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15 Must-have Analytics Skills For Every Data Analyst In 2022


We live in an information age where large amounts of information are available to you at the push of a button. Every second, thousands of terabytes of data are being transferred in cyberspace all over the globe. Some of this information is in its raw form and referred to as data. 

Think of data as a brilliant book written in Latin. You may not be able to read Latin so what you must do is find some who can speak Latin. This person will make sense of this book and translate it for you. The translator here stands as the data analyst. He will collect data required by the organization or business, analyze them and then come out with information that the management can use to make decisions that will move the business forward. 

In this article, I’ll list the 15 must-have analytics skills for every data analyst in 2022. Carefully read through.

Who Is A Data Analyst?  

A data analyst is a professional who collects and stores data on sales numbers, market research, logistics, linguistics, consumer behaviors, and other behaviors. He ensures the accuracy and quality of the data, then he processes it and presents it to the management of businesses or organizations to help them make better decisions. A data analyst must have acquired a bachelor’s degree in a relevant field. However, there are numerous online courses for people who wish to switch careers and venture into data analysis. Most of these courses do not require you to have had any previous training in this field or any experience at all. All you need is a working computer, the internet, and the willingness to learn. 

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What Is The Job Description Of A Data Analyst?

A data analyst is expected to perform the following tasks on the job:

  • Manage users and user roles.
  • Design and maintain data systems and databases. 
  • Ensure the quality of data while working with a quality assurance analyst if necessary.
  • Commission and decommission data sets.
  • Help develop reports and analysis for single or multiple systems. 
  • Provide technical expertise in data storage structure, data mining and cleansing.
  • Support initiatives for data integrity and normalization.
  • Use statistical tools to interpret data sets while paying attention to particular trends and patterns that could be valuable. 
  • Create documentation that allows stakeholders to understand the data analysis process. 

What Is Analytics?

Analytics is the systemic processing of data to provide relevant information that helps the relevant stakeholders make the right decisions for the organization/business. 

There are four types of analytics and they each build on each other to increase value to an organization. They are; 

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Descriptive analytics

This analytics helps the organization identify trends either in sales, revenue, website traffic, orders, etc. It can help the organization keep track of what happened and when. 

Diagnostic analytics

This considers the reason something happened by comparing descriptive analytics. It helps the organization identify which strategies are effective and which are detrimental based on the trends. 

Predictive analytics

This determines the likely outcomes of some decisions. They enable the organization to be proactive with solutions. These outcomes are determined by detecting tendencies in descriptive and diagnostic analyses. 

Prescriptive analytics

This determines the action the organization should take to achieve optimal results. This type of analysis is extremely valuable; however, it requires the use of complex algorithms and advanced technology, such as machine learning. This may make this option far-fetched for some businesses. 

Why Is Data Analysis Important In Business? 

Data analysis is more in-depth than merely presenting numbers and figures to management. It requires a knowledgeable approach to recording and analyzing data. It is very important because it helps businesses predict customer trends and buying patterns. Through data analysis, businesses can discover what affects their sales positively or negatively. Businesses can know what times of the year they experience a demand surge so they can plan on how best to meet the needs of their customers. 

Data analysts organize, interpret, and deliver data. Without data analysis, the information would sit there completely useless to the management and organization. 

Data analysis can also serve as a diagnostic to determine why a business or an organization is not reaching its set goals, even though it seems to do all the right things. It takes into consideration the input used, against the results produced, while considering other external variables in order to find the smoking gun. A lot of businesses get out of a stagnation phase after getting a proper data analysis team because they are no longer going it blind. 

Data analysis also increases business productivity because they employ resources where they are most effective, ensuring increased productivity and returns. It also drives effective decision-making. It is nearly impossible to make a wrong move when all your decisions are informed by accurate and reliable data. When the predictive analysis is available, a business can be proactive and work towards averting problems in the future. 

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Examples of Analytical Skills For Every Data Analyst 

An excellent data analyst benefits from fully mastering the following analytics skills:

1. SQL

SQL means Structured Query Language. It is a spreadsheet and computing tool used to handle large sets of data and process information much quicker than spreadsheet software. Knowledge of the functionality of this tool is very essential to a data analyst as it gets work done faster and more effectively.

