The Differences between Data Analyst, Business Intelligence Developer, Data Engineer and Data Scientist

Data Scientist and Data Engineers are only two among the most common requirements in Business today. It is not a new degree in college, in fact, the core of the roles have been around for long years.

In traditional parlance, any person who can do the analysis of data are called data analyst. Any person who helps the data analyst to create the backend platforms are called Business Intelligence Developer.

With the vast amount of data, big companies and corporations have come up to conceptualize a new role such as Data Scientist, Data Engineers, Business Intelligence and the like.

Here are my understanding of the different roles that exist today such as Data Analyst, BI Developer, Data Scientists and Data Engineer

Data Analyst

Data Analyst is experienced professionals who can extract or query data. They can also process the data, provide reports, summarize and visualize data. They do understand how to leverage the existing tools and use the appropriate methods to solve the problem. They can also provide ad-hoc reports request and provide stunning charts.

The only thing that can’t provide is the requirements for BIG DATA. Their capability is limited to only samples of data and they are not expected to analyze big data. Typically, their skills are not fit to develop algorithms for a specific problem.

Skills: As a Data Analyst, any person is expected to have a basic understanding of Statistics, data manipulation, data visualization and among other is exploratory data analysis.

Tools: Microsoft Office, SPSS, SPSS Modele, SAS, SAS Miner, SQL, Tableau

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Business Intelligence Developers

Business Intelligence Developers are professionals in the field of understanding data and they can interact with other business units to understand the business requirements. They should understand the reporting needs of every business unit.

Because of this capability to interact with other business units, a BI Developer can collect the requirements, do the design and build a BI solution for the company. They can now help improve or modify the existing data warehouses, ETL packages, cubes, dashboards, and analytics reports.

Business Intelligence Developers are data experts that interact more closely with internal stakeholders to understand the reporting needs, and then to collect requirements, design, and build BI and reporting solutions for the company. They have to design, develop and support new and existing data warehouses, ETL packages, cubes, dashboards and analytical reports.

Additionally, they work with databases, both relational and multidimensional, and should have great SQL development skills to integrate data from different resources. They use all of these skills to meet the enterprise-wide self-service needs. BI Developers are typically not expected to perform data analyses.

Skills: As a BI Developers, any person is expected to have a basic understanding of the ETL, Develop Analytic Reports, Crystal Clear Reports, OLAP Cubes, Web Intelligence, Web Scraping solutions, Business Design.

Tools: Tableau, dashboard tools, SQL, SSAS, SSIS and SPSS Modeler.

Tools: DashDB, MySQL, MongoDB, Cassandra

Tools: Data Science Experience, Jupyter, and RStudio.

Data Engineer

Data Engineers are data professional similar to data analysts, however, they are more capable to handle Big Data. They do prepare Big Data Infrastructure and then turn over the responsibility to Data Scientist which in turn capable to analyze Big Data.

Data Engineers are so-called Software Engineers who are responsible in Designing, Building and Integrating Data from various resources and of course, managing Big Data Infrastructure.

As a Data Engineer, they can create complex queries and make sure that it is easily accessible to all business unit, infrastructure should be working smoothly and the goal is to optimize the performance of big data ecosystem,

On top of big datasets, Data Engineers can also do some ETL (Extract, Transform and Load) and create big data warehouses that can be used for reporting or analysis by Data Scientist.

As a Data Engineer, they are more focus on the Design and Architecture of Big Data platform. They don’t have any knowledge of Machine Learning or analytics for Big Data.

Skills: Hadoop, MapReduce, Hive, Pig, Data streaming, NoSQL, SQL, programming.

Data Scientist

A Data Scientist is the sexiest and most flexible job in the 21^st century and is expected to dominate the world of business.

They are professionals in the field of Statistics where they apply and utilize it to create a Machine Learning Algorithm. They can handle analytics approaches and strategies to deal with business problems.

With the availability of Big Data, Data Scientist is of a great help to the organization because they can turn this data into something valuable and actionable insights.

Data Science is not a new field, data scientist are professionals with advanced skills and competencies than that of a Data Analyst. Aside from the basic role of Data Analyst, these people are expected to have a very strong skill in programming, some strong skills in creating an algorithm, can able to handle and understand BIG DATA and some knowledge in different domains of skills.

As a Data Scientist, you are expected to interpret and deliver the results of the investigation of the certain problems by the use of visualization, by creating or building a data science application or some data storytelling to exhibits solutions to a problem.

A Data Scientist can do problem-solving wherein traditional and modern methods of data analysis are considered a capstone of their skills. They can easily provide a solution to a problem and they can easily discover patterns in the data.

Moreover, being a Data Scientist, you are not only bound to create solutions to a problem. Even when there is no problem, as long as BIG DATA is available, you are expected to be creative by exploring the data and come up with a formulation of questions and provide interesting insights subject for investigations.

A very strong and well-equipped skills Data Scientists are expected to have a very broad knowledge of the different Machine Learning Techniques, Data Mining, Advance Statistics and Big Data Infrastructures.

Skills: Python, R, Scala, Apache Spark, Hadoop, machine learning, deep learning, and statistics.