Business intelligence and data science are two ways of looking at the world. Often we get bewildered by these two terms.
Data is no more new to any business. Almost every industry collects it, whether it’s software, pharma, automobile, retail or construction.
There is a list of several data collected by every business like their customers’ phone numbers, website visits, email addresses, sales records and many more.
It would not be a mistake to say data has become a lifeline for any business. However, data won’t value to any brand unless utilized.
Several brands gather data from various sources but face challenges in taking advantage of the data to gain valuable insights for their business.
As a result, the volume and variety of data are increasing and creating complexity for them.
Here is where Business Intelligence and Data Science come into effect. Both professionals work with data and can help you to transform business operations and help to create an effective business strategy.
They are two different types of businesses with two contrasting purposes. Each has its advantages and disadvantages, but they serve a similar motive: to provide decision-making tools and make your business smarter.
In this blog, we will discuss key differences between Business Intelligence and Data Science so you can quickly decide which one is right for your business.
What is Business Intelligence?
It is the process of analyzing data to aid in decision-making. The main components of Business Intelligence are analysis, insight, action, and measurement.
Companies use BI tools to monitor and support their management teams with insight into how well they are performing.
Furthermore, it enables decision-making by providing valuable information on where a business stands compared to its competitors and helps to make crucial technical decisions that can impact sales, marketing and product development.
Let’s understand in depth with an example:-
- Suppose a retail brand name, “XYZ”, wants to boost its sales. Instead of jumping directly into the investment marketing pond, they use business intelligence tools. That gives them in-depth insights into their customer’s buying preferences and habits using existing data.
- As a result, “XYZ” can understand the behaviour of their consumer in a better way. They discover that they always get 40% more sales on the Sunday of every month.
- Now they use this information to schedule promotions and other marketing campaigns to make sure they focus on that specific day of every month.
Basic terms of business intelligence:
Get familiar with these key terms for a deeper understanding of business intelligence.
A data warehouse is a system that stores companies’ information from different sources in a compact and accessible location.
They are vital to business intelligence. By analyzing and reporting data across the business, they provide meaningful insights.
Usually, they consist of data from various sources of business: Marketing, sales, finance, HR, operation, mailing lists, and others.
Business analytics and data mining-
The data in the data warehouse is further studied and mined by business analytics tools. Data mining helps to locate patterns and trends in data by combining databases, statistics, and machine learning.
Once the tools extract all the information from the data, a dashboard is prepared to present it. It allows for more effective communication between users and analysts. Charts, graphs and diagrams help to create visual representations of the data so that all parties can understand.
Data analysis and illustrations are then shared with key business stakeholders for them to spot primary insights and make decisions.
Benchmarking allows managers to track changes and performance against the organization’s objectives. In addition, benchmarking can be done against industry standards and competitors, providing another level of insight into what is successful and what can be improved.
Business intelligence tools:
Business intelligence tools are interactive, self-serving, and accessible to all levels of users. Historically, IT departments managed all access to data; today’s business intelligence tools allow users to create dashboards and reports depending on their needs.
Business intelligence tools empower individuals to answer their questions without relying on the help of experts to understand the data.
Some popular business intelligence tools include Sisense, MicrosoftPower BI, Tableau, Qliksense, Dundas BI, and many more.
What is Data Science?
In many ways, it’s similar to business intelligence. Data science involves extracting information from datasets and creating forecasts.
To do so, it uses machine learning, descriptive analytics, and other sophisticated analytics tools.
The process of data science begins with the collection and maintenance of data. Next comes processing that uses data mining, modelling, and summarization techniques to derive insights.
The next step is data analysis. It conducts through text mining, regression, descriptive and predictive analytics, and other methods. With the aid of this information, patterns behind the raw data get discovered that help to predict future trends.
Several industries are using data science. Businesses can use such an approach to develop new products, study customer preferences, and predict market trends.
For example, automakers use data science to improve their auto-driving systems by collecting extensive information for statistical analysis. Auto-driving developers work with computer scientists to improve a system that can be responsive to different situations through machine learning.