2. Spreadsheets 

Even though SQL seems to be the preferable alternative, this traditional spreadsheet is still very essential. It may be regarded as rudimentary knowledge to some, but several businesses still prefer their data and reports here. A good data analyst should have all his basics covered, including the knowledge of the spreadsheet.  

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3. Critical thinking 

As with many tech-related jobs, critical thinking is important to a data analyst. He needs to decide what data he needs, how to collect and process them to get the information that he requires. Critical thinking is a skill all data analysts should develop. 

4. Statistical programming languages 

Some programming languages, like R or python, are used to analyze large data sets. A data analyst should be versatile. Therefore, he or she should be familiar with a variety of data analysis programs rather than restricting them to one or two. You never know which of the tools the company has available. 

5. Data visualization 

The data analyst should be able to present his findings using graphs and charts. He must share the relevant parts of his finding in a clear, concise, and understandable manner so it’s useful to the management. 

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6. Public speaking

Data analysts are not politicians or freedom fighters, so they may not need Martin Luther King Jr.-level public speaking skills. However, they need to get their information across. If a data analyst has zero public speaking skills, he may not get the information across. They may lose some relevant aspects of his findings in translation. On that note, some public speaking skill is a plus to a data analyst.  

7. Machine learning 

Some companies need to do some prediction analysis, and this requires some complicated algorithms and machine learning. A data analyst needs to have some skill to enable him to harness the beauty of artificial intelligence to get maximum results for the business. 

8. Data warehousing

Data warehousing involves creating a virtual or organizational system for a company’s data. A data analyst has to create, safeguard, monitor, and update this data warehouse. It is important that they keep it safe, therefore they may grant access to only data analysts and top management personnel.

9. Communication 

In the course of his/her work, a data analyst has to communicate either with other members of staff or other analysts on the team. Communication skills are important since you cannot function effectively in isolation. Excellent communication skills help ensure that work gets done smoothly and quickly within an organization because everyone understands what is expected of them. 

10. Problem solving 

There is no job in the cooperate scene that does not require problem-solving skills. A data analyst, like others, must be able to think fast and view problems objectively while figuring out the best possible ways to solve the problem. He must be able to maintain a level of calm in a crisis. 

11. Research 

Data analysts also need to do some research to determine the solution to technical problems that they may find overwhelming. They may also need to evaluate new processes based on data analysis and evaluate the results other businesses have been able to get from these results before attempting to replicate them. A data analyst cannot do without research skills. 

12. Attention to detail 

When dealing with copious amounts of data in a short time frame, it is important you pay attention to details, find patterns that no one else may have noticed, and identify possible errors. This makes a good data analyst. Sometimes the computer programs do not do all the work, your mind must also be engaged. 

13. Collaboration 

A healthy respect for teamwork and a willingness to collaborate is a feature of a great data analyst. You must learn to work with both internal and external stakeholders towards a common goal, the growth of the business or organization. Your own personal interests and pet peeves have to take a back seat in dealing with work-related collaborations.

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14. Project management 

They may require a data analyst to head a team of other analysts to produce the desired result. Project management is essentially the act of nursing a project from its conception to the day they finally completed it. A data analyst with this skill will easily be able to pull his team together, use everyone’s strengths, make up for weaknesses and ensure that they complete the task. 

15. Data prep 

Data prep is converting data from a hunk of meaningless numbers and letters into a well-sorted and understandable report. It is usually done before data visualization. Data prep makes it easier for the information to be converted to understandable graphs and charts for presentation.

With these skills, there is no stopping you from arriving at the peak of your career as a data analyst.

Frequently Asked Questions

Where are data analysts needed? 

Any business or organization that wants to keep proper track of its progress and make informed decisions about its future needs a data analyst. 

How much do data analysts make?

In the United States of America, they earn between 50 and 70 thousand dollars annually. 

What educational credentials are necessary to become a data analyst?

A bachelor’s degree is the minimum requirement for an entry-level job as a data analyst. However, some positions require you to have a master’s degree.

What other jobs are related to a data analyst role?

Financial analyst, marketing specialist, business analyst and business systems analyst are all related to the data analyst role. 

Conclusion 

Data analysis is very important to every business and a good data analyst who has mastered these skills is an invaluable asset to any business or organization. Having information is one thing. Understanding this information and what it represents is another. 

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