterns and trends in enormous data sets.⁵

Machine learning models provide additional insights by using algorithms to detect patterns in historical data and forecast future outcomes.⁵ For instance, an analyst could train a machine learning model to analyze customers’ credit scores and predict the likelihood that they’ll purchase a car in the next three years. Businesses can use data analytics for targeted advertising, personalized products and more.

Organizations increasingly use big data insights to make informed decisions and mitigate risk. Analyzing vast data sets can reveal patterns and trends that might not be evident in smaller sample sizes. This information can help companies identify growth opportunities and improve strategic planning. For example, researchers have used data analytics to investigate how public transportation affects hospital accessibility and propose strategies to reduce inequities.⁶

Companies in numerous industries use big data technologies to improve their performance. These applications highlight the versatility and value of these tools:⁷

• In healthcare, wearable biosensors collect health data from patients for predictive diagnostics 
• In marketing, big data from search engines, mobile apps, and other sources enable companies to analyze consumer behavior and generate targeted product recommendations
• In engineering, smart sensors placed in bridges and roads enable engineers to monitor structural stability

Historically, organizations used spreadsheets or paper records to manage data. However, big data is too vast and complex for these tools to be effective or time-efficient. Instead, business analysts use various big data management strategies, such as employing machine learning algorithms to detect errors and inconsistencies in data sets, storing big data in scalable cloud platforms, using automated data preparation tools to clean data, and speeding up data processing with distributed systems, such as Hadoop and Spark.⁸

Business analysts must navigate many big data challenges. For instance, big data requires innovative storage methods and data infrastructure. It must be cleaned before processing, which can be time-consuming and which raises ethical and privacy concerns, and it often needs to be analyzed in real time to deliver value.⁹

Big data analytics offers many advantages for companies of all sizes and industries. Organizations can leverage big data insights to provide personalized customer service and improve client engagement.¹⁰ These findings also enable companies to boost performance and increase innovation.¹¹

Recent technological advancements have led to new developments in data analytics. Bioinspired computing enables users to scale big data processing and increase security. Additionally, artificial intelligence and machine learning can improve the accuracy and speed of data analytics.¹²

Immense data sets require scalable data infrastructure analysts can use to extract insights. Many organizations have turned to cloud computing solutions to meet these demands. Cloud architectures can scale to the volume of big data and process information quickly.¹³

Predictive analytics in big data uses past and current data to anticipate future trends. These models help businesses make strategic decisions and assess investment risks. For example, energy companies use predictive analytics to predict how weather events may affect service costs. Law enforcement agencies can use these models to analyze crime data and identify areas that need additional patrols.¹⁴

Learn how to harness big data analytics to transform organizations and tackle business challenges. The Online Master of Science in Business Analytics (MSBA) program from the Leavey School of Business at Santa Clara University hones the skills you need to reach your professional goals. Through a robust curriculum led by acclaimed industry veterans, you’ll develop fine-tuned expertise in database management, cloud computing, machine learning, and more.

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