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Role of Data Science as a Tool for Gaining Business Intelligence

by Lois Earles

What is Business Intelligence

Business Intelligence (BI) is a process for assessing data and delivering actionable contents to leaders, managers, and employees which leads them to make better business decisions for the company’s growth. Organizations collect data from internal and external IT systems, prepare it for analysis, run queries against it, and create data visualizations and reports to make the analytics results available to business users. This data is used for operational decision-making and strategic planning as part of the BI process.

The ultimate aim of business intelligence efforts is to help firms make better business decisions that will help them raise revenue, improve operational efficiency, and gain a competitive advantage. To do this, BI uses a combination of analytics, data management, reporting tools, and diverse data management and analysis approaches.

It’s worth noting that this is a relatively new definition of BI, and the term has a tangled history as a buzzword. Traditional Business Intelligence was invented in the 1960s as a framework for sharing data across enterprises. In the 1980s, it evolved with computer models for decision-making and turning data into insights before becoming distinct from BI teams with IT-based service solutions.

Flexible self-service analysis, controlled data on trustworthy platforms, empowered business users, and speed to insight are priorities in modern BI solutions. This article is just the tip of the iceberg when it comes to business intelligence.

What is Data Science

Data science employs scientific methods, algorithms, and systems to obtain knowledge and to regulate data from noisy and unstructured data. It also applies that knowledge and actionable insights across a wide range of application areas. Data mining, machine learning, and big data are all connected by data science.

Data science unifies statistics, data analysis, informatics, and related approaches to use data in order to understand and analyze actual occurrences. Within the framework of mathematics, statistics, computer science, information science, and domain knowledge, it employs techniques and theories from a variety of domains.

Data science, on the other hand, is distinct from computer science and information science. Jim Gray, the recipient of the Turing Award, envisioned data science as the “fourth paradigm” of science (empirical, theoretical, computational, and now data-driven), claiming that “everything about research is changing” due to the effect of information technology and the data flood.

How do Data Science and Business Work Together?

Although data science or business intelligence can provide meaningful information to firms, combining the two delivers the most knowledge to drive strategic decisions. Considering the case of a professional services firm that has been unable to secure new business. Because they only have a limited number of resources to respond, they decide to employ a data-driven process to determine which RFPs they are most likely to win.

The organization decides to use business intelligence to analyze prior RFP results and generate customer and project profiles with a high success rate. The organization can then utilize that information to generate various hypotheses and scenarios and employ data science and machine learning to estimate the possibility of future project wins. As a result of combining business intelligence and data science, the organization now has a data profile of clients and projects that are in their sweet spot for winning new business.

It’s easy to see how BI and data science may both aid with insight, but when they are combined it provides greater value. Invest in online business intelligence training to make the best progress with your business.

Correlation between Data Science and Business Intelligence

At this point, it’s clear that data science and business intelligence have, and will continue to have, a fascinating relationship. Data science and business intelligence aim to provide significant data-driven insight, but data science looks ahead while business intelligence looks back. That isn’t to claim one is superior to the other. Each has a spot where they can solve various issues.

Despite their differences, data science and business intelligence can work together to produce greater insights than the sum of either part. That value will expand in the future, thanks to cloud computing, machine learning, and artificial intelligence developments. You’ll need tools that can help you reap the rewards of this mutually beneficial connection. Data Science and Business Intelligence are those tools.

A Data Scientist can handle high-speed, complicated, high-volume data from a variety of sources using sophisticated technologies like Big Data, IoT, and cloud.

Moreover, in a traditional BI environment, businesses are enforced to rely on the expertise of their in-house Analytics team to extract insightful data. Still, Machine Learning and Artificial Intelligence (AI) powered Data Science has launched Self-Service platforms that allow users to easily access, analyze, and extract results from the database without the need for technical assistance.

Changes brought by Data Science in Business Intelligence and what is expected in future

Data Science, according to experts, is an extension of Business Intelligence. When Business Intelligence Analysts assist managers and executives in making decisions, Data Scientists enable managers and executives to become Analytics experts themselves.

