In a world where technologies such as machine learning and AI are informing business performance, our future leaders of industry need the ability to understand and interpret these complex sets of information. Now more than ever we are in the big data age, when organizations are compiling huge amounts of statistics about the business environment they are working in. Companies know that accurate data can provide the insights they need to stay ahead of their competitors and build on their successes moving forward.

A growing need for people who can harvest, refine, and interpret data

Today, business decisions relating to market trends, the launch of new products, and consumer behavior can all be informed by insights drawn from data. This has led to a growth in the need for people who can harness the power of datasets, retrieve actionable intelligence, and help organizations make informed decisions about their future. Therefore, business students who are planning a career in any type of industry, from marketing to finance, will need exceptional data analysis skills. This enables them to impress employers who have data analytics posts to fill, to stand out among the competition, and to deliver valuable insights once they are brought onto the team. It’s an especially dynamic, challenging role, and that’s why getting the best possible training is crucial. 

Creating study programs that blend practical experience with academic study

Top programs of study include practical tuition in the cutting-edge methodologies students will need when they move into the world of work and are expected to keep track of their company’s data. That’s why universities like St. Bonaventure have integrated advanced software tools into their learning schemes. Through working with data-driven insights, their students can learn to use evidence-based strategies to solve real-world problems and will develop a specialist skill set.  

As part of the online business analytics masters at St. Bonaventure University, students benefit from using innovative data analysis software to gain the competencies required to excel in a range of business situations. This remote course is open to bachelor’s degree graduates from any arts or science degree, and it offers them a way to gain an advanced qualification in just two years. Once they leave, students have a strong connection to the industries they will be working in and are ready to improve the decision-making of their next employer.

Which technologies do business analytics students engage with? 

Programs are taught using a range of cases and diverse course materials, as well as leading software tools in the field of big data. Students learn to manage IT systems, obtain findings from analytics, and understand the challenges of digitizing business procedures to measure progress. 

Using key performance indicators to monitor business performance 

Often abbreviated to KPIs, key performance indicators will be an invaluable source of information to graduates when they move into their first professional role. Tracked using analytics and reporting software, they represent a set of measurements that the company can use to see how successful a certain strategy has been. KPIs might be financial and related to the net income, the revenue, or the net profit performance of an organization. KPIs can also be based on operational metrics, so a business can view how its different departments are performing. 

Many KPIs are centered on customers, especially in terms of retention, service efficiency, and customer satisfaction. Students will learn to use KPIs as a way of establishing a business’s achievements, often in relation to those of other companies in the same field. To do this, they will learn to monitor and measure a range of factors, such as punctuality, quality, and productivity over time, with the intention of reaching a goal. In due course, they will be able to provide the information a company needs to adapt, implement new policies, and grow. 

How does predictive analytics answer questions for businesses? 

Students of analytics will learn how businesses use information gleaned from past events to make decisions about the future. Predictive analytics involves looking for patterns in the data, such as when equipment failures occurred, which types of customers responded best to a campaign, and whether people were willing to pay more for premium products. Once the data has been processed, the idea is to look for patterns that can be extracted, scrutinized, and eventually applied to new situations. As it is used to predict the best course of action, this form of analysis is often referred to as forecasting. 

In finance, statements from previous years can help to provide insights into how a business’s revenue and sales figures might look in the coming months. In marketing, historical behavioral data can help to allay uncertainty about when to launch a campaign. The data can predict sales trends month on month, which allows the team to create targeted ads that are more efficient. Companies that own large venues, can also use predictive analytics to gauge how many people may arrive at different times of the year and then plan accordingly to avoid being over- or under-staffed. Understanding the movement of customers is important for businesses that want to plan their staffing needs efficiently.

Understanding trends and outcomes with descriptive analytics

Business students need a robust understanding of descriptive analytics if they are to use statistics to interpret historical data. The information created by these tools can help companies interpret outcomes and events, as well as track relevant trends. It involves putting in place a series of metrics to measure a business’s goals against its performance. The resulting data will be analyzed to create reports on sales, workflow, and other metrics. These can help the management team understand how well a marketing campaign went, whether a training scheme is proving effective, and which social media platforms are driving the most traffic. 

