In this article, we are going to discuss various roles that exist inside the data world. We will also understand data analytics and the types of data analytics!
Since we’ll be working with data in Power BI, I felt it would be beneficial for us to grasp knowledge on various data roles. We are already aware of how Power BI is a very powerful analytical tool. Let us first explore and understand the meaning of analytics, data analytics to be precise. We will also understand the many types of data analytics.
Data analytics is the science of evaluating raw and unstructured data to anticipate various outcomes. It is concerned with the collection, cleansing, transformation, and modelling of data in order to uncover hidden patterns and useful information. It is concerned with storytelling of data.
There are around 4-5 types of data analytics.
The following are the different types of data analytics:
This basically depicts what happened during a certain period of time or in the past. For example, has ABC organization made a consistent profit in the last six months? Or did it suffer massive losses? What happened in the past, exactly?
This explains why things transpired the way they did. For example, we are all familiar with the Great Depression. But why did it happen? What might be the causes of this occurrence? Could the 1929 stock market fall have been the catalyst for the Great Depression?
This provides us an idea of what will happen in the future. This is something that data analysts like doing. They examine all of the data they have in order to identify trends and forecast what the future will be like. When you wish to invest in a company, you perform fundamental and technical analysis on that company. You then try to find out how much profit it made in the past number of years. On that premise, you attempt to predict and calculate the predicted profit for the future year.
Cognitive analytics teaches you what would happen if a given event in the past did not occur, and how you would manage such events. For instance, if your pizza slid from your hands and landed on the floor. If you had the ability to ‘undo’ it, what would you do differently the next time?
Prescriptive analytics is concerned with figuring out what measures should be taken in order to achieve a specific goal or objective. This is simply planning. For example, if you want to buy a new laptop by next year, you decide to set away a certain amount of money every month until you have enough money to buy that laptop next year.
Now that we have understood the various types of data analytics, let us get into knowing the various data roles that exist within the data world.
The following are the various jobs/roles that exist within this domain:
A data analyst is responsible for acquiring, cleaning, manipulating, and analyzing large amounts of information in order to solve problems. They devote the majority of their time on analysis and visualization. Communicating the findings of their research is an important aspect of their work, and they do it by creating graphical representations such as charts and graphs, writing reports, building a dashboard, and presenting it to concerned parties. They use a variety of tools such as Microsoft Excel, SQL, Tableau, Power BI, R or Python, and others to help them complete their everyday activities efficiently
A business analyst is someone who studies and records a company’s market environment, operations, or frameworks. A BA focuses on helping enterprises in improving their performance by making data-driven decisions. They are similar to data analysts, but their primary goal is to detect problems, produce business insights, and provide recommendations for business improvements. They ensure that a company operates efficiently and effectively. Business analysts use a variety of tools such as MS Office, ReQtest, Jama, Orcanos, Power BI, and others to help them do their everyday work.
Data engineers are in charge of capturing, processing, and maintaining data. They typically deal with missing data or outliers, if any, and make judgments about how to interpret and handle that data. They undertake both primary and secondary research. Data engineers perform comparable activities to data analysts, such as revealing hidden patterns and anticipating trends using data, as well as writing reports and disseminating them to stakeholders. Not only that but, Data engineers also design and test scalable Big Data ecosystems for companies so that data scientists can run their algorithms on reliable and well tuned data platforms. They use a variety of tools such as Apache Hadoop, Apache Spark, Apache Kafka, Cloudera Data, and others to help them do their everyday work.
A data scientist is someone who works to identify useful data sources and automate information gathering, find patterns and trends in massive datasets in order to gain insights. They use algorithms and data models to forecast outcomes. The majority of data scientists have a technical background (CS/IT). They work with data analysis tools such as Python, R, SQL, Power BI and others. Machine learning algorithms are also used in their operations. Data scientists invest more time and effort towards constructing predictive models, developing analytical approaches, and incorporating complex programming in order to find answers.
Database administrators deal with creating and maintaining databases as per their company’s requirements. Basically, they know their way around databases. They protect sensitive data and allow access to key databases for enterprises, institutions, and so on. In addition, they diagnose, backup, and restore database sets as well as system access. They also modify and integrate outdated apps to to use the most up-to-date technology. Database administrators use a variety of tools such as MySQL, SQL Server Management Studio, DevOps, ESM Tools, Oracle RDBMS and others to help them do their everyday work.
Machine learning engineers design systems that allow computers to learn and make predictions without the need for human intervention. This covers facial recognition software, speech recognition software, and other similar systems. They begin by preparing and cleaning data. After that they will choose a model to apply with the data. That model will automatically deliver a variety of recommendations based on trends discovered. Data pipelines are also maintained, created, and streamlined by ML Engineers. ML Engineers use a variety of tools such as TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM and others to help them do their everyday work.
Data architects work with data frameworks to conceptualize and visualize them. They help in the identification of data harvesting sources in accordance with the data strategy. They also plan and manage end-to-end data architecture, as well as maintain database systems and ensure that they run smoothly and efficiently. Data architects use a variety of tools such as Draw.io, Lucidchart, Amundsen, DbSchema, Navicat Data modeler and others to help them do their everyday work.