What are the definition and differences?
Business Analyst
First, let us start with a business analyst. They identify technical solutions for complex business problems. They can work in different various industries such as banking & finance, transportation, telecommunications, healthcare, IT industry etc. A Business Analyst can also be defined as the agent of change who identifies new opportunities for business to leverage on technology. Business analysts often specialize in different roles such as system analyst, functional analyst, agile analyst, service request analyst based on the area of interests. To give an example, a service request analyst handles user queries and responsible for system enhancements.
Data Analyst
Whereas, a Data analyst uses system & tools to check how businesses utilize data to make more informed decisions. It maybe highly similar to the role of a business analyst however, data analyst works more directly with the data itself thus more technical in that aspect . Data analyst is mainly responsible to identify important business questions, apply statistical techniques, perform complex data analysis to extract useful information and develop conclusions.
They are also responsible to protect data of an organization, ensures that data repositories produce more consistent or reusable data.
Data Scientist
Now, a data scientist is to analyze and interpret raw data into business solutions using machine learning and algorithms. It is more technical of the two mentioned above as most data scientist jobs ask for a master’s degree in data science or a related field.
A data scientist performs similarly as a data analyst, but possess more technical techniques such as advanced algorithms and statistics expertise. They can build, train, and use ML and deep learning models to understand data – skills that Data analysts don’t possess. These skills make data scientists immensely valuable in interpreting solutions from open ended questions and also identifying hidden insights.
Data Engineer
A data engineer is one who manages and prepare big data that is then analyzed by data analysts and scientists. They are responsible for designing, building, integrating, and maintaining data from several sources hence Data engineers need advanced software development skills, which are not as essential for data analysts and data scientists.
A data engineer is the first to come in to build data pipelines like a front liner. They do not analyze the data that they receive and store however they make sure it is available to the data analyst / scientists. They also improve on these pipelines regularly to make sure the data stored for analysis is accurate and accessible.
Database Admin
A database admin are main person in charge of the use and proper functioning of enterprise databases such as the backup and recovery of business-critical information. It is very critical as a Database admin as everything about the business’ operation relies on properly functioning, designing and modeling databases.
They also usually perform data backup and recovery, as well as security and disaster management.
IT
An IT Systems Admin are like the generalists who keep an organization functioning. They’ve typically have a little knowledge of all of the systems in a company and have the skillset and tools to figure out anything themselves with an internet connection. These resources are often tasked with keeping the lights on, cloud management, security, office, email management, or other server-side admin functions and scripting language.
Project Analyst Manager
Last, but not the least, is the Project analyst manager. A typical analytics manager usually is experienced in overseeing all the above mentioned operations and assigns duties to the respective team leads based on specifications. Analytics managers are typically well-versed in technologies like SAS, R, SQL etc. Being an analytics managers also need to have good interpersonal and social skills as well as good leadership qualities to lead the project. They need to be creative thinkers who can easily manage a team.
Conclusion
Although these scope and variety of skill sets required may appear confusing as there are many options for companies that want to get started. Davoy offers multiple engagement options that can adapt to virtually any project size at any time and any pre-existing data analytics team size and composition.
Contact us to learn more about how Davoy can help you get started with various role it the department that enable you to have a successful data projects at your company.
