5 key Analytical skills required in Data Analyst Job
Skills that a data analyst should have in their data science career path.
A Data Analyst should have good analytical skills. Most Data Analyst jobs require analytical skills. First of all, we would discuss analytical skills here.
Let us see
What are Analytical Skills?
Analytical Skills is the ability to collect, analyze information, solve the problem according to the information, and make the correct decision. In an organization, the authorities will prefer employees with the ability to collect information, investigate the problem, and find the ideal decision for the problem. Here, the analytical skill is used.
We use Analytical skills when observing information, interpreting data, transforming data into decisions.
Some Essential Analytical Skills will be discussed here.
- Creativity
- Understanding the context
- Technical mindset
- Data Designing
- Data Strategy
1. Creativity
When analyzing data/information, requires the key analytical skill (i.e) Creativity. Creativity to notice the different insights, trends in the data is important. The data analyst should think out of the box to analyze some events.
2. Understanding the context
Context is the specific structure or environment or event that happens. In analysis, the analyst should interpret the data. To analyze the data, they should be clear with the problem statement and its environment of usage. For example, If you are analyzing a restaurant's sales and deciding what customers prefer to eat. You have to be familiar with the restaurant's flow of activity and sales. If you didn't have much knowledge about its environment, data interpretation will be tough. So understanding the context is a key analytical skill.
3. Technical mindset
When analyzing data, having a technical mindset is also important. It will help us solve the issue quickly. The analyst should be able to break down the complex task into smaller and simpler tasks. It will help to get the analysis more easily. This makes our complex process very easier. Weighing the pros and cons of the task you are going to implement is also a key factor. It leads to effective decision-making that satisfies the need of the stakeholders.
Stakeholders are the people or organisation who invest money and time to get the analysis done by the experts which results in their growth of the organisation.
In short, the analyst should have the technical mindset to split down the bigger things into smaller steps.
4. Data Designing
As we all know designing is a decorative pattern or plan for the execution of anything. Here we use data. So we are creating a pattern with data to easily functioning the workflow in the data analysis process.
Organizing the dirty data into a clean format is the main goal of this data designing. The analysis process becomes a piece of cake when the data is in an organized manner. If that data is unorganised, we will miss out on something that may be a very important category. Leaving that data may lead to biased results. Then our hard work for the analysis becomes wasted.
5. Data Strategy
At the end of the data design part, we will have all the data in an organized manner. Now we have to plan for the analysis process.
Strategy is a plan of action designed to achieve a long-term or overall goal. The data strategy is a plan to manage the resources needed for the analysis process, tools required for the analysis, and a plan to follow tasks and processes these analysts have to go through. Teamwork will let us deliver the project on time and within budget.
Managing resources, planning which tool to use, and creating the workflow of the analysis process is the main part of this data strategy.
Other most important questions a Data Analyst will ask through the process
To make a Data-driven decision we must be clear with the concept and area of that specific analysis.
Data-driven decision-making: The process of using facts to guide business strategy
It’s impossible to solve a problem if you don’t know what it is. So to know about the problem, the analysts will ask various questions to solve that problem. Here, let us see some of the core questions the analyst ask throughout the process.
1.What is the root cause of a problem?
To solve a problem, we must deep dive into the problem statement.
For example, The Crackers manufacturer started selling their products online. But, he couldn’t find a huge customer interaction. He doubted that selling crackers in online mode is not a wise decision. So he hires a data analyst. Now the hired Data Analyst should deep dive into the problem statement. The analyst wants to know that their customers want to get the products in person or they want to get by online. The analyst should also find the root cause of the problem.
A root cause is a reason why a problem occurs. If we can identify the root cause, we can easily solve the problem. To know the root cause the analyst should ask so many interior questions related to that problem.
2. where are the gaps in our process?
The “gap” is the space between where the project is and where it wants to be in the future. To reduce this gap and get the project done efficiently, companies perform a gap analysis. Gap analysis is a methodology of assessing the current stage of the process to determine whether the requirements are met and what are steps to be taken to meet the requirement.
Summary
- Creativity: Thinking out of the box.
- Understanding the context: Understanding the basic background of the problem
- Technical mindset: Splitting down the bigger things into smaller steps.
- Data Designing: Organising the data
- Data Strategy: Planning Resources, tools and technologies going to be used.
To know about the Six steps of the Data Analysis process:
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