There are two popular definitions in the contemporary digital discourse: data science and business intelligence. Both are frequent when it comes to data management in the context of business processes and marketing. The indiscriminate use of both definitions leads to sorrowful misconceptions oftentimes.
However, the difference in meaning between data science and business intelligence is not so fine to make them both interchangeable. The difference is similar to the one taking place between the words “data” and “information”. Of course, particular nuances are available as well.
It is worth knowing the nuances not to confuse the definitions. It allows dealing with both data science and business intelligence without expecting irrelevant outcomes that those practices can hardly deliver by default.
Let’s dive deeper into the distinctive features of data science and business intelligence to make things right.
What Stays Behind?
Both activities include data collection, modeling, and intelligence gathering in a broad sense. Probably, this is all that unites both technologies. Then, only differences follow.
Business intelligence is specific for certain business-related issues such as price, profit, efficient use of resources, etc. In contrast, data science represents the ways how various factors (social, spatial, seasonal, etc) affect business as a whole. Data science brings data to processing algorithms while business intelligence has to use already available algorithms and technologies.
At first sight, both terms are interlinked tightly enough to be interchangeable. But in fact, this is not so. Different sorts of data stay behind the two technologies making their distinction drastic. But more on that later.
Historically, business intelligence has been known since the last decade of the 20th century. This is an applied-science discipline widely used in business management. Data science as a separate subject was formed in the 2010s approximately. To recognize the difference between data science and business intelligence it is, first of all, necessary to consider some basic notions related to both entities.
Data, Information, and DLCM
If we take the word “data” as a term, it means the raw unprocessed flows of data coming from various sources capable of generating any data as such. When we speak about information, we mean processed data having a certain sense in one or another context. Classification of data types along with data lifecycle management (DLCM) is worth mentioning here as well.
DLCM implies numerous activities related to data processing. When we collect, classify, save, record, use, and delete data, the data lifecycle management takes place. Data classification implies the following types of data in the present digital environment:
Well-structured data. This type implies clear and visible data. Quite insignificant processing is needed to interpret such a type of data. This is what can be called information.
Semi-structured data. Not entirely random data is meant by this type: a kind of poor structuration is available to some extent. Some analysis is required to find data correlations.
Non-structured data. This is just random data that requires complicated processing to turn the data into something meaningful.
When basic notions are defined, it is easier to understand what is what.
What is Data Science?
Data science is an interdisciplinary area that refers to decoding (and even demystification) massive amounts of data. This is about Big Data only. Mathematics, statistics, computing, machine learning, and other related research fields constitute data science in combination. The following five stages shape the workflow of data science:
- Data collection
- Data retention
- Data processing
- Data analysis
- Data reporting
Even though the stages reflect quite a generalized scope of activities, data science appears to be a complex scientific approach to what turns into information eventually.
What is Business Intelligence?
Business intelligence is a spectrum of technologies and practices that cover business-related information to be collected, compared, processed, and analyzed. Business intelligence is great at monitoring business efficiency to improve business planning.
Business intelligence comes to a deep analysis of business processes at the end of the day. That’s why business analysts are the ones who practice business intelligence. They use various forms of quantitative analysis along with iterative algorithms of prognostic modeling to interpret business data. The statistics they use help understand what has been happening in the company's business activities to develop strategies for further growth. Business analysts solve complicated problems referred to both profitability and cost reduction.
Types of Business Analysis
Business intelligence is based on data-science technologies that can unlikely be understood by non-specs. Nonetheless, the available basic types of business analysis address quite simple questions that can be raised by any business person. In other words, each type of business analysis is determined by a corresponding question to be answered. Here they are:
- “What’s happened?”. The question reflects the descriptive business analytics that can be performed with quite simple tools (MS Excel, for example) via such methods as detailing and correlation;
- “Why did it go down the way it did?”. This is about diagnostics that rely on various analytical techniques based on data analysis;
- “What’s coming next?”. Predictive analytics come into play with both statistics and mathematics. This type of business analysis is the most complex one having the following sub-categories:
- predictive modeling
- root causes analysis (RCA)
- identification and validation of data (Data Mining)
- forecasting (what is to happen if the trend keeps going)
- the Monte Carlo method (how it will go what is to go) - “What does it need to be done?” The prescriptive analysis is a call-to-action from a business standpoint. This is when tactical recommendations in the style of “try so” appear to optimize business processes.
Data Science vs Business Intelligence From a Managerial Perspective
Data science covers many interdisciplinary areas such as computing, mathematics, statistics, programming, AI, machine learning, and the like to address the challenges related to Big Data processing. In contrast, business intelligence serves to solve particular business issues in which analytics, planning, modeling, and predictions play critical roles. Formally speaking, business intelligence can be considered a branch of data science.
Nonetheless, the managerial challenges inherent in various business activities can reverse the relationship between data science and business intelligence. In many cases, it is hard to determine which of the two has a higher priority for business managers. It is worth compartmentalizing typical aspects inherent in business management to see how data science and business intelligence behave in each case.
Use of Data
Data science utilizes all three types of data (well-structured, semi-structured, and non-structured) that comprise the so-called Big Data. Business intelligence deals with well-structured data only (information) that requires no extra processing to be applied to analytics.
Required Skills
Data science engineers have to be savvy in computer sciences, statistics, mathematics, programming, and data analysis to be able to work with massive amounts of raw data. Besides, such advanced technologies as artificial intelligence, deep learning, and neurolinks should also be present among the skills of data science specs.
Business intelligence engineers should possess mathematics, statistics, modeling, and planning to be able to optimize business processes by using relevant information collected for analysis.
Scope of Application
The business-related scope of data science includes a complex analysis of numerous factors that may affect customer behavior. Data science allows identifying common trends as well as modeling behavioral patterns.
Business intelligence provides various analytics on the ongoing business processes, profitability, cost reduction, sales forecasting, demand management, etc. In other words, the scope of business intelligence includes certain business tasks.
Typical Domains
The sectors where data science is frequently used cover education, technologies, academic research, finance, and e-commerce.
Business intelligence ranks high in industries, marketing, retail, technologies, and finance.
Decision-Making
Since programming in a broad sense seems to be the main activity in data science, no critical decisions with regard to business processes can be drawn from findings of data science by managers.
The very results of the statistical analysis provided by business intelligence imply making critical business decisions at the end of the day.
Main Tools
Programming is the basic activity in data science. Various programming languages can be applied, but the most popular are Python and R. Many other technologies that belong to computer science such as AI, ML, Hadoop, TensorFlow, Spark, neurolinks, and the like constitute the toolkit of data science.
Statistical analysis is the main process in business intelligence. Such tools as MS Excel, databases (SQL, etc.), Power BI, Cognos, and highly-specialized packages of statistical software serve business intelligence specs.
Workflow
The working process inherent in data science includes a lot of studies and works on data extraction. The workflow is interdisciplinary by nature.
Routines with numerous iterations constitute the typical workflow in business intelligence. But in each case, business-specific activities take place.
Conclusion
Data science and business intelligence can overlap in many aspects once both activities imply the processing of various data. However, they differ in the scope of application and working routines. Besides, the types of processed data make data science more comprehensive and integral in comparison with business intelligence.
At the same time, business intelligence is focused on business-specific information to a greater extent than data science is. Hence, business intelligence seems to be more applicable to commerce activities from a managerial perspective.
Contact us today to determine which activity - data science or business intelligence can meet your business challenges best. But whatever choice may happen, our experts will assist you equally well in both domains.
