Data science is growing immensely in today’s modern data-driven world. Business intelligence and data science are two recurring terms in the digital era. These involve the use of data that are totally different from each other. Data science is a bigger pool that contains huge information; business intelligence can be considered as a part of the bigger picture. These are both data-focused processes, but there is some difference between the two. Business intelligence focuses on analyzing things, whereas data science aims to predict future trends. Data science requires an effective technical skill set as compared to business intelligence.
Power BI certification allows interested candidates to explore Power BI concepts such as Microsoft Power BI desktop layout, BI reports, dashboards, power BI DAX commands, and functions. Microsoft Power BI is a widely used business intelligence platform, and this follows a hands-on applied learning approach.
This is a means of performing descriptive analysis of data with the help of technology, skills for allowing one to make informed business decisions. The tools which are used for business intelligence collect, govern, and transform data. This allows decision-making by enabling data sharing between internal and external stakeholders. The main aim of BI is to derive actionable intelligence from data. BI enables acton such as gaining a better understanding of the market, uncovering new revenue opportunities, improving business processes, and staying ahead of competitors. This has shown its impact on cloud computing. Cloud has made it possible to collect data from resources and use this efficiently. This deals with the analysis of structured and unstructured data, which paves the way for new and profitable business opportunities. Business intelligence tools enhance the chances of enterprises entering a new market as this helps in studying the impact of marketing efforts.
Importance of business intelligence:
As the data volume is increasing, business intelligence is more essential than ever in providing a comprehensive snapshot of business information. This provides guidance towards informed decision-making and even identifies the area of improvement, which leads to greater organizational efficiency and even increases the bottom line.
Data science mainly involves extracting information from datasets and creating a forecast. This involves the use of machine learning, descriptive analytics, and other sophisticated analytics tool. This is a process of collecting and maintaining data. Further, this involves the process of data via data mining, modeling, and summarization. After this, data analysis is conducted, etc. After analyzing the data, the patterns behind the raw data can be discovered to forecast future trends. Data science is used in different industries. Companies can use a devised approach to develop new products, study customer preferences and predict market trends. Here high volume of data can be collected from electronic medical records and individual fitness trackers.
Importance of data science:
Data science in different companies is able to predict, prepare and optimize their operations. Data science plays an important role in the user experience; for many companies, data science is what allows them to offer personalized and tailored services.
Business intelligence vs. Data Science: Is it the same or different?
Business intelligence and data science play a key role in producing companies’ actionable insights. Let us check on some common attributes between the two:
- Perspective: business intelligence focuses on the present, while data science looks toward the future and further predicts what will happen next. Business intelligence works with past data in order to determine the responsible course of action, while data science creates predictive models which recognize future possibilities.
- Data types: business intelligence works with structured data, which is typically data warehoused or stored in data silos. Data science works with structured data and further results in greater time, which is dedicated to cleaning and improving the data quality.
- Deliverable: reports are used when it comes to business intelligence. Different deliverables for business intelligence include creating dashboards and performing ad-hoc requests. Data science deliverables have similar end goals and focus on long-term projects. These projects include creating models in production instead of working from enterprise visualization tools.
- Process: the difference between the processes of both comes back to the time, same as how this influences the nature of deliverables. Business intelligence mainly revolves around descriptive analytics. This is the first step of analysis and sets the stage for what happened in the past. Here non-technical business users can understand and interpret data via visualization. Data science would take the exploratory approach and means investigating the data via its attributes, hypothesis testing and exploring different trends, and answering questions on a performance basis.
- Decision making: business intelligence and data science are used for driving decisions, and this is central to determining the nature of decision-making. The forward-looking nature of data science is used at the forefront of strategic planning and determines the future course. These decisions are preemptive instead of responsive. Business intelligence aids in decision-making based on previous performances which have occurred. These fall under the umbrella of providing insights, and this supports business decisions.
Both business intelligence and data science have differences, but the end goal of these are ultimately aligned. It is important to note the complementary perspective of both. From the company perspective, both data science and business intelligence play similar roles in business processes that provide fact-based insights and support business decisions. Data science and business intelligence are facilitators of each other, and it is said that data science is best performed together with BI. These are required to have an efficient understanding of company trends which are hidden in the large amount.
In order to summarize simply, data science and business intelligence are not the same things, but this represents the evolution of business intelligence; thus placing data into introspective plays a central role in the business. Data science and business intelligence are equally vital roles on the same team. The individual roles are different, and when together, they serve the broader business analytic world. Though there is a difference in the way data science and BI handle objective tools, the end game is the same.
James is the head of marketing at Tamoco