What are the differences between Business Intelligence for Business Analysis and Data Science?

In a world driven by data analysis, most people would assume that Business Intelligence and Data Science are the same, or even interchange the names when referring to data management, however they are very different concepts even if some of their roles might overlap.

In order for businesses to make informed and responsible decisions, they need to look at analysis and data of the current situation. For this, they rely on business analysts who know how to interpret, read and present business data in a way that will help executives to make these decisions. According to the analytics platform Tableau, “In practice, you know you’ve got modern business intelligence when you have a comprehensive view of your organization’s data and use that data to drive change, eliminate inefficiencies, and quickly adapt to market or supply changes.”

Business analysis involves understanding market trends and helping decision-makers find ways to realize strategic operational and tactical goals as they relate to culture, capital, costs, customers, compliance, competition, and competence.

With the program that we teach at ERP College, students will learn the systems needed to understand complex business information. These systems include a relational database management system (RDBM) and SAP business intelligence, which form the basis for business information organization and reporting. Furthermore, students will learn the applications of SQL Server, UDT (Universal Design Tools), IDT (Information Design Tool), WEBI Rich client tool, along with Microsoft Access. Students will also learn how to convert their business information into visual aids for reporting and communication purposes, this concept is called data visualization.

Working with an SAP environment provides hands-on experience with cutting-edge tools and software that can be used to make the most of the data that is available. The ERP program also oversees databases, how to lead intelligence analysis, analyze data for business decisions, and report on their insights. The classes include, but are not limited to:

Relational and Analytical Data Source

Relational tables are central to the way that business intelligence analysts organize and interpret most data. The better relational and analytical data sources can be handled, the more insights can be drawn from the data. Practice will enable understanding relational and analytical data sources and their importance at a high level.

Dimensional Data Modelling

As business intelligence analysts deal with exceptionally large data sets or multiple data sets, scaling up their organizational methods will push them to get creative. Dimensional data modeling is a data structure technique optimized for data warehousing tools. The program enables students to refine their capacity to model dimensional data in order to make large data sets useful to their employers.

Structure Query Language (SQL)

Across almost all web applications, SQL remains the gold standard for data organization. This is also true for the majority of data present in a business setting, and students will be well versed at choosing the right structured query language for every project and then using it properly.

SAP BO Business Intelligence

A framework for business intelligence, SAP BO Business Intelligence 4x represents the most cutting-edge technology available to business analysts. Once students know how to navigate this framework, they will take their business analysis skills to a new level.

SAP Lumira

In order for business data to be communicated, it needs to be reported clearly. This largely relates to how well they convert their data visually, for which SAP Lumira is particularly well-suited. In the program, all the ins and outs of SAP Lumira are taught, including when and why to use it.

Hands-on Project

As part of our Business Analysis for Business Intelligence program, students will apply the learned skills at a practicum, where they will be placed

On the other hand, a data scientist uses machine learning algorithms to create a model from data obtained, to help that business work in an effective way. Different from business analysts, they work with unstructured and structured data, and they need to be able to handle both and translate it, so a company can ultimately reach the right decision, and move forward. Data scientists perform advanced technical analysis to understand complex systems and make related forecasts about them. This is done by using scientific and mathematical methods such as statistics, mathematics, and computer science with the main mission of extracting useful patterns and insights from data. The results are demonstrated using statistical models, visualizations, and product data.

According to the data scientist Matt Przybyla, some of the skills the role needs to have are:

  • Python, R, SAS, and SQL (or some form of data wrangling)
  • Jupyter Notebook
  • Object-oriented programming (OOP)
  • Machine Learning algorithms
  • Problem-solving
  • Understanding the business
  • Making sense of data
  • Communication of business problem to stakeholders
  • Communication of possible solution
  • Communication of results and impact

At ERP College, the Data Science program will introduce students to real-life scenarios and with easy to follow python examples. They will also understand the most common problems businesses tend to present, and design strategies that will produce significant impact and deeper understanding of any business, healthcare or scientific problem. The certificate focuses on job orientation technical skills, and the technical knowledge necessary to consolidate, cleanse, and standardize enterprise data into SAP-HANA data warehouse systems. Students will gain the skills needed to store, manage, process & analyze large data sets, design advanced data systems, structures & algorithms and develop machine learning models to discover new Insights and hoard big data using SAP-HANA system.

As has been stated, both roles have similarities and differences that will make them converge on the same path, however, while one position needs to have previous knowledge in mathematics, the other doesn’t rely so much on that experience. Both will eventually have to come to solutions to the issues they find in the business, and the method they use for that goal will set them apart from one another. Having said this, it is important to remember that each role can also change depending on what each company is looking for, and the needs they have, which is why sometimes a business analyst will turn to a scientist for help and vice versa, or also decide to take the additional training to be able to evolve in their positions.

It is also important to state that both profiles have become more relevant in the last year in the middle of a pandemic since businesses have had to make crucial decisions to adapt to a new virtual and socially-distanced environment. According to the data science news portal, Datanami, companies will increase the budget spent on data in 2021. “Organizations are making dramatic budget cuts in many areas in an effort to overcome the effects of COVID-19 and keep their business viable. Yet, in 2021 we predict that many will sustain or actually increase their investment in data science to help drive the critical business decisions that may literally make the difference between survival and liquidation”, stated Domino Data Lab CEO Nick Elprin.

Furthermore, data science has been also widely chosen and used to foresee an eventual number of cases and develop a mortality risk calculator, as this article in Health IT Analytics states.

Business Analysis Vs Data Science






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