It’s 11th September 2022, and I am signing in from Colombo, Sri Lanka. My weather app says, temperature is 29 Celsius, a sunny day, as usual.
Recently, I started following multiple individuals who are working in the data analysis field and among them was few established experts and some were sharing their experience in changing their career to the data field. Also, there were profiles sharing their skills in getting started as a data analyst. Hence, I started looking at some of these increased number of data analysts opportunities and obviously “data” is the big talk of the town.
But what exactly is the data analysis and how you can move into a data analysts career path?
Well, my background story is that I grew an interest in this field when I was doing the quantitive analysis for my MBA Thesis. So, I want to brush up my skills relevant to this field now. So, let’s research what is data analysis.
According to the dictionary, there are three definitions for “Data”. They are,
- factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation
- information in digital form that can be transmitted or processed information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful
Going through all these definitions, we understand the relationship between Data and information. And data must be processed to be meaningful. Hence, data analysis means the processing of raw data to get meaningful outputs. There are five types of data anlaytics such as Descriptive, Diagnostic , Predictive, Prescriptive, and Cognitive. Each type of analytics is used based on the requirement and the purpose.
The World Economic Forum Future of Jobs Report 2020 listed “Data Analyst” as one of the top emerging jobs. A data analyst, tells a story of the raw data. According to Microsoft, Data analysis is the process of identifying, cleaning, transforming, and modeling data to discover meaningful and useful information. For decision making process, these model data then transfer into visualization through reports.
A data analyst is different from a business analyst and also different from a data engineer. Often companies expect everything get done by a data analyst. But let’s understand the role of a data analyst.
A data analyst enables businesses to maximize the value of their data assets through visualization and reporting tools such as Microsoft Power BI. They are responsible for following
- Profiling data
- Cleaning data
- Transforming data
- Designing and building scalable and effective data models
- Enabling and implementing the advanced analytics capabilities into reports for analysis
Data analysts have to work with data engineers to get the accurate data sources that meet stakeholder requirements.
Data analysis process
According to Microsoft, there are 5 phases of data analysis process.
Businesses use data output provided by a data analyst to derive decisions. Marketers will rely on the reports given by a data analyst to plan their next marketing campaign. If any of the data used on the analysis is inaccurate and contained false data, then it will hugely impact the business. Therefore, preparing data is critical in a Data analyst’s job.
Data preparation is the process of profiling, cleaning, and transforming data to get it ready to model and visualize. Preparing contains,
- Maintaining data integrity
2. Correcting wrong and inaccurate data
3. Finding missing data
4. Converting data from one structure to other or one type to other
Also, data analyst should focus on ensuring the data security and privacy. Hence data analysts have to spend quite a significant time in eliminating poor data quality.
Always think about the data points available in our fingertip and imagine how we can process the data to information.
When data preparation completed, data modeling can be performed by creating relationships between the tables. Further we can enhance the model by adding custom calculations to data.
Advantages of a proper data model
- Able to make accurate reports
- Allowed data navigation faster
- Decreased time for report writing process
- Simplified future report maintenance
- Improved report performance
Business decision making is based on the report data. But these needed insights should be compelling stories that are understandable by the stakeholders.
It is important to convey the insights from data in a method that is understood by all stakeholders. This is where numbers become a story. In the visualization phase, we share data insights with the respective team managers, executives, or CEO to consider data-driven decisions.
Visualization use tools such as Tableau, Power BI, Lookers or Excel. Depending on the insights you are sharing, a data analyst can decide which tool suited.
If you are struggling to learn something new, remember small steps counts for a big step and one-step-at-a-time.