Data Science for Business Intelligence

The knowledge of the market dynamics on various scales is essential for corporate success. To develop this business intelligence, the more recent discipline of data science shows us efficient techniques. For leaders, it is important to understand the relationship between business intelligence and data science. 


We begin with learning the distinguishing characteristics of data science and BI. Then, we look at the technicalities of the same. Finally, we will understand how these two are related to each other. 


Part 1 – Clarifying How Business Intelligence and Data Science are NOT the Same 


There are multiple similarities between data science and B.I. Therefore, everyone gets confused when discussing any of these methods. By the end of this section, you will have no doubts about their distinct attributes and common features. Later, a brief about technical details will show you how B.I. and data science are closely related. 


#1 | Data Science and Business Intelligence are Different Concepts 


In intelligence development, the companies want to maintain the knowledge of the recorded events and outcomes. So, business intelligence or B.I. is more static. B.I. focuses on the past and the relevant record-keeping. 


At the same time, data science focuses on the future. To predict the possibilities, the profession of data scientists relies on mathematics, statistics, and computers. After this process, the analysis reveals the hidden patterns in the data. 

In this way, B.I. and data science differ from each other. In fact, there are more dissimilarities when we look at the technical differences. 


#2 | Overlapping Aspects of Business Intelligence and Data Science 

Both methodologies need data. And the users must handle the data input carefully. Otherwise, incorrect information is present in the output. BI and data science depend on the data for suggesting smart decisions and pattern recognition. 

In the field of business intelligence, the interpretation of data is a significant activity. And this statement is also true for data science. 


Part 2 - Type of Analytics Used in B.I. and Data Science for Business 


Since business intelligence and data science are not completely the same ideas, the analysis process is different for both. In this section, we must learn about the different analytical approaches in each. 


#1 | Business Intelligence Utilizes Descriptive Analytics 


As stated earlier, business intelligence has the attributes of a news reporter. It deals with the record-keeping and interpretation of observed data points. This method uses “descriptive analysis” to answer the simple questions about the historic data. 


For example, what was the quarter-on-quarter revenue variation from a preceding fiscal year? Did it increase? Or has it decreased? Ask these questions, and BI must return the description of what had happened in that period. 

This indicates that business intel functions well with structured data and detailed queries. 


#2 | Data Science Involves Predictive or Prescriptive Type of Analysis 


We have established that data sciences explore and envision future outcomes. It is true that such a process uses existing data. But it is also correct that the output of data science is not limited to the description of past events. 


Here, we must note that predictive analytics are present in the data science discipline. You can call it “prescriptive analysis” as well. 


Now, the future is uncertain. As such, data science must process structured as well as unstructured data. It can handle more dynamic queries if compared to the business intelligence process. 

Ask it to develop a probabilistic index to detect the employees who are about to resign. This type of question might not include a specific duration. This query does not detect any group of employees intrinsically. And the output is a possibility of events that could happen anytime in the future. 


Part 3 – How are Business Intelligence and Data Science related to each other? 


Based on the above discussion, you have learned that BI is effective for more technical persons. It can process structured data and precise queries. And it is effective when talking about how the business has been performing for a while. 


But the data collected for BI can be used for data science. Also, you can feed the unstructured data to data science services. Then, the data scientists can perform structured or unstructured queries. Yet, data science will generate meaningful insights irrespective of the data structure. 


In conclusion, both BI and data science must co-exist to understand the life cycle of your enterprise. The methods of data science help you predict the future of your business. So, it is valuable for innovative engineering. Business intelligence is suitable for technical meetings for discussing past performance. 


Summary 


Both data science and BI complement each other. Thus, you must not choose just one of these two. Business intel and data science are two distinct processes with overlapping components. Their inputs can be the same, but outputs are unique for data science and BI. 


At SG Analytics, the research, and analytics firm, you get innovative support for creating and maintaining a data science ecosystem. And we are also efficient when a client needs business intelligence services. 


Contact us if you want these services for your business growth. 

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