What is the difference between a Business Analyst and a Data Scientist?
Prior posts have discussed data science in detail by distinguishing a data analyst from a data scientist, a data engineer vs. a data scientist, and the difference between computer science and data science. As discussed in those articles, capturing big data, analyzing it, and using statistics to explain trends is a day in the life of a data scientist. Data scientists use programs like R and Python for their analysis and a large part of their job is graphically communicating their results.
Business analysts deviate from data scientists because their focus is on the business model itself. While a data scientist approaches business through a statistical lens, business analysts approach business with a more integrative approach.
Doctoring the Business Model
A business analyst’s job is like that of a doctor in that it assesses a business model as if it were a patient.
- Like a doctor, a business analyst is well trained in the field. In health, pediatricians are child specialists and cardiologists are heart specialists. Similarly, in industry, a business analyst for a car company is an expert on cars while a business analyst for a fast food restaurant is an expert on the fast food industry.
- A business analyst collects data on profits, losses, and growth to write reports, just like a doctor collects height, weight, and temperature to record patient information.
- Business analysts communicate with business departments as well as consumers and stakeholders in order to evaluate whether or not the business plan matches the company goals. This is similar to how a doctor communicates with nurses, office staff, and patients themselves to not only help diagnose and treat patients, but also help to make the practice run smoothly.
The business analyst will have a good understanding of the business model and the market it serves, including demographics, geographical location, and nearby competitors. The analyst would look through historical data, gaining insight into how the business has performed over time. They will also identify seasonal trends and advise management about such trends.
Consider the example of a fast food chain. A business analyst can look into the specifics of one store in the chain, assessing, for example, why sales are falling behind compared to historical sales at the same store or to current sales at other stores within the chain. Or, it can look at specifics for the whole chain, and how they compare with those at competing chains.
Business analysts use specific tools and models, such as time series, to perform analysis. They gain insight from past data and use it to assess future business performance. Think about Wall Street and quarterly reports by US companies. The published expectations are examples of final products business analysts derive via looking at past data, market trends, supply and demand, and so on.
After all the data collection, communication, and evaluation, the business analyst must then write a report. Excel should be utilized to generate clear graphs and charts displaying the information. Microsoft PPT and Word are also critical for a business analyst, as the reports need to be shared with personnel and must contain easy to understand models. The report will initiate a dialogue between the business analyst and the decision-makers within the company on the current business model and whether or not it should be modified.
Sometimes, a business analyst is expected to make software recommendations to address needs identified while assessing the business model. In this capacity, a business analyst must be able to work with programmers to communicate and modify systems as well as with users in order to train and address any questions or problems that arise.
Data Scientists and the Market Pulse
Data scientists are different than business analysts in that they are not necessarily experts in a specific field, although they certainly can be. Data scientists are not tied to a specific business. Instead, they can use their skills and tools seamlessly to otherwise unrelated domains. They are data experts, not field experts, and instead of evaluating a business like a doctor or business analyst, the data scientist is more like a heart rate monitor. Data scientists take the current pulse of the market.
For example, if the same fast food chain above were to hire a data scientist, the scientist would want to capture every single sales event, including day of the week, food ordered, time ordered, and consumer information. Data scientists aren’t after summaries and overall profits like business analysts are. Instead, data scientists are looking to capture specific details in order to identify trends and patterns in the data.
A data scientist would attempt to integrate every single data event into a mathematical model that provides a scaffold for future event predictions. They know the advanced math and machine learning programs needed for such prediction models. For example, returning to the restaurant example, a complex statistical pipeline might reveal that Wednesday nights have been consistently low in sales. A very primitive math model to match the data to would be a linear model with a valley in the middle, assuming that all other times are exactly equal in sales. In other words, assuming that every day of the week was constant, the math model would be a horizontal line and there would be a dip in the line, like a sin curve, smack dab in the middle.
Having such a model in mind, data scientists can brainstorm strategies to boost Wednesday evening sales such as a “happy hour” or a “buy one get one free” promotion. A data scientist could then run simulations or compare to other franchises’ sales when the promotions are implemented to predict which method would result in the largest profit margins.
In addition to identifying such trends, a data scientist is also responsible for presenting the results and options to decision makers. They usually use statistical programming with visualization packages, although excel can be used as well.
The Tools of the Trade
The world of a business analyst is business-model centric. Either they are reporting, discussing, or modifying the business model. Not only must they be proficient with Microsoft Office, but they also must be excellent researchers and problem-solvers. Elite communication skills are also a must, as business analysts interact with every facet of the business. They must also be “team players” and able to interact and work with all departments within a company.
Data scientist’s job descriptions are much different than business analysts. They are mathematicians and understand programming language, as opposed to report writers and company communicators. They therefore have a different set of tools they use. Utilizing programming languages, understanding the principles of machine learning, and being able to generate and apply mathematical models are critical skills for a data scientist.
The commonality between business analysts and data scientists is that both of them require generating and communicating figure-rich reports. The software used to generate such reports may be the same between the two different positions, but the content of the reports will be substantially different.
Which is right for you?
If deciding between a future career between a business analyst and a data scientist, envisioning the type of position you want should steer you in the right direction. Do you like interacting with people? Do you like summarizing information to make reports? If so, you are more likely to be happy with a business analyst position than a data scientist because data scientists work more independently. Data scientist’s are also more technical in nature so if you have a more technical background a career as a data scientist might be for you.