International University in Geneva
In this modern, high-paced, data-driven world, business analysts assist organizations in turning current and historical data into big ideas. Artificial intelligence, machine learning, and management science are enabling business decisions. At International University in Geneva you will gain data-driven insights and business optimization using descriptive, predictive, and prescriptive analytics.

Our MSc Business Analytics course will turn you into a confident lateral thinking person, fully up to date with the latest business theories and practices. While getting input from inspiring industry experts, we will also make sure that we immerse you in the business challenges facing today's business world.

Business Analytics: when data generates value for business

Business Analytics is a concept that defines the management of an organization's data, with an emphasis on statistical analysis.
It involves the use of advanced technologies and methods to analyze information from a wide variety of sources and in large volumes.

Benefits for a company thanks to Business Analytics

  • Present information in a structured and coherent way
  • Analyze the company's data
  • Facilitate decision making with relevant indicators
  • Consolidate all data, purchases, sales, accounting, customers, etc.
  • Automate the decision-making process by using the same indicators for the entire company
  • Improve visibility on figures, discrepancies, anomalies
  • Anticipate business problems and forecast trends

As you can see, the benefits of data analytics for a company are wide and represent an invaluable source of insights on which to rely to make strategic decisions and envision the future with confidence.

With effective data visualization, Business Analyst can translate large data sets and metrics into charts, graphs and other elements to make data easier to represent and undestand, and share real time insights, trends and outliers.

Let's take an in-depth look at the B.A. approach to help you better understand this crucial area for businesses today.

How to use Analytics in Business ?

The main techniques of Business Analytics

Different analysis methods are available to define a Business Analytics strategy, here are the 3 main ones :

- Descriptive analytics: tracks key performance indicators to understand the current state of a business; and business problems

- Predictive analytics: which analyzes trend data to assess the probability of future results.

- Prescriptive analytics: uses past performance to generate recommendations on how to deal with similar situations in the future.

For more details on the Business analytics techniques, click here...

What is the scope of Business Analytics?

7 steps of a good Business Analytics strategy

Now let's see what the seven basic steps of a good Business Analytics strategy are. Actually, many business analytics tools and solutions will help the professional to determine the best approach and prepare the best recommendations based on his analysis.

1.         Study and define business needs

The first step in the business analytics process involves understanding what the business would like to enhance or the problem it wants to fix.
The relevant data to solve these objectives is decided by the stakeholders, the users with knowledge of the processes and the analyst(s).
In this phase, key questions such as "what is the available data", "how can we use it", "do we have enough data" must be answered.

2.         Macro Data Mining

This step is about cleaning up the data and doing calculations for lost data, removing outliers, and transforming combinations of variables to create brand new variables.
This is where a specific tool can already be used.
Time series graphs are plotted, showing norms or disparate values.
In this step, the removal of disparate values from the dataset is a crucial task, because disparate values usually affect the accuracy of the model if they can remain in the dataset.
With clean data, the analyst will understand it better. He or she will trace the data using scatter diagrams to detect possible interrelationships or misalignments. He or she will visually check all potential data ranges and synthesize the data using proper visualization and descriptive statistics that will help stakeholders gain a core comprehension.

3.         Data Analytics

Using statistical analysis techniques, such as correlation analysis and hypothesis testing, the analyst will identify all factors associated with a target dynamic.
He or she will also conduct a simple regression type analysis to find out if straightforward predictions can be made.
In addition, various groupings are being compared with different scenarios and these are further tested with hypothesis testing.

4.         Predict what is likely to happen

Business analysis is about being proactive in making decisions, this is called predictive analytics. The analyst moderates data using predictive techniques, such as decision trees, neural networks and logistic regression.
These techniques reveal new ideas and models that uncover relationships and "hidden evidence" of the most influencing variables. The analyst then proceeds to compare the predictive values to the actual values and computes the predictive errors.
Typically, multiple predictive models are executed, and the best scoring model is selected based on its precision and performance.

5.         Search for the best solution

The analyst will apply the coefficients and results of the predictive model to run hypothetical scenarios. The analyst will use manager-defined objectives to determine the best solution, with restrictions and limitations provided.
The analyst will select the ideal solution and model based on least error, business objects, and intuitive recognition of the model coefficients most aligned with the organization's strategic objective.

6.         Decision Making and Outcome Measurement

The analyst will make decisions based on insights derived from the model and organizational objectives.
The action taken will be measured after a pre-determined period of time.

7.         Update the system with the results of the decision

Ultimately, the decision and action results, as well as new learnings from the model are stored in the database.
Information such as " did the decision and action work?", " did the treatment group compare to the control group?" and " what was the ROI?" are provided. The outcome is an ever-evolving database that is continually updated with new insights and knowledge.

Jobs and opportunities for graduates in Business Analytics

The industries most involved in Business Intelligence are the high-tech sectors and they do offer most of the business analytics related jobs : Aeronautics, Automotive, Telecommunications Automation, Robotics, Energy, Laboratories, Banking, Insurance, Applications and Services IT, Mass Distribution.

Master of Science in Business Analytics courses prepare students to become quantitative analysts in companies, or even in public organizations or administrations.

They will most often work in statistics departments (forecasting, data auditing, datamining) or in financial analysis (risk, actuarial). Some will be involved in the development of econometric models in areas ranging from demand analysis to market risk or credit risk assessment models. Data science and business analytics present a real opportunity in a growing digital world. 

Specific positions include Data scientists, Data miners, Business intelligence project managers, Specialized software tool designers, Research and development engineers, Expert business intelligence consultants, Researcher in the areas of expertise of artificial learning, massive data mining, business intelligence.

A few words to put Business Analytics in a nutshell

For a business analytics strategy to be successful, you need to:

- ensure data quality

- having qualified analysts who understand the technology and the business

- Establish an organizational trade-off with information-driven decision making

Before this can happen, a "Data-Driven culture", or DNA, as the experts in the field like to say, must be developed.

Data-driven decision is defined as the practice of using data in the most diverse business processes. Systematically and continuously - it is possible to generate data-driven management.

Entrepreneurs with a strong Data-Driven culture establish processes and operations to facilitate the acquisition of necessary information for employees. They are also transparent about access restrictions and governance methods. And they are therefore better prepared to create and execute a business analytics strategy.

The most innovative companies have a Business Analytics strategy (see BA examples here) as Business Analytics provide insights that inform business decision-making and can be used to automate and optimize processes.

Feel ready to join International University in Geneva, get a Business Analytics degree and bring things further ?

Discover more about our program