Business Analyst and Business Analytics Professional – They are NOT the same! Find out how.
A business analytics professional? You mean a business analyst, right?
They are not the same.
It’s okay. Many people get confused about these two terms. They are often used interchangeably. Companies and start-ups also seek out a business analyst, when they actually are looking for a business analytics professional. This causes confusion among analytics enthusiasts who are looking to get into business analytics. So I thought I will explain the difference between these two job roles in a simple way.
Let’s get started…
Who is a business analyst?
The International Institute of Business Analysis (IIBA) defines Business Analysis as the discipline of identifying business needs and determining solutions to business problems.
Conventionally, a business analyst coordinates between the technical team and the client. The client could be either external, with requirements to solve a specific problem, or the internal team that needs to work with the technical team. The technical team has the capability to either build a product or deliver a service.
The business analyst ensures that the product or service delivered by the technical team meets the current requirements of the client. He/she collaborates with the internal and external stakeholders in the design and implementation of the product or service.
Who is a business analytics professional?
As you must have realized by now, a business analyst doesn’t have much to do with data and is more concerned with functions and processes. On the other hand, a business analytics professional is mainly concerned with data and reporting.
Here are three basic elements that separate a business analytics professional from a business analyst:
- Programming skills
A Business analytics professional works with structured data and uses SQL to retrieve data from databases. If the management wants some high level metrics about their organisation, he or she will write SQL queries to extract and analyse data from a transactions database and prepare a set of visualizations.
An analytics professional also possess a good knowledge of data science programming languages. They are good at manipulating, analysing and visualizing data using either of these two programming languages.
On the other hand, a business analyst has nothing to do with data. They focus more on the processes and functions. Key business analyst value propositions include business value maps, context, vital pain points, key performance indicators (KPIs), model requirements, process and IT re-engineering, testing and calibration, and so on. A business analyst is knowledgeable in development frameworks like SLDC. They use Excel to do analysis and quantitative calculations – they don’t use programming skills to perform calculations.
2. The ability to tell story with data
A business analytics guy should be able to communicate the insights he has derived from the raw data. He needs to communicate in simple terms to the end stake holder, because usually the end stakeholder would be a business-centric and/or non-technical person. He should avoid using technical jargons and communicate the insights he has derived in the simplest terms possible. The stakeholder might be an external or internal client. Usually, they are non-technical business professionals who have the authority to make decisions.
Business analytics professionals are also good story tellers and they use advanced tools like Tableau, R and Ds.js to communicate their story or findings to the end stakeholders. For example, if a movie producer wants to better understand film industry trends before releasing a movie. An analytics professional will explore a data set and build dashboards to answer a set of questions and tell a tale with data.
On the other hand, business analysts are also good communicators. They use power point, word, and excel to create visual models such as work-flow diagrams or wireframe prototypes. They are also good at creating clear and concise technical documentations. Whereas, they don’t build custom dashboards using advanced data visualization tools like business analytics professionals do.
3. Analytical problem solving skills
Business analysts use a variety of techniques to analyze the problem and solution. They conduct analysis and deconstruct the problem or solution using different methods. Examples of this include use cases, business process models, and decision models.
Business analytics executives on the other hand, use statistics, predictive analytics and logical thinking to solve business problems.
Let’s see two examples of how they solve business problems:
- Bank A receives an influx of loan applications. A business analytics professional will build and apply a model to provide a recommendation on which loan applications the bank should lend to.
- If a home-goods manufacturer wants to predict expected profits from a catalogue launch, an analytics professional will apply a framework to work through the problem and build a predictive model to provide results and a recommendation.
From the above post, it is evident that both these functions are crucial for a successful business model. The value of analytics comes from the business decisions it enables and automates. A business analyst will always start with the business questions first, and not the data. They are also domain experts who take care of the project from the beginning to the end. Conversely, a business analytics professional starts to solve the business problem with the data first. They care about the source of data, format of the data, and how to retrieve data. Data is the raw material for them.
A business analyst works closely with a business analytics professional to ensure that the final project is delivered successfully.
Now that you have read this post, I hope you have a clear understanding of these two roles. If not, you can always drop in a comment to clear your doubts!
Latest posts by Manu Jeevan (see all)
- 50 Amazing big data and data science quotes to inspire you - August 1, 2017
- Top mistakes beginners make while learning data science (Part 1) - July 20, 2017
- 6 steps to building a big data culture that works - July 17, 2017
Follow us on