Many new terms have been invented to explain the new age of data, with business intelligence (BI) and data science being among the most popular. They are also often confused with one other. So what exactly is BI, what is data science — and what’s the difference?
It all starts with data
Or more specifically, big data. This refers to the huge amounts of digital information, both structured and unstructured, that businesses deal with every day. It’s called “big” data because traditional data processing software is unable to deal with it adequately. Instead, specialized BI tools are used to collect, warehouse and process the data to make it useful to business professionals.
Data science also begins with the collection of big data — but that’s where the similarities end. Whereas BI involves looking at big data from a historical perspective to answer existing questions, data science is about digging more deeply into the data to uncover new insights that will help to transform the business.
Both BI and data science can play important roles in a data-driven organization. Key differences between BI and data science are:
Point of view
BI systems focus on past events, such as “How many unique visits did my website get this month?” and “Which neighborhoods saw the most flu cases last winter?” Data science uses simulation and modeling to predict what might happen in the near future. For example: “What products should I recommend?” and “Would this treatment be effective for this population?”
BI plays an important role in helping organizations gain real-time insight into how their business is performing day-to-day. It is typically used to generate key performance indicators (KPIs), trend graphs and other reports. It helps the business operate efficiently, make more informed decisions and identify and solve problems more effectively. Data science, on the other hand, is focused on mining the data for insights that will drive future innovation and growth.
A typical BI system is usually preconfigured by its client organization to use a defined set of data sources, and create preplanned reports from a fixed set of business rules. Data science offers a much more flexible approach, whereby data sources and data-processing modules can be added on the fly as the system evolves. This exploration and experimentation allows the business to be much more agile in its data strategy.
In summary, BI helps you to run the business, while data science helps you improve the business and make it more competitive.
Data science vs. business analytics
BI and data science differ fundamentally in that one is focused on the present and the other on the future. By contrast, business analytics has more in common with data science in that both employ a predictive approach to improve business outcomes.
What does a business analyst do? They often have expertise in a specific business domain (e.g., retail, supply chain or healthcare) and employ a wide variety of tools and methods to analyze business problems and suggest solutions. As well as BI software, they might also use customer surveys, interviews and other sources.
Data scientists take a more technical approach. Using mathematics, computer science and statistics, they design and deploy the algorithms used in decision support. Whereas business analysts can come from a variety of backgrounds such as business and humanities, data scientists often have a strong background in computer science, mathematics or IT.
Becoming a data scientist
Data science is a relatively new field, but many higher learning institutions are now offering data science degree-level courses for those interested in pursuing a career in data science or analytics. One example is the Master of Science in Business Intelligence & Analytics (MSBIA) offered by Saint Joseph’s University. Subjects covered in this two-year program include data mining and warehousing, market intelligence and online analytical processing.
Compared to standard business intelligence, data science allows organizations to evolve from being retrospective and reactive in how they handle data, to being predictive and proactive. If you’re looking for a career where you can flex your creativity and technical talent to help organizations grow, it’s a great choice.