It is impossible for the world to function without data. With time, data has become an inseparable part of life and every process related to data stands important. Business intelligence (BI) and data science are two processes focused on data but they are different in their approach to it.
Generally, data science focuses on making predictions or forecasts about future trends. Whereas business intelligence makes efforts on analysing past events to evaluate what has generated positive results in the past and what has not proven to be in the organisation's favour. Data science is much more technical when compared to business intelligence which is more of an abstract concept that involves few technicalities.
Read on to do about data science and business intelligence and what are the distinctions between the two.
What is Data Science?
Data science is a specific field of research that employs sophisticated tools, procedures, and vast volumes of data. It aids data scientists in finding, following, and extracting useful information from data patterns. Making important decisions in business and finance requires this kind of information.
The field of data science can be characterised as one that uses sophisticated analytics, statistics, and scientific principles to glean useful information and draw insightful conclusions from both structured and unstructured data. These in turn help in making corporate decisions, developing strategy, and other activities like financial planning.
What is Business Intelligence?
Business intelligence is the process of enhancing a company's trends and decisions through the search, aggregation, and evaluation of the accumulated data stored by an organisation. Business intelligence is one of the most profound and innovative technical concepts of the 1990s.
Business intelligence makes it possible to make decisions involving data technology. Initially taking the form of organised consumer data, business intelligence eventually started to influence big organisations' choices. Business intelligence presents consumer data in representational form such as graphs, charts, tables, etc.
Difference Between Data Science and Business Intelligence
Both data science and business intelligence help convert structured and unstructured data into readable information to enhance the decision-making process. The distinctions between data science and business intelligence can be enumerated as follows:
| Basis | Data Science | Business Intelligence |
| Data type | Data science mostly deals with unstructured or semi-structured data which is in real time and distributed. | Business intelligence deals with structured data which is sliced and divided into warehouses. |
| Complexity | The degree of complexity is higher in data science. | Business intelligence is simpler in nature and involves low complexity. |
| Role | Data science uses mathematical tools and statistics to identify and evaluate data patterns, analyse them and make predictions about future trends. | Business intelligence focuses on organising datasets, taking out useful information and showcasing them in representative or visualised form such as dashboards, charts, graphs etc |
| Focus | Data science mainly focuses on the future and makes forecasts regarding the same. | Business intelligence focuses on past and present performance. |
| Usage | Data science is used by companies to make close to accurate predictions about future situations in order to increase profit and mitigate risks. | Business intelligence is used by companies to identify the cause of any failure or success in the past to make changes in the present, accordingly. |
| Skills required | Data science is a more advanced field as it is a combination of subjects like mathematics, statistics, programming language and machine learning. Professionals in data science must possess the skill of data modelling. | Business intelligence requires a lesser qualification as compared to data science. Business intelligence is more about reporting and presentation of data in a representative format that can be done through Excel sheets. |
| Tools and software | Data science professionals must possess insights into predictive algorithms and be equipped with knowledge of programming languages like R and Python. | The knowledge tools required by business intelligence professionals are Tableau, Watson Analytics, QlikView etc. It focuses more on the concept of data visualisation and extraction. |
| Process | The nature of data science processes is active and repetitive and there is a larger scope for experimentation. | Business intelligence processes are static in nature with zero to little experimentation. |
| Flexibility | As data science is futuristic, it provides more flexibility to add or delete data sources as per requirement. | Business intelligence is less flexible as the data sources have to be calculated and planned beforehand. |
| Method | Data science methods are mostly scientific, analytical and technical. | Business intelligence methods are always analytical. |
| Type of approach | Data science takes a proactive approach. | Business intelligence relies upon the reactive approach. |
| Data size | The technologies used in data science can hold large amounts of data, that is, terabyte size. | Business intelligence technologies and tools are not equipped with the facility of holding large amounts of data. Hence, they are restricted to small datasets. |
| Business value | The business value of data science is considered to be more when compared to business intelligence as it is focused on the future and makes predictions to make the organisation better. | Comparatively, the business value of business intelligence is considered to be lower than data science As it is restricted to extracting data and representing them in the form of charts and graphs. |
| Expertise role | An expert in data science is known as a data scientist. | An expert in business intelligence is a business user. |
| Consumption of information | Data science information is consumed at multiple levels in an organisation which includes the executive level, managerial level, enterprise-level and so on. | Business intelligence information is mainly consumed at the department level and the enterprise level in a company. |
Conclusion
Although both data science and business intelligence have very different approaches towards data, both of them effectively work with them. Business intelligence is advantageous at the initial point of establishing an organisation whereas adding layers of data science in the later part can make it more functional and dynamic. Data science and business intelligence professionals are in high demand as no sector can work without data.
If you want to get a grasp of the study of data science and business intelligence then the IIT data science course can be a perfect fit for you. The IIT data science course can always act as a support to uplift your career along with your academic degree. It can help you stand out among your contemporaries. To have a successful career in the said discipline, consider signing up for the Certificate Program in Data Science and Machine Learning, IIT Roorkee by Imarticus.
