Mastering the Art of Data-Driven Business: A Guide to Business Intelligence and Analytics

The term ‘data analysis’ may conjure images of scientists or finance professionals poring over spreadsheets. However, most industries increasingly depend on data analytics to drive decision-making and improve workflows. For example, the grocery chain Kroger created a mobile application that tracks inventory and prioritizes staff activities to restock stores efficiently.¹ Likewise, PepsiCo uses Bluetooth-enabled sensors to monitor the freshness of food during transportation and optimize the supply chain to reduce waste.²

Organizations’ growing reliance on data has led to a surge in demand for business intelligence specialists. The U.S. Bureau of Labor Statistics predicts that the number of jobs for management analysts will increase by 10% between 2022 and 2032.³ These positions often require knowledge of data analytics concepts and methods.

Read on to discover how to master data-driven business decision-making and optimize performance.

According to a 2023 Discovery Analytics article, business intelligence “integrates products, technology, and processes to arrange the crucial data that management should use to boost the performance and revenues of the company.”⁴ This approach allows leaders to identify the most critical information in enormous datasets to derive insights and make strategic decisions.⁴

Business intelligence (BI) involves data collection, management, analysis, and visualization. These steps require analysts to identify relevant information, detect trends, and translate their findings into accessible graphic representations.⁴

Organizations implementing business intelligence systems can anticipate market changes, streamline operations, and improve performance. These benefits can help them gain competitive advantage over other companies.⁴

Data analytics has numerous applications in business. Many organizations use predictive analytics to anticipate future events or trends, such as customer preferences and staff scheduling needs. They also use descriptive analytics to understand past information such as historical sales trends.⁵

Additionally, many business leaders leverage machine learning for new insights. Machine learning enables business intelligence analysts to uncover hidden patterns that humans may not detect when using traditional techniques.⁵ This branch of artificial intelligence uses algorithms to interpret data and make decisions without human input.

Organizations must source data effectively to gain accurate and valuable insights from it. This process starts by identifying relevant information. Analytics professionals gather data from many sources, including website traffic, sales records, and social media platforms. The most effective data mining techniques involve asking specific questions and looking for datasets that can provide answers. For instance, if you want to learn about customer preferences, you can focus your data collection efforts on product reviews and surveys.⁶

Next, data management involves consolidating, processing, and storing data. Businesses typically protect data from breaches by implementing security measures, such as encryption and firewalls.⁷

Data visualization involves presenting data or abstract concepts in the form of accessible graphics. This process improves decision-making by allowing users to interact with datasets, detect patterns, and infer connections between data points.⁸

Analysts utilize many types of data visualizations, including:⁸

• Trees, which represent data hierarchically from top to bottom
• Tables, which present data organized into rows and columns
• Diagrams, which use lines to illustrate the flow of information and connections between ideas
• Pie charts, which represent values as percentages of a whole

They also use interactive dashboards to gather information into centralized web pages. These reporting tools display data in bar graphs, pie charts, and other visualizations. It’s essential to design dashboards with responsive features, such as filters and drill-down options, that allow users to explore data. Analysts also use colors, fonts, type size, and other presentational elements to create visual hierarchies and highlight significant information.⁹

Descriptive analytics is a valuable tool that relies on historical data to provide insights into the factors that influenced past events. Organizations use this approach to identify trends and learn from previous mistakes or successes. For example, you can use descriptive analytics to investigate a sudden drop in sales or detect the root causes of medication errors in a hospital.¹⁰

By using descriptive analytics, data scientists can track key performance indicators (KPIs) and measure progress toward goals.¹⁰ Say you want to increase customer engagement. You can use descriptive analytics to evaluate social media engagement, new email subscribers, and other relevant KPIs.

As another essential tool, predictive analytics uses historical data to forecast high-probability events or trends. Retailers often use this method to predict customers’ future preferences based on their past purchases and browsing history. Companies can also use predictive analytics to assess risk, predict market fluctuations, and schedule preventative maintenance for equipment.⁸

Among BI tools, prescriptive analytics focuses on recommending the most strategic business decisions. This approach uses Big Data and machine learning to synthesize descriptive and predictive analytics and offer actionable insights.⁸ Additionally, prescriptive analytics uses algorithms to create optimization models that consider the potential outcomes of various scenarios and recommend the best decisions.¹¹

The insights gained through these various forms of analytics can inform data-driven decision-making—that is, business choices that are rooted in fact-based probabilities rather than intuition or gut hunch. However, businesses often struggle to foster a data-driven culture. Leaders may encounter resistance from colleagues who don’t understand analytics. It’s also challenging to keep up with the exponential growth of data and use this information effectively.¹²

Organizations can overcome these obstacles by embracing iterative learning (learning from past efforts/successes/mistakes) instead of dwelling on failures. Additionally, they can do well to focus on the long-term benefits of data analysis and strive for slow but steady growth.¹²

Data analysis provides meaningful insights, but businesses must use information ethically and legally. Data privacy is one of the most significant ethical considerations. Organizations should allow clients to retain control of their information as much as possible and only share it with authorized sources. Additionally, companies can protect their clients and employees from data security threats by encrypting sensitive information and implementing access controls.¹³

Discover emerging trends in business intelligence in the Online Master of Science in Business Analytics program from the Leavey School of Business at Santa Clara University. Through the innovative curriculum, you’ll gain expertise in the latest industry developments and technologies. Led by seasoned Silicon Valley insiders, this flexible online program provides dedicated faculty mentorship and networking opportunities with industry veterans. You’ll make new connections and unlock valuable career information—all with the freedom to study from anywhere and continue in your current job as you complete your degree.

Don’t wait. Schedule a call with an admissions outreach advisor today.