Choosing the next move with prescriptive analytics

Businesses always want to choose a course of action that will offer optimal results, and MS students will learn how prescriptive analytics can assist with this. This form of analysis is designed to aid decision-making in the here and now rather than formulating long-term goals. The algorithms involved process huge amounts of data and answer statements based on “if” and “also” queries, depending on the business needs. They can be used to help weigh the risks involved in investment decisions, as machines have no cognitive biases, or to provide insight into conversions so companies know which of a customer’s interactions are most likely to result in a sale. 

Prescriptive analytics can be a useful tool on a business’s web pages, as they can amass data based on how individual customers have used the platform and then produce individualized recommendations the next time they visit. This leads to customers feeling better served by the company and higher rates of brand engagement, but it can also be used to enhance an ad campaign with personal content based on each person’s past browsing behavior. 

When it comes to banking, students will be taught how prescriptive analytics is essential in the detection of financial fraud. As institutions have to deal with such huge volumes of transactions, they are unable to monitor each one and therefore rely on an algorithm to spot anomalies in customer data. Once an issue is detected, the software notifies the bank and recommends what should be done next – for example, notifying the customer and putting a temporary hold on their funds. 

Presenting information in an accessible format using data visualization

As students grow and progress on their course, they will find ever more creative and accessible ways to present information to future colleagues and managers. Doing this in a visual format, such as an image or chart, rather than as a written report, is called data visualization. This can be an effective way of condensing large amounts of complex data, clearly showing the patterns that have emerged and highlighting the most useful insights. As a result, the decision-makers in the business understand where they should be focusing their efforts, be it customer service, marketing, or sales. 

As the sheer volume of data that is used to drive analytics is so vast, visualization makes it easier to recognize the formation of patterns and possible errors. As a result, a manager is better placed to identify areas of the operation that are making progress and those that are struggling. Moreover, many people find it easier to process information when it is presented in a visual format, compared to reading through an in-depth report. Therefore, managers and leaders are in a position to act quickly and decisively when new insights are presented to them, which can have a significant impact on business growth. 

Gain insight directly from consumers through text and sentiment analysis 

Consumers do not always have the time or inclination to fill in feedback forms, but now sentiment analysis can analyze huge numbers of conversations to identify opinions that might otherwise be missed. Specifically, the algorithm will search for expressions of neutral, negative, and positive sentiment using tools such as AI and natural language processing. Students will be instructed on how to use these because sentiment analysis is now a key driver of business intelligence and offers a range of benefits. It takes in reviews, social media comments, customer service chats, and more to build a comprehensive picture of consumers’ attitudes toward the brand and aspects of its service, such as a new product range. Using these findings, a business can make the changes required to enhance customer acquisition, improve satisfaction levels, and inform product development. 

Process vast amounts of information using machine learning algorithms

Although machine learning has been around for some time, students on an MS course will be taught how the big data era has brought it to the fore for business. The technology assists with sifting through masses of data and using the results to solve various problems. This could be in providing recommendations for browsers, producing relevant search engine results, or resolving issues for users through a chatbot interface. These and more machine-learning applications allow businesses to process the data they are constantly creating at greater speeds and can make timely, accurate decisions. Furthermore, companies can detect changes in the business landscape swiftly and be ready to respond to changes in customer demand. This gives them more control over stock levels and ensures they do not run out of popular items or become overstocked with less viable products. 

Searching for the details with cohort analysis

The more accurate a company’s data is, the better decisions it can make moving forward. Cohort analysis is a tool that can be used to discover nuance and context within a large amount of information. Business students will need to become familiar with this form of behavioral analytics because it doesn’t just provide answers about what people do and when – it can also offer an idea of why. Groups of consumers or users that share characteristics, such as their age or location, are referred to as cohorts. Using this technology, businesses can notice patterns of behavior in specific cohorts. This allows them to gain targeted insights that have more context. These give the company a better understanding of its clients, make it easier to segment customers into groups, and then create personalized marketing campaigns which can be far more effective. 

Equipping MS business analytics students with real-world skills 

As part of a postgraduate qualification, students gain the practical, evaluative aptitudes they will need as future industry leaders or freelance consultants. In the workplace, they will be ready to use analytics to influence decision-making, help develop key strategies, and monitor their company’s performance. From building forecasting and strategy models to gaining specialized knowledge of big data and operational management, universities will provide all the fundamental knowledge – soft and technical – that is required. At the heart of every good course is a dedication to industry involvement. This is supported by a focus on the use of analytics tools in a practical environment and by teaching students to solve the real problems that any industrial client may experience.