Business Intelligence vs Data Science

Man sitting in front of computer screen with red display

As part of their digital transformation agenda, many companies are developing data science initiatives to stay competitive in today’s challenging and constantly evolving business landscape. 

According to Forbes, about 59 percent of enterprises had adopted big data analytics by 2018, which is up from 17 percent from 2015.1 Data scientist jobs have increased by 650 percent since 2012 and the U.S. Bureau of Statistics projects that 11.5 million new jobs in this field will have been created by 2026.2 

Companies are drawn to data analytics for the benefits that it brings to various industry verticals and use cases. It provides deep insights into operational processes, behavioral patterns/preferences of the target market, factors influencing the business landscape and so much more. It’s one capability that every business needs to gain an edge over the competition.            

But many of these companies do not sufficiently understand the world of data science, its branches and how it can be applied to support business operations. A clear example of this knowledge deficit is the inadvertent conflation of business intelligence and data science. 

Many enterprises are unaware that these fields require different skills and technologies. As a result, they fail to use their analytics tools appropriately and reap little from their investments in data science capabilities.

What Is Business Intelligence?

Essentially, business intelligence (BI) is the use of special methods and software to derive actionable business insights from relevant data.3 

For example, beverage companies leverage business intelligence when they analyze reams of relevant data to identify patterns in consumer purchases. Supply chain firms do so when examining a trove of shipping information to determine the most significant points of delay in their supply chain.     

BI tools make this information available as charts, graphs, maps and dashboards—formats that are easy for the reader to grasp. This underscores the role that the business intelligence analyst plays in producing these reports. The quality of BI that’s produced depends on the skill of the analyst, as well as the quality, quantity and accuracy of the data used.   

In summary, enterprises extract business intelligence from past and present data and leverage it to solve current challenges.

What Is Data Science?

Data science makes projections about the future from existing data. The practice of data science involves data analytics: using algorithms and machine learning principles to detect patterns in the data and extrapolate subsequent trends.

With data analytics, a company can make predictions about its future sales from present sales volume. Marketing teams also use data analytics to determine the likely impact of their campaigns across various communication channels.

Data science draws its methods from several fields, including statistics, machine learning and information science. Data scientists are expected to be knowledgeable in these areas, and bring these skills to bear when analyzing and generating predictions from available datasets.

Business Intelligence versus Data Science: The Differences and Why They Matter

Both business intelligence and data science provide businesses with ways to turn their data into useful assets. However, there are fundamental differences between them.

Data science is predictive. It takes current data and makes future projections. On the other hand, business intelligence is more descriptive; it interprets data from the past and present in terms that are relevant to its user’s purposes.

BI analysts work with business intelligence data to answer existing business questions. Data analysts leverage algorithms to model future scenarios about which new questions can be asked. Although these differences seem subtle, they are critical and can help companies derive maximum ROI from their technology investments and achieve their business goals faster.

First, it helps you understand what tools are needed and how to apply those tools. Statistics and data visualization software may be sufficient for a BI project, but data analytics will often require machine learning tools.  

Businesses also have to employ persons possessing the right knowledge and skills to fill data scientist and business intelligence analyst roles. Both are ideally grounded in statistics, mathematics, and IT systems. However, the business analyst typically has a firmer background in business issues.


Platforms that Implement Business Intelligence and Data Analytics

There’s no shortage of platforms offering business intelligence and analytics capabilities. But they do not all achieve the same standard of service.

Some of the more popular platforms include:

Microsoft Power BI4

Microsoft Power BI is a cloud-based business intelligence and analytics tool that enables businesses to evaluate and share their data across multiple devices. It’s equipped with interactive reports, easy visualization, scalable dashboards and a host of other useful features.  

This tool takes disparate datasets and weaves them into easy-to-understand charts and graphs. It’s widely used, partly because it’s integratable with the Office 365 environment.

Oracle Analytics Cloud5

Oracle’s AI-powered analytics tool comes with a range of benefits. They include conversational analysis, quick correlation detection, multi-scenario modeling and real-time collaboration between users. This software has strong natural language capabilities that allow it to receive queries in 28 languages. That’s more than any other analytics software.

Reports and dashboards from Oracle Analytics Cloud can also be embedded into other platforms, such as websites and Customer Relationship Management (CRM) applications.

SAS Visual Analytics6

SAS, through its Visual Analytics product, offers a BI platform with strong automation and ease-of-use credentials. Besides its smart visualization and reporting, this tool also identifies potentially related groups of data as well as best-fit graphics for any given set of data. This helps users to easily identify and understand trends.

The Dynamic Nature of Business Intelligence and Data Science

Business intelligence and data analytics have both grown tremendously over the past two decades. They have evolved along with the technologies associated with them. As data pools expand, businesses will require stronger, more sophisticated tools to glean better insights from new datasets.

They will also need to hire workers who are skilled enough to use these tools effectively. This is already happening. The U.S. Bureau of Labor Statistics forecasts the demand for business intelligence and other management analyst roles to grow 14 percent through to 2024.7

Given the evolving requirements of these fields, anyone considering business intelligence and data science-focused jobs should be conversant with the fundamentals and current trends in these fields. This involves equipping yourself with the skills necessary to pursue a career in your preferred vertical.

Take advantage of the increased focus on data science initiatives and fast track your pathway to a data science career with courses that provide systematic hands-on learning.


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