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10 differences between Data Science and Business Intelligence

In years past, Business intelligence (BI) was something only big blue chip companies could enjoy. Mainly because employing analytics software was expensive, and it required building data centres and hiring IT specialists, who are also expensive. BI systems have, over time, become less expensive and have become a useful way of gathering corporate data and correlating that data in a way that will produce useful observations to the business.

However, times have changed. Data is getting bigger every day, in terms of volume and variety, and businesses need Data Science if they are to capitalise on market opportunities faster than their competitors. BI and Data Science are distinctively different beasts. BI systems deliver answers to the questions you know you need to ask. They are usually single systems that don’t help you to predict anything: they may help you to view the relationships of various variables, but they don’t help you to easily obtain any new meaning from those relationships, nor do these systems help you to apply insights to new data. Data science driven Big Data programmes, on the other hand, may consist of several technologies and should work to provide customer insight that companies can use to predict present and future patterns, thus enabling them to react customer behaviour accordingly.

Data science versus traditional business intelligence

Using data science allows organisations to stop being retrospective and reactive in their analysis of data, and start being predictive, proactive and empirical. Moving from traditional business intelligence (BI) to adopting data science is a huge shift and a fundamental part of becoming a data-driven organisation. By taking an empirical view of its data and implementing tools like Hadoop and NoSQL databases, a public sector organisation can transform its operations entirely. Whether that means taking the pressure off the data warehouse, and so reducing cost; or driving efficiency improvements through recommendations for process changes, the opportunity is enormous.

To survive and prosper in the increasingly competitive market as well as to be able to resolve complex business problems, drive innovation and growth, companies must shift their focus from traditional BI to data science. Data Science changes the game for virtually all industries. When used in conjunction with predictive analytics, it allows organisations to achieve real-time insights and make future predictions that increase understanding of customer behaviour, improve response to customers and deliver a tangible competitive advantage. To give you some context, here are 10 key differences between the two:


BI systems are designed to look backwards based on real data from real events. Data Science looks forward, interpreting the information to predict what might happen in the future.


BI delivers detailed reports, KPIs and trends but it doesn’t tell you what this data may look like in the future in the form of patterns and experimentation.


Traditional BI systems tend to be static and comparative. They do not offer room for exploration and experimentation in terms of how the data is collected and managed.

Data sources

Because of its static nature, BI data sources tend to be pre-planned and added slowly. Data science offers a much more flexible approach as it means data sources can be added on the go as needed.


How the data delivers a difference to the business is key also. BI helps you answer the questions you know, whereas Data Science helps you to discover new questions because of the way it encourages companies to apply insights to new data.


Like any business asset, data needs to be flexible. BI systems tend to be warehoused and siloed, which means it is difficult to deploy across the business. Data Science can be distributed real time.

Data quality

Any data analysis is only as good as the quality of the data captured. BI provides a single version of truth while data science offers precision, confidence level and much wider probabilities with its findings.

IT owned vs business owned

In the past, BI systems were often owned and operated by the IT department, sending along intelligence to analysts who interpreted it. With Data Science, the analysts are in charge. The new Big Data solutions are designed to be owned by analysts, who spend little of their time on ‘IT housekeeping’ and most of their time analysing data and making predictions upon which to base business decisions.


A retrospective and prescriptive BI system is much less likely to be placed to do this than a Predictive Data Science programme.

Business value

Analysis of data should inform business decisions in the best interests of the company, which means demonstrating value in the here and now and predicting it in future. Data Science is much better placed to do this than BI.

Looking at the above, it should come as no surprise that companies are driving up their investments into Big Data strategies and delivery platforms, driven by Data Science. However, financial investment is secondary to the mind-shift that is required to truly succeed with Big Data. Proof points and use cases must be introduced to convince all key stakeholders to shift to the truly data-driven culture that is a necessary foundation for a successful Big Data Analytics strategy, which is what we’ll be looking at in our final blog post in this series.

Mike Merritt-Holmes, VP Services, UKI & Nordics Think Big, a Teradata company