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The Evolution of Data Insights: Data Science vs. Business Intelligence in the Big Data Era

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data science business intelligence

Business Intelligence and Data Science are two distinct disciplines focused on data analysis. Discover the differences, the commonalities and, above all, the complementarity between these two fields...

Not so long ago, traditional, descriptive Business Intelligence was sufficient to monitor a company’s performance. In the age of Big Data, however, BI is no longer enough.

Faced with the explosion in the volume and velocity of increasingly varied and complex data, and the multiplication of sources, Data Science has become indispensable for collecting and processing information in real time, and extracting all its value.

Today, Business Intelligence and Data Science must be combined to meet the modern challenges of Big Data. Discover the differences, similarities and complementarities between these two fields.

What is Business Intelligence?

Business Intelligence (BI) brings together technologies and skills that enable descriptive analysis of data for the purpose of making better-informed decisions. BI tools enable data to be collected, managed and transformed.

By analyzing data, it is possible, for example, to better understand a market, discover new revenue opportunities, improve business processes or gain a competitive edge. Generally speaking, BI enables the analysis of past data to monitor the organization’s current performance.

Thanks to Cloud Computing, Business Intelligence can now process more data, from more varied sources, and more efficiently than ever before. The Cloud is the technology that has had the greatest impact on Business Intelligence in recent years.

What is Data Science?

Data science is the interdisciplinary field of processing data to extract valuable, future-oriented insights. To achieve this, statistics, mathematics, computer science and business expertise are used.

As a general rule, the aim of Data Science is to answer questions or simulate hypotheses. The various tools and technologies used include Machine Learning and Artificial Intelligence. The Cloud provides the agility, elasticity and processing power required for Big Data analysis.

Data Science vs Business Intelligence: similarities and differences

Business Intelligence vs Data Science share many similarities. Both aim to analyze data and exploit it for the benefit of the company. Like Business Intelligence, Data Science enables the analysis of past data. However, whereas BI enables descriptive analysis, Data Science enables predictive or prescriptive analysis, looking to the future.

In the past, only teams of IT experts could exploit Business Intelligence tools and techniques. One of the major differences with Data Science is that it enables the entire enterprise to access the benefits of data analysis. Business Intelligence is more generalist, through descriptive analysis reports.

With the rise of self-service solutions, all employees will soon be able to access centralized data repositories and automated tools to extract and exploit information. Data scientists, for their part, will be on hand to operationalize the data and support non-technical users. According to a report by Research and Markets, the self-service BI market could reach a value of $7.3 billion by 2021.

As mentioned earlier, one of the main differences of Data Science is that it is adapted to handling massive and complex data. This is not the case with traditional BI platforms, which only offer “retrospective” knowledge. Data Science, on the other hand, enables reactivity and proactivity.

The use of AI, and more specifically Machine Learning, also represents a major difference between Data Science and Business Intelligence. It is precisely machine learning libraries that enable the automation of data analysis.

Data Science is also about answering specific questions. As a science, it aims to verify a hypothesis through analysis. Business Intelligence is more generalized, with descriptive analysis reports.

While Business Intelligence relies primarily on analytical tools, Data Science also encompasses data management, governance and visualization solutions.

Data Science vs Business Intelligence: two complementary disciplines

Many experts see Data Science as an evolution of Business Intelligence. Business Intelligence offered solutions to the problems of the present, while Data Science provides avenues for the future.

In addition, Data Science has enabled decision-makers and managers to take advantage of data analysis autonomously, thanks to self-service tools. Again, this is a real improvement.

However, the two fields are also complementary. BI experts can prepare data for data scientists, suggest paths to follow, or help them create powerful predictive models.

Within an analytics team, the Business Intelligence expert can deliver analytical reports on current trends, while the Data Scientists develop solutions for the future. Together, they can gradually build a powerful analytical platform that all employees can draw on.

On a single project, the BI expert may look at past data to identify successful projects and customer profiles. Based on these clues, the Data Scientist can develop different hypotheses and use Machine Learning to predict their probability of success.

What does the future hold for Business Intelligence and Data Science?

Over time, Data Science has overtaken traditional Business Intelligence. Its predictive analysis capabilities are proving far more useful for business than the descriptive analyses offered by business intelligence.

Faced with the massive increase in data volumes, a single computer no longer offers sufficient storage capacity and processing power. In fact, Business Intelligence and Data Science increasingly rely on the Cloud, and this trend is set to become even more pronounced in the future.

Over time, Data Science has overtaken traditional Business Intelligence. Its predictive analysis capabilities are proving far more useful for business than the descriptive analyses offered by business intelligence.

The Cloud offers unlimited storage capacity and processing power, at low cost, and with a particularly appreciable elasticity. Data ingestion from a multitude of sources is also facilitated.

In the future, we can also expect to see increased use of artificial intelligence and machine learning. As these technologies continue to evolve, they will become increasingly useful for data analysis…

Now you know the differences, similarities and complementarities between Business Intelligence and Data Science. For more information on the subject, take a look at our complete dossier on Data Science, or get started with our Data Scientist training course!

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