Exploring the Key Responsibilities of a Data Analyst: Tasks You Need to Master
A data analyst is a professional who helps companies to get the most out of their data by providing insights and recommendations after analyzing the data.
A data analyst is responsible for collecting, processing and performing statistical analysis on large sets of data. In addition, they are responsible for creating scalable data models, enabling advanced analytics and ultimately integrating such capabilities into analysis reports.
Here is a dive into the tasks and responsibilities of a data analyst:
1. DATA PREPARATION
A data analyst spends most of their time preparing data for analysis. This is because the validity and reliability of their insights and recommendations directly depend on the quality and accuracy of the data they use for analysis. Data comes different sources hence the frequent need for it to be organized, cleaned and formatted before use.
The common tasks involved in data preparation include:
- Data cleaning. This entails locating and fixing any errors, inconsistencies and missing values.
- Data transformation. This simply means converting data from one format to another for example changing the scale of units or ensuring that data is in its correct data types. This makes data more suitable for analysis.
- Data integration. This involves combining data from multiple sources, such as merging datasets and adding new data to an existing dataset.
- Data reduction. It entails reducing the size of a dataset often by removing redundant or irrelevant data.
- Data normalization. This means transforming data to have a common scale. This ensures that data is consistent and simple for analysis.
All these tasks ensure that data is accurate, complete consistent and ready for analysis.
2. DATA MODELLING
Once data has been prepared, it is ready for modelling. Data modelling is the process of creating conceptual representation of data objects and the relationships between them.
It involves identifying the entities, attributes and relationships that are relevant to the analysis and creating a model that represents these entities.
An effective data model helps improve the accuracy of reports, allows for faster and more efficient data exploration, reduces the time used in report writing process and increases the simplicity of future report maintenance.
A data analyst first identifies the entities, attributes and relationships relevant to the analysis then creates a data model using a tool such as Entity Relationship Diagram(ERD). They then use data analysis tools such as Power BI, Excel or Python to extract data from the databases and analyze and visualize it.
3. DATA VISUALIZATION
Data visualization is the process of presenting data and information to people using visual tools like maps, charts and graphs. Complex data can be presented in a simple, quick and clear manner thus simplifying comprehension and consumption of data.
The reports generated during the visualization assist organizations and decision-makers in comprehending the meaning of the data in order to make precise and important judgments.
A data analyst takes time to comprehend the business problem at hand and determine the necessary data points for use. It is important for a data analyst to keep the visuals simple, use the right visualization, highlight the crucial points and provide context during data visualization as all these aspects come together to tell a story.
Some of the common visualization tools used by data analysts are Microsoft Power BI, Tableau, Qlik Sense among others. Python libraries such as Matplotlib, Seaborn and Plotly are also used.
4. DATA ANALYSIS
This is the step that involves really understanding and interpreting the information contained in a report for the purpose of finding insights, identifying trends and patterns and predicting outcomes and finally communicating the insights in a clear and simple way that can easily be understood.
Data analysts use advanced analytics and use statistical techniques such as regression analysis, hypothesis, testing and machine learning techniques to uncover patterns and insights from data.
Python libraries such as Pandas, NumPy and tools like Power BI are commonly used to do data analysis.
5. DATA MANAGEMENT
Data management involves the process of gathering, storing, organizing, managing, and protecting data over the course of its lifecycle.
In many companies, data management is a responsibility of a data engineer. However, in small and medium sized companies, a data analyst is expected to carry out data management activities.
It is therefore important to have understanding of data management principles.
For a data analyst to be effective in their work, the require to be well versed in the following skills:
Hard skills
- Knowledge of a programming language such as Python or R
- SQL and NoSQL databases
3. Math and Statistical analysis
3. Power BI, Tableau or any other visualization tool
4. Microsoft Excel
Soft skills
- Communication
- Attention to detail
- Critical thinking
- Teamwork
