Why a Business Intelligence or Business Analyst background will help you become a better Data Scientist

On a daily basis the world generates close to 2.5 quintillion bytes of data. The main challenge companies experience is to manage the large volumes of data. Hence, they are in a constant lookout for data professionals with overall skills in collecting, storing, processing, analysing, developing business intelligence and providing actionable solutions from the vast data. In the past, the terms Business Intelligence Analyst, Business Analysts and Data Scientists were used interchangeably. However, in the recent times, each role has become distinct from the other.

While Business Intelligence Analyst (BIA) and Business Analyst (BA) have many similarities, the only slight difference between the two is that; BI makes use of the past and current data to optimise and provide solutions for current success. On the other hand, BA analyses the past and present data to prepare businesses to make future decisions. This blog will dive deep and uncover the key differences and help you understand the difference in roles. Later on you can read about how a BIA and BA differ from a Data Scientist.

First, let’s cut the confusion and understand the above mentioned roles separately:

1. What’s the difference between a Business Intelligence Analyst (BIA), a Business Analyst (BA) and a Database Administrator (DBA)?

Business Intelligence Analyst – The role of a Business Intelligence Analyst is more technical than a BA. As a BIA you will be working on several activities such as data mining, online analytical processing, solving and reporting using various software in order to analyse the company’s existing raw data to make effective business decisions. As a BIA you will mostly be concerned with what and how rather than why.

Knowledge and Skills of a BIA – Coding skills, knowledge of data analysis tools and programming languages such as R, SaS, Python, etc.

Related job roles – Technical Business Analyst, Business Intelligence Reporting Analyst, Business Intelligence and Automation Analyst, Data Warehouse Specialist, etc

Business Analyst – A Business Analyst typically identifies and defines business problems using various BA techniques to provide insightful solutions to organisations, helping them achieve their goals and objectives. As a BA your scope of work will be broader than a BIA as business analysis can be applied to any type of problems or opportunities including Business Intelligence.

Knowledge and Skills of a BA – Domain knowledge along with the knowledge of how to collaborate with stakeholders and SME’s along with technical skills such as testing, Data modelling and data management.

Related job roles– Implementation Consultant, Business Readiness / Change Analyst, Process Analyst, IT Systems Analyst, etc.

Database AdministratorDatabase administrators are IT professionals responsible for managing, storing and organising data using software. The role involves making the data accessible to users while maintaining security of the data from reaching unauthorised individuals. In comparison, BIA and BA roles are broader than Database Administrator roles in terms of its scope and application while making business decisions.

Knowledge and Skills of a Data AdministratorThe requirements for a Database Administrator are – data modelling and design, Backup and recovery, performance management and tuning, Data security, general data management, etc.

Related job roles – Principal Database Administrator, Systems Integrator, DB2 Database Administrator, etc.

2. What’s the difference between Data Science and Business Intelligence?

While both Business Intelligence and Data Science mostly focus on “data” to provide effective business solutions such as; increase profit margins, retain customers, grow into new markets and so on; there is a significant difference between these two terms.

The one major difference between Data Science and BI is  BI can handle static and structured  data, while a Data Scientist can handle a very high-speed, complex, high-volume data coming from a variety of sources using advanced technologies like Big Data, IoT and cloud.

Furthermore, in a traditional BI environment, businesses are forced to take the expertise from the resident Analytics team in order to extract insightful information from the data, but Machine Learning and Artificial Intelligence (AI) powered Data Science has launched Self-Service platforms that provide users to easily access, analyse and extract results from the database without taking the help from the technical teams.

3. Here’s how Data Science has already changed the Business Intelligence Industry and what BI professionals can expect in the next 5 years

Experts have defined Data Science as an evolution of Business Intelligence. When the Business Intelligence Analysts are providing decision support to managers and executives, the Data Scientists are enabling the managers and executives to be Analytics experts themselves.

BI has heavily focused on the use of traditional analytics tools, but Data Science has become more in-demand since it applies a holistic approach to data management. Data Science has combined Data governance, Analytics, advanced Visualisation tools and BI to form a robust system to manage high volume data.

With e-Commerce seeing a steep surge in the recent times, global retail businesses have started to realise that traditional BI has slowly progressed towards Data Science and Machine Learning techniques to transform real-time insights into profitable business solutions.

According to a recent Analytics study conducted by McKinsey in 2016, the author has proved that Data Science coupled with Machine Learning and AI, has overtaken the traditional Business Intelligence, since static or past data is no longer sufficient to predict future business trends and events.

In order to stay relevant with the rapidly evolving big data landscape – various data science and analytics tools, technologies, processes, and experienced data professionals need to work together to extract the most valuable data insights from this abundance of digital data. BI professionals need to pull up their socks and get their hands dirty on the latest BI tools and analytics such as the Augmented analytics to be ready to face the world of Big Data.

4. A background in Business Intelligence / Analytics will help you become trained as a data science & AI professional

When compared to other professions, BIA and BA professionals have an upper hand if they wish to transition into the field of Data Science and AI. Below are the transferable skills, knowledge and tools required to enter into Data Science:

  • Industry related knowledge: Thorough knowledge of the workings of the industry, which may be be crucial to understanding data.
  • Data management tools: As a Business Analyst you would also ideally have experience and knowledge of conducting analysis using various data science tools and spreadsheets.
  • Strong interpersonal and communication skills: You will be confident conversing with top level management and key stakeholders of the company, to communicate complex information in simplified manner.

5. Steps for switching from Business Intelligence to Data Science and Machine Learning

With a rise in the need for Data Scientists and Analysts, Business Intelligence Analysts and Business Analysts might want to consider making a career change to Data Science. Here are a number of steps a BA must follow in order to fill the skills gap and become a Data Scientist:

  1. A data scientist needs to be able to handle statistical data with ease and have strong foundational knowledge about various mathematical concepts related to data science. So, if it’s been a while since you last looked at your statistics book, it’s time you pick it up brush up your skills and knowledge.
  2. Upskilling yourself by getting enrolled into certifications or courses in Machine Learning and Artificial Intelligence (AI) is a great start to becoming a Data Science professional. Machine Learning, algorithms and statistics are some of the major skill requirements for any Data Scientist position.
  3. Strengthen your coding skills. It’s important you have a coding language under your belt. As Data Scientists, its essential to build your own algorithms and systems. If you already know one, push yourself to learn more languages – it will definitely take you to the next level.
  4. In order to get ready to face the world of Big Data, it is beneficial if you have practical experience working in the field. This can be a small project within your company or developing your own side project in your spare time. Real-world experience will help you make your skills stand out on your resume.
  5. Lastly, become a part of the Data Science community. It’s the best platform for you to stay up-to-date with the industry trends and a great place to find connections that will help you land your very first Data Science job.

“Data is the new Oil. Data alone will amount to nothing unless it won’t be found, extracted, refined, distributed and monetized. This was where Harvard Business Review termed Data Scientist as the sexiest job of the 21st century.” David Buckingham

Big Data has transformed the industry. Companies are in need for Data Scientists more than ever. If you are a BA or a BIA looking for a career change into Data Science, this is the best time for you to become trained and fill the skills gap to become an in-demand Data professional.

Find out how the Institute of Data’s vocational training programs can help you kick start your career in Data Science. You can also schedule a call here to speak with a career consultant.

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