Advancements in technology and cloud computing are generating a tremendous amount of data every day. Firms and enterprises are leveraging this spawned data to make better decisions. This is for the benefit of the organization. Companies are witnessing a rise of computer scientists and statisticians. They remain engaged in data-related works. They are making sense of all the data through analytics techniques and tools. There isn't an industry, science, and engineering left untouched by data analytics. That is where the role of data analysts and business analysts comes.
What is Analytics?
Let us first understand what analytics is. Analytics is the art and science of identifying and communicating data-driven insights. It helps stakeholders, managers, and other executives. Enterprises can make informed decisions based on the processed granular data. These data have to go through phases of analytics. Various statistical and mathematical operations get implemented during this analytics phase.
Who are Data analysts?
These data analysts are professionals get associated with uncovering trends & insights from data. They use these to make informed business decisions. They collect, clean, and translate data sets for solving business-related problems. The need for data analysts is in all sectors such as business, criminal justice. They also help other industries like finance, science, healthcare, and governance. Every industry is leveraging them for gleaning insights from data.
The data analysts process data through 5 different steps or phases. These are:
- Identifying the granular data extracted for analysis
- Collecting the data
- Cleansing the data and prepare them for analysis
- Analyzing the data
- Interpreting the results of the analysis
Who are Business analysts?
Business analysts focus on analyzing diverse types of data. They make practical use of them so that from them, data-driven business decisions can be yield. Business decisions are nowadays a prime element to compete with the competitors. Business executives can use the extracted meaningful data of the business analysts.
This helps them to make or change those decisions. Business analytic draws predictions taking cumulative information from data analysts. These data help in classifying problems and get solutions. They are also responsible for guiding businesses. The meaning in these data helps in enhancing processes, and business products.
They straddle between IT and business enhancement, bridging the gap between these two departments. This helps executives make better business decisions. They remain involved with the business executives and leaders. They cater to them data-driven updates for decision-making. Business analysts perform tasks like:
- Assessing business processes through extracted data for cost, efficiency, and other significant metrics.
- Delivering insights with business partners & chief stakeholders
- Preparing necessary and strategic guidance to adjust the process, procedures, and performance improvements.
Difference between Data analysts and Business analysts:
Both data analysts and business analysts work with data. But the main difference lies in what they do with the extracted data. Both work for the similar goal of extracting valuable insights. But each of them works at a separate step of this process. It also happens that both of them have to work in tandem to make informed business decisions. Let us now witness the differences between data analysts and business analysts.
| Data Analyst | Business Analyst |
|---|---|
| Data analysts are more inclined towards gathering and analyzing data for future prospects. | Business analysts fetch the granular. They then analyze these data to aid in improving business decisions. They also take care of the effectiveness of the business decisions. |
| They analyze data. | They analyze client and business needs. |
| They analyze patterns in the data for making suitable business decisions. | They interact with clients, their data, and with project managers. The data they collect from the different sources helps to analyze their needs. |
| They have to be proficient in programming languages like R, Python, Spark, and SAS. They also need to have skills in frameworks like Keras and TensorFlow. | They need to have sound knowledge of R, SQL, Excel, Tableau, etc. |
| Data analysts perform predictive & prescriptive analysis. | Business analysts perform retrospective and descriptive analysis. |
| They grab the data from the data warehouse. Then they perform data cleansing, analysis, and data visualization. | Data analysts model the data and putting it in a schema. Then they extract reasoning for impactful business decisions. |
| Data analysts enjoy playing with numbers and graphs. They bring to the table impressive and effective ways of solving problems. | Business analysts enjoy operating in the corporate world and with the business. They have to understand the need of the company for future progress. |
| Data analysts perform deep technical analysis of the data. This helps others identify and communicate with the data. They also use some visualization tools to visualize the meaning from it. | Business analysts are more inclined towards identifying business problems and solving them. This makes the business more effective and profitable. |
| It is a simple data processing domain with steps like probing, cleaning, modifying, and modeling data. They do this with the aim of discovering valuable information out of it. | It is a research domain of identifying business requirements. These professionals also determine solutions to problems. |
| This requires specific technical knowledge along with mathematical and statistical knowledge. | This requires domain knowledge, business understanding, communication, analytical thinking. They also need problem-solving skills and decision-making abilities. |
| Data analysts use tools like Datapine, RStudio, Python, MySQL Workbench, SAS forecasting. Other tools they use are Erwin Data modeler, Talend, Apache Spark, Tableau, etc. | Business analysts use tools like Excel, SQL, Tableau, Trello, Rational Requisite Pro, Smartsheet, ClickUp, and MS. Visio, etc. |
Educational Background and Salary Structure:
Data analyst:
Most entry-level data analysts must have a bachelor's degree. A major in data science, computer science, statistics, and mathematics is an advantage. Also, he/she can have major in finance, economics, or management information system. A sound understanding of calculus, statistical formulas, database systems, and data handling tools is also a must. Many organizations prefer to have a certificate or certification also.
- Entry-level salary: ₹325,616 approx.
- Mid-level salary: ₹635,379 approx.
- 9 to 12 years experienced salary: ₹852,516 approx.
Business Analyst:
Entry-level data analysts must a bachelor's degree in a related field. A major in business studies, finance, management with a sound understanding of SQL and other relational databases is a plus point. Also, it is helpful if the aspirant can work with data handling tools, frameworks, and programming languages. The aspirant should also have good written and verbal communication. This makes the professional a good presenter.
- Entry-level salary: ₹350,000 approx.
- Mid-level salary: ₹5,27,712 approx.
- Experienced salary: ₹830,975 to ₹12,09,787 approx.
Conclusion:
Both data analysts and business analysts work in tandem with the data. The goal is to extract valuable insight and communicate with what the data wants to reflect. These two fields have a long-lasting career growth in dealing with data. This vertical is in demand for every sector and is not going anywhere in this century. But, every interview asks the difference between data analysts and business analysts, aspirants should have a clear idea of how they differ from each other.