Traditional analytics tools have dominated BI, but Data Science has grown in popularity since it takes a more holistic approach to data management. Data Science has brought together data governance, analytics, advanced visualization tools, and business intelligence to create a comprehensive system for managing large amounts of data.

With the rise of e-commerce in recent years, worldwide retailers have realized that traditional BI is gradually giving way to Data Science and Machine Learning methodologies to turn real-time insights into successful business solutions.

According to a 2016 McKinsey Analytics report, Data Science, along with Machine Learning and AI, has surpassed traditional Business Intelligence because static or historical data is no longer adequate to anticipate future business patterns and occurrences.

Various data science and analytics tools, technologies, processes, and skilled data professionals must work together to extract the most useful data insights from this glut of digital data in order to stay relevant in the quickly expanding Big Data landscape. To be ready to tackle the world of Big Data, BI experts must pull up their socks and get their hands dirty with the latest BI tools and analytics, such as Augmented analytics.

How data science and Business Intelligence can promote strategic planning in organizations

• Data Science is concerned with data and is now widely used in analytics and machine learning. Data Science and Business Intelligence are involved in the discovery of meaningful and productive patterns and observations.

• EDA investigates the data and assists us in discovering entirely new features of it. The basic equation in business is to maximize earnings while also increasing corporate efficiency. Business intelligence is the process of sifting through data to extract usable managerial knowledge and information.

• When data and conclusions are used effectively, a company’s profitability can be increased by a factor of ten. The correct implementation aids in the transformation of a static plan into a system that is not only efficient but also future-proof. That is one of the benefits of data science.

• Data science is employed in many sectors of our daily life, including weather forecasting, stock market forecasting, health sciences, finance, logistics, sales forecasting, and many others. The implementation of data science and machine learning will undoubtedly improve business intelligence performance. This will result in increased corporate efficiency and better strategic planning.

Data-Driven Decision Making

Data science is a compilation of methodologies and processes for extracting information and understanding from unstructured data. When employed correctly, data science has a wide range of applications in the corporate world.

A business analyst will deal with business administration and participate in EDA, which is a method of analyzing datasets, summarising their key characteristics, working with the data, and refining it so that it can be used productively. Businesses can make better financial and marketing decisions when they have access to large amounts of data.

If a company has historical data on which products sold well at which times or in which locations, it may devise a strategy to boost sales. Big Data is extremely beneficial to retailers and fast-moving consumer goods sellers. Various crucial decisions can be made based on accurate data, which can lead to increased earnings.

There are numerous uses for data-driven decision-making. In finance, for example, it could be determining the best cost-effective approach to employ cloud services or hiring additional personnel. It could also be the most cost-effective strategy to market a new product.

In the case of marketing, data-driven decision-making allows us to determine which promotional media has the highest reach and ROI. Data can be utilized to track client loyalty in the case of overall company growth. Consumers’ past data can be brought in and examined to determine the demographics of the most devoted customers.


Successful data science, along with business intelligence, helps organizations develop better business strategies by assisting in strategic management, efficient decision making, and supply chain management. Therefore, giving them a competitive advantage over their competitors.

This will be reflected in the economic net performance in terms of profits. In organizations, successful strategic planning aims towards a desirable future, goals, or accomplishing the intended target. It’s all about the bigger picture and determining the best course of action.

Preparations are done from the perspective of Data Science, using data, numbers, statistics, projections, and other insights. This is unquestionably advantageous. For example, if all market factors and product sales are predicted, a corporation can combine all of its processes into a single centralized corporate plan, organizational structure, and predetermined budget. All of this contributes to the company’s smooth operation.

The Bottom Line

While either data science or business intelligence can give useful information to businesses, combining the two yields the greatest insight to help companies make strategic choices.

Hope that this article has clearly described the role of Data Science in business Intelligence and in helping organizations achieve their objectives.

The best way to learn data science is with Great Learning courses, where you can learn more about its concepts and its role in Business Intelligence at your own pace and within the comfort of your home.

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