Data Analytics for Informed Business Decision Making

Many business professionals feel overwhelmed and stressed by decision making. According to a 2023 survey by Oracle, 74% of business leaders and employees report that the number of decisions they make daily has increased 10x over the past three years. Additionally, 85% of respondents said their inability to make decisions has reduced their quality of life.¹

One way to aid in the decision making process is to try to find actionable insights from the data that your company collects. The technical term for using raw data to generate insights is data analytics. Within data analytics, there is a subset that prioritizes using data for business success: business analytics. Business analytics focuses on the mass amounts of data that organizations collect and how to apply its value to business strategy. This approach uses mathematical and statistical models to understand the current state, uncover hidden patterns and predict future trends.

Data analytics can decrease stress and help leaders make better decisions because they have visible, proven evidence to back up their rationale. Many organizations apply data analytics to supply chain management, pricing solutions, consumer behavior and even employee relations. Since data collection can happen fairly quickly now, leaders can gather up-to-date information and execute on their decisions in a fraction of the time it used to take.

There are endless opportunities for how to use data analytics in your organization. This article explores techniques, tools and best practices to transform data into meaningful insights.

Before you can yield all the benefits of data analytics to make informed business decisions, you first must have a sufficient amount of data literacy. Below are the common methods that data scientists and analysts use in their work.

When presented with a problem, business analysts start by asking questions and finding relevant data. Organizations collect information from many channels, such as customer surveys, quarterly reports and social media platforms.² Choosing accurate and timely data sources can help analysts process information efficiently.

Once analysts have identified such data sources, they can use data mining tools and techniques to harvest information. Popular data mining software includes KNIME Analytics, Orange, RapidMiner and SAS. These platforms allow analysts to extract data from outside sources, process it into useable formats and share more easily among members.³

Big data refers to enormous datasets that can’t be stored or processed with traditional methods. This information has three defining characteristics: high volume, high velocity and great variety. These traits make big data challenging to manage and analyze.⁴

Analysts can use cloud computing resources to distribute big datasets across multiple servers. Additionally, frameworks like Apache Hadoop and Apache Spark enable organizations to parallel process large volumes of data across a distributed network. Many professionals also train machine learning algorithms to evaluate data quickly.⁴

Analysts typically start assessing information by conducting exploratory data analysis (EDA). This process uses statistical methods to identify patterns and outliers in datasets. EDA helps analysts understand data distributions and create hypotheses.⁵

Examples of EDA models include:⁵

• Histogram: Classifies data into categories and represents the frequency of observations in each category in a bar graph
• Scatterplot: Represents data points as dots on an x-y axis to explore correlations between an independent and dependent variable
• Probability plot: Compares the distribution of the actual dataset to a theoretical distribution

Data analysts can improve decision making by formulating and testing hypotheses.⁶ For example, an analyst might hypothesize that a new marketing strategy will increase customer engagement by 20% or that email personalization will boost sales.

Businesses often use A/B testing to conduct experiments and test hypotheses. This approach involves splitting participants into two groups and changing one variable. For example, one group might receive personalized marketing emails, while the other gets mass emails. Analysts then gather data to assess how the variable change affected the outcome.⁷

Rather than investigating data for past business operations, predictive models forecast future performance based on historical trends. These tools can improve decision making by enabling organizations to explore the potential outcomes of their choices.⁸

Predictive analytics has many applications across industries. Manufacturers can use predictive analytics to forecast sales and optimize supply chains. Similarly, food companies research and develop new products based on forecasted customer demands and market shifts.⁸

Many businesses use customer segmentation to learn more about their clients. This method separates customers into groups based on past purchasing behavior, demographics and other characteristics. Analysts collect and evaluate data on each segment to gain valuable insights.⁹

Businesses use customer analytics to target and personalize marketing.⁹ For example, a clothing company might discover that younger customers engage more with social media marketing, while older clients prefer email marketing. Based on this data, the business can use different marketing channels for each segment to enhance the customer experience.

Not everybody can look at a graph and understand what's being presented. Unlike data analysts, who might often be speaking to other people who solely work in data, business analysts often need to communicate their findings to decision makers who don’t have statistical backgrounds. Data visualization allows analysts to transform data into accessible graphics for broad audiences.¹⁰

But data visualization isn't one size fits all; analysts should choose tools based on their datasets and findings. Best practices are that tables display quantitative data in columns and rows, while bar charts compare categories. Bubble charts use colored circles to represent two or more variables.¹⁰

The most compelling data visualizations have simple designs, limited color palettes and clear annotations. They should be in clear, large fonts and not be overloaded with text. Analysts should also use storytelling techniques to provide context and explain significant findings.¹⁰

Business analysts can use machine learning techniques for data driven decision making. Tech professionals design machine learning algorithms and models that can identify trends in data and make predictions. Machine learning models process information faster than humans and area great tool for anyone working in data analytics, business analytics or data science.¹¹

Companies can use machine learning algorithms to forecast customer behavior and offer personalized product recommendations based on past purchases (e.g., "You Might Also Like" sections on websites). This approach can also help leaders make decisions about resource allocation and risk management.¹¹

With all the data at their fingertips, analysts are often asked to work with sensitive information and be extremely diligent about what they do and do not share. In fact, the International Institute of Business Analysis (IIBA) and other professional associations require members to follow strict ethical codes. For example, IIBA certificate holders must agree to respect intellectual property and maintain data confidentiality.¹² They might also be asked to submit background checks, use multi factor authentication and regularly audit their software.

Business intelligence (BI) technology helps organizations manage and process data. Analysts can store and analyze data using comprehensive BI platforms like Databricks, Microsoft BI and Snowflake. Additionally, Amazon offers many cloud-based solutions for easy scalability.¹³

Many companies have successfully incorporated business analytics into decision making. Every industry can benefit from the opportunity to get real-time feedback on their products and services and improve upon them. For example, Chipotle uses employee data to improve retention and offer equitable promotions and raises.¹⁴ Meanwhile, Kohl’s uses machine learning models to analyze customer data and provide personalized product recommendations.¹⁵

Businesses in every industry need data savvy individuals to help them make more strategic decisions. A data analyst's work can influence almost every part of a business' operations and save vast amounts of time and money, so their expertise is highly in demand.

If you want to get ahead in this lucrative field, you need a degree that shows you have the analytical skills needed to excel. The online MBA program at Yeshiva University is designed to help you learn the intricacies of data analytics and study the latest business intelligence techniques. As you complete coursework at your own pace, you'll deepen your knowledge of data mining, quantitative methods, data visualization and more. You’ll also build professional relationships with our expert faculty, alumni, and other business leaders.

Schedule a call with an admissions outreach advisor to determine if our program is the right fit for you.