Data science and BI – the difference explained by BI consultants

As BI consultants, we’re often asked what the difference is between Business Intelligence and data science.

While Business Intelligence and data science share similar values and concepts, the difference between the two can be monumental.   

Both Business Intelligence and data science bring a wealth of advantages to enterprises seeking to make the very most of their data. Both harness data to draw greater strategic insights, and both landscapes share critical components such as data management and data analysis.   

But how do each of these approaches differ, and what advantages can each promise to bring users, and teams, throughout an enterprise?   

In our blog below, we’re going to answer these pressing questions in more detail – as well as discuss how we, as a consultancy, support each solution to provide maximum value.  

Understanding Business Intelligence  

Making use of structured and semi-structured data, Business Intelligence processes involve applications such as SQL Server, Azure Data Factory, Azure Synapse and Power BI to better understand historical data and what has happened. As a result, it can inform current strategies with critical insights into past events, such as:   

  • How are marketing promotions affecting sales?   
  • What are the peak times that sales are most often made?   
  • Which demographics are most likely to convert?   

It’s important to understand that both Business Intelligence and data science make use of the benefits of data warehouses, data lakes, and big data – this being unified and centralised data that promise reliable and secure results.   

Defining data science  

While BI prioritises the analysis and evaluation of historical data, data science focuses primarily on modelling future events, trends, and outcomes. Doing this involves Artificial Intelligence, Machine Learning, and other fields of data science. Data science enables enterprises to maintain a market-leading position. With access to future insights, users can more effectively enhance strategies and decision-making.   

There are different ways an enterprise can benefit from data science. Some specific use cases include:  

  • Forecasting how sales and seasonal trends will affect the stock level of objects in high demand  
  • Automatically targeting personas with promotional items or advertisements that are determined to succeed  
  • Creating clusters of personas based on identified behaviour, before targeting them with custom messaging  

Requiring more sophisticated techniques, as well as more data, time, and budget, data science insights can be difficult for businesses to reach.  

However, the awareness that they generate throughout companies can often lead to some of the most advanced, reliable, and proactive strategies. In a time of crowded markets and agile consumers, these strategies are more vital than ever.   

Understanding Business Intelligence  

Both BI and data science insights can enhance perspectives and strategic decision-making. Understanding each discipline’s techniques, and the processes involved, can clarify which approach is best for tackling the issue at hand.   

The BI process: fulfilling objectives  

To begin BI analysis or reporting, pre-existing data and business logic are needed. Often, an end user will approach a BI expert with a request to provide insights that may further a certain objective or fulfil a certain requirement. After that, BI tools, ranging from those present in Microsoft Azure to MS Power Platform, may be deployed to pull in all relevant data and ascertain a particular insight as a result.   

As a final step, the verified result may be pulled into a reporting dashboard, powered by a tool such as Power BI, where it can be accessed simultaneously by multiple users enterprise-wide, mitigating the chances of silos occurring.   

Data science analytics: Explorative questioning  

Data science analytics often begins with a question.   

These questions may often be ambiguous in their goal, and will often require performing data analytics on various datasets both structured and unstructured.   

Data scientists can explore the relationships between all data, rather than only that in certain formats, formulate an answer that provides insights. Once data scientists have uncovered the most effective way of determining the answer, they can create an automated process that ensures insights remain up to date long into the future.  

How we can help  

As a specialist Business Intelligence consultancy with a range of additional expertise, we can facilitate and deploy a wide range of BI and data science capabilities that are fully attuned to the needs of your business.   

 With an agile, outsourced approach to Business Intelligence and data science, we can support enterprises at any stage of growth to gain critical value from their data, their architecture, and, ultimately, their strategies. 

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