Data Science Vs Business Intelligence

Data science and Business Intelligence are big topics in the data world as they empower organisations to understand their data and leverage it to make business decisions. Data is the lifeblood of many organisations as it helps them understand their business, customers, and current activity and plan, forecast and predict how the business will evolve.

In this article, we will compare Data Science Vs Business Intelligence and highlight key differences between the two disciplines of Data Engineering.

What is data science?

Data science is the process of gaining intelligent and informative insights based on data through various methods including AI (Artificial Intelligence), Advanced Analytics, Cloud Computing and ML (Machine Learning). It can be invaluable in understanding the ‘why’ things happen within a business and what can be learned from that.

What are the benefits of data science?

These are the pros of data science for all organisations:

• Gain powerful insights to help understand the business and make well-educated forecasts and predictions.
• Find gaps to spot opportunities for growth with additional offerings of products and services with analysis of customer data.
• React quickly and efficiently to changes in the market with real-time data analysis and reporting.
• Better serve end customers and improve customer service with insights and knowledge based on your existing client base.  
• Inform marketing and sales approach to drive high ROI across all activities.

What are the challenges of data science?

There is a wide variety of challenges that data science offers businesses, however, there are some challenges to consider:

• Costs – investing in data science can be costly to set up and maintain, especially if you are carrying out all activity in-house
• Data volume – The majority of data scientists will work with large data sets that come from a wide variety of sources which can be complex and challenging in itself.
• Data security and compliance – Another challenge organisations will find when it comes to their data science is ensuring the data is secure and safe.
• Finding the right talent – Data scientists are high in demand, therefore it can be difficult to find the right talent.

What are key technologies used in data science?

There are a wide variety of key technologies used in data science offered by world-leading brands and they include:

• Amazon Web Service – AWS technologies that can be used for data science include Amazon Machine Learning (AML), Amazon Redshift, Amazon S3 and Amazon Recognition.
• Python – Python is one of the leading programming languages in the world and it is particularly useful for data scientists as it can analyse large volumes of complex data.
• Microsoft Azure – Offers a wide variety of data processing, machine learning and data storage solutions.

What is Business Intelligence (BI)?

Business Intelligence drives smart decision-making by leveraging business analytics, data mining and data visualisation and reporting. Business Intelligence is an umbrella term which refers to the journey of data which includes the collection of raw data, data processing and storage, data analysis, data reporting and decision-making.

What are the benefits of BI?

There are multiple benefits on offer for businesses that invest in BI and they include:

• Insights – Businesses can gain insights which can drive profitability, improve performance, optimise operations, gain a competitive advantage and more.
• Data visibility – Most organisations deal with huge volumes of data. With BI, they can gain visibility into their data to better understand your business and customers.
• Adapt to change – With real-time analytics, because can remain agile and adapt to change quickly and efficiently.
• Confidence in decision-making – Making decisions based on data provides peace of mind and a high chance of success. 

What are the challenges of BI?

Some challenges to consider when it comes to investing in BI for your business include:

• High costs – Businesses will need to invest considerably in their BI operations to gain the benefits on offer.
• Stakeholder buy-in – Due to the high costs involved it might be difficult to secure stakeholder buy-in
• Resource and time – There is a significant resource and time required to set up your BI infrastructure. However, you can outsource some of the processes to save time, resources and costs.

What are key technologies used in BI?

There are a number of key technologies that can be adopted for BI, however, we will focus on the technologies used for the data reporting and salutation of data. Some of the most popular BI tools include:

• Microsoft Power BI
• Tableau
• Periscope
• Pentaho

Read the full article on top data analytics reporting tools.

Data science Vs Business Intelligence – Key difference

The top-level differentiation between data science and BI is that data science will predict and forecast future trends with technologies such as AI and ML. However, BI will focus on the analysis of past events to make predictions and drive decision-making. Both are crucial to the success of many organisations dealing with large volumes of data.

“Without big data, you are blind and deaf and in the middle of a freeway.” — Geoffrey Moore (Author)

Ardent, data engineering services

Ardent have been delivering data engineering excellence for over a decade to drive data science and BI for our clients across the globe. Read about our client's success with data science and BI:

• Improving data turnaround by 80% with Databricks for Fortune 500 company
• Ensuring data availability with expert operational monitoring and support
• Building robust, secure and scalable data pipelines with AWS infrastructure

If you are looking to invest in your data science or BI and want to drive intelligent decision-making for your organisation, we can help. Get in touch to find out more or explore our data engineering services. 

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