Let’s understand in depth with an example:-
- Suppose a recruitment platform collects job seekers’ career information on their mobile app. They want to create a good user experience by customizing it for each user.
- Using data science processes, including artificial intelligence, allows them to access the profile and intelligently recommend relevant jobs.
- As a result, it improves the app’s rating as users feel they are getting a much more personalized experience.
Basic terms of Data Science:
Get familiar with these key terms for a deeper understanding of Data Science.
Machine learning is the process by which computers learn from examples, just as humans do. Computers use data to become more efficient, let’s say, by using voice assistants like Amazon Alexa to make recommendations based on your daily alarms.
Artificial intelligence (AI) is the ability of computers to perform tasks in a way that seems humanlike.
Today, artificial intelligence is becoming very important for many companies like Google and Facebook in building their products or for Facebook’s use as services.
These two companies have used AI in big-data analysis, which works along with their internal systems to improve the quality of their information to make well-informed decisions.
Business analytics and data mining-
They are both essential components of data science.
Data analytics is the technique used to collect and analyze data to make informed business decisions. Data mining is a technique to forecast future trends by studying existing ones.
Data is any information that can be stored or transmitted. Data is most often stored in databases, but it can also be found on hard drives and other devices.
Big data is a term used to describe large collections of data sets that are analyzed using computers to reveal trends and patterns. Big data can be extremely complex, making it difficult for conventional data management tools to store or process it.
In addition to being more widespread than small data sets, big data also holds more information which makes it useful for planning and strategy.
Machine learning technology has been designed to accelerate the process of uncovering and analyzing key trends in big data by going beyond traditional algorithms by exploring new ways of managing this type of information.
Data Science tools:
There are many popular data science tools that are used for data visualisation, statistical programming languages, algorithms, databases and more.
Some of the most used tools are Rapid Miner, DataRobot, Tableau, Amazon Lex, SAS, and others.
Comparison between business intelligence and data science?
The purpose of both business intelligence and data science is to turn data into information that supports business decisions. It is important to note that the two approaches differ in some ways.
- Business Intelligence analyzes historical trends; answers questions such as what occurred previous to the current period and what trends may emerge in future.
- Data Science Produces forecasts based on historical data and statistical analyses; answers the question of what will happen or which is the most likely outcome in the future.
- BI requires basic business knowledge, including advanced statistics and data transformation skills.
- Data science requires more technical skillset compared to business intelligence. One needs to know to code, and data mining, including advanced statistics and domain knowledge.
Data collection and management:
- BI is designed to manage well-organized data.
- DS is designed to manage a large and less volume of dynamic and structured data respectively.
- Business Intelligence is less costly and requires fewer resources in everyday business management.
- Data science requires more advanced skills in forecasting, managing dynamic data and data streams
- Business intelligence is a data-driven approach to decision-making in which visualizations of past and present trends are compared with competitor data to conclude the performance.
- Data science involves the use of hypothesis testing, exploratory data analysis, and trend identification to predict what might happen in the future.
- Business intelligence is a proactive process, meaning that it helps to make decisions based on what has occurred in the past. If a business had greater website traffic during a sale, it may be able to hold more sales in months where traffic is typically low.
- Data science is predictive – it helps organizations prepare for future events by anticipating what will happen in the future. It helps define the business strategy by predicting what will happen in the future.
Business intelligence and data science are two distinct terms that, when considered alone, seem to mean very similar things. However, from an analytical point of view, these two fields of study focus on different disciplines and aspects of the business. Business intelligence invites an in-depth analysis of past performance as a means of informing decisions. Data science, however, looks to the future and brings with it the exciting prospect of predictions and determining cause-and-effect relationships. Both areas have driven a huge amount of change across the world of business, creating more informed decisions through their use. It’s clear that both have amazing implications for any organization using them effectively, but while they may appear to be cut from the same cloth, they differ in objectives and processes.