The terms data science and Business Intelligence (BI) are becoming popular every passing day in the digital era. While both share a common foundation of data management and visualization, both are somewhat different.
BI can be considered a part of the larger data science landscape, which contains a wealth of information.
The business intelligence process assists managers, executives, and workers make informed decisions about their businesses by analyzing data. It collects, integrates, analyzes, and presents the data through software services and tools.
The main objective of Business Intelligence is the growth of the business. BI helps in better strategic decision-making, optimizing the business processes, increasing the efficiency of operations, and gaining insights about the market, giving businesses an edge over other business rivals.
Data science is a reservoir of tools that are used to extract valuable insights from raw data. It entails extracting, manipulating, visualizing, maintaining, and predicting data.
As a general rule, Data Scientists find patterns within data. It is a multidisciplinary field that has three main components – Mathematics, Statistics, and Programming. Apart from this, data scientists rely heavily on artificial intelligence, machine learning, and deep learning to find the hidden patterns and insights from the data and make predictions.
Difference between Data Science and Business Intelligence
Business Intelligence and Data Science are data-driven fields, but data science is much more complex than business intelligence.
Business intelligence is only limited to the business domain and works for the growth of businesses. Data Science, on the other hand, offers a much broader perspective.
Past data is analyzed in business intelligence so that the company can understand its current trend. Data Science, on the other hand, focuses on making future predictions and forecasting growth.
When it comes to BI, the goal is to generate reports based on structured data within an organization, whereas data science aims to generate insights from data. The insights presented here are the outcome of complicated predictive analytics, and their output is not a report per se but rather a data model.
The scope of Business intelligence tools like InsightSquared Sales Analytics, Klipfolio, ThoughtSpot, Cyfe, TIBCO, etc., is limited to the analysis of management information and the formulation of strategic plans. However, data scientists use complex algorithms, data processing, and even big data tools like SAS, BigML, MATLAB, Excel, etc., in the development of their models.
The fundamental components of BI are based upon analytics. In contrast, Data Science emphasizes taking a holistic approach to data management, such as providing a complete Data Governance, Analytics, BI, and advanced data visualization framework. For small or medium-sized businesses with a limited number of Analytics needs, an average business intelligence solution from the market might be sufficient, while business enterprises with deep automation requirements may benefit from a machine-learning-powered BI system, again requiring the participation of a Data Scientist.
There’s a difference in flexibility as well. Data science is more flexible. Going forward into the future, a variety of data sources can be incorporated as necessary. Business Intelligence is significantly less flexible. Data source estimates need to be pre-planned in BI.
After reviewing all the above comparisons, it can be concluded that DS and BI are both analytical and information-centric, but the level of insight makes a difference. Insights from data science are mature and future-focused. This is why data science is said to be an evolution from Business Intelligence. Thus, it can be safe to say that data science needs to be combined with BI to be effective.