What does a data analyst do?

You’ve probably heard about a “data analyst” before, but never actually had it clear—what does this person do exactly?

They aren’t data scientists, but they aren’t statisticians either. The combination of data and tech certainly sounds intriguing, right?

Let’s break it down.

What does a day in the life of a data analyst look like?
Data analysis has progressed as a result of the advancement of computing and an ever-increasing shift toward technical methods and forms. On a broad dataset, a data analyst gathers, processes, and conducts statistical analysis. They learn how to use data to find solutions to challenges and resolve issues.

A data scientist extracts and collects data, arrange it and uses it to draw concrete conclusions. The exact role of data analysts depends based on the type of data they’re dealing with and the project they’re working on.

From hospital facilities and department stores and fast-food restaurants, data analysts have the ability to support companies in almost any sector. Employers who wish to understand more about their customers will benefit from the perspectives that data analysts bring to the table.

Data analysts work on designing systems for gathering data and translating their results into analyses that can help their organization improve, regardless of the sector they work in.

The data analysis process is long and involves many roles. The data analyst has the abilities to work during the entire process, from setting up the data analysis system, to creating dashboards, and extracting actionable insights based on the collected data.

To establish operational priorities, data analysts collaborate with IT teams, administrators, and/or data scientists. They collect and clean data from primary and secondary sources, and use common statistical methodologies to analyze and evaluate the information.

They find new prospects for process change by identifying trends, similarities, and patterns in diverse data sets. Data analysts must regularly report on their results to advise important stakeholders on the next moves.

The skills of a data analyst

Here are the most important skills required for a data analyst:

Coding. Data analysts should be competent in at least one programming language and have a working comprehension of a few others. For data collection, data cleaning, computational equations, and data visualization, data analysts use programming languages like R and SAS.

Excel. Advanced modelling and analytics techniques are expected of data analysts, as well as a clear command of other Excel functions.

Data visualization. It takes a lot of trial and error to get good at data visualization. A strong data analyst learns how to use various tables and graphs, how to scale visualizations, and which graphics to use based on the audience and other visualization-related skills.

SQL. Relational databases of data structures are known as SQL databases. Data is contained in tables, and to do analysis, a data analyst takes data from diverse tables. SQL is the most common querying language used by data analysts, and there are several versions of it, including PostgreSQL, T-SQL, and PL/SQL (Procedural Language/SQL).

Data warehousing. Some data analysts are required to build a data warehouse by connecting databases from various sources and searching for and managing data using querying languages.

Data mining. When data isn’t properly processed in a spreadsheet, data analysts mine unstructured data using other methods. Then, they clean and analyze it until they have a sufficient volume.
Machine learning. Even though machine learning is not a required skill in most data analyst careers, data analysts with machine learning expertise are extremely valued.

Curiosity and creativity. It’s vital to have a solid understanding of mathematical methodology, but it’s much more necessary to approach challenges with an innovative and logical mindset. This will assist the analyst in creating interesting analysis questions that will help the organization develop a deeper understanding of the topic at hand.

Open communication. Whether it’s to a larger group of people or a small selection of executives making strategic decisions, data analysts must clearly communicate their conclusions. The secret to succeeding as a data analyst lies in the way you communicate your findings.

The responsibilities of a data analyst

Here are the key responsibilities that a data analyst has daily:

Collecting data
Data analysts use a set of different software apps and tools to collect the required data volumes through a streamlined process. Their task is to keep improving this process, making it as automated as possible. To achieve this, they often work with developers, data scientists and other team members.

Creating reports
Reports provide managers with knowledge on future developments as well as fields that the business will need to change. A report isn’t as easy as scribbling numbers on a sheet of paper and submitting it to your supervisor. Effective data analysis means telling stories about numbers. The reports, responses, and observations that data analysis offers must be grasped by the decision-maker, who is often not an expert, to remain useful.

Detecting trends

A data analyst must first be able to see critical trends in the data to generate a reliable report. Data is used to discover patterns and ideas that the analyst can use to make recommendations to their clients at every level. It’s critical to publish in frequent periods, such as weekly, monthly, or quarterly since it lets an analyst spot meaningful trends. They’re all part of a larger time cycle that allows stakeholders to see patterns over time.

Working with other team members

Because of the vast range of data analyst positions and duties, they work with people from various divisions within the company, including marketing, administrators, and sales staff. They also work closely with data scientists, as well as members of the IT department.

Being communicative is one of the most important characteristics of the data analyst role due to its collaborative nature.

Four types of data analytics

Responding to questions and recommending activities to address certain decisions are at the heart of data analytics.

Depending on the type of question or challenge, there are four types of data analytics.

Descriptive: What is happening?
This is the most popular of all the analytics types. In business, it gives the analyst a view of the company’s core statistics and measurements. For example, you could notice that the sales are going down.

Diagnostic: Why is it happening?
Diagnostic analytical software can allow an analyst to drill down and identify the core of a problem based on the descriptive data. Such research is possible with well-designed business data dashboards that provide time-series data interpretation, filters, and drill-down capabilities. They should give you the reason why your sales are going down.

Predictive: What is likely to happen?
Forecasting is at the heart of predictive analytics. Predictive models are used to determine the probability of a situation occurring in the future, forecast a measurable number, or estimate a point in time when something might appear.

Prescriptive: What should we do?
The prescriptive model uses an interpretation of what occurred, why it happened, and many “what-might-happen” analyses to assist the consumer in determining the right course of action. Prescriptive interpretation is usually concerned with several different acts rather than just one.

The tools of data analyst

To compile and make sense of their data, data analysts use a variety of tools.

These are usually advanced tools to obtain data from social media, media outlets, and other sources, as well as tools to organize and classify data to visualize it for stories and demonstrations.

Here’s a list of some of them:

Google Analytics
Google Tag Manager
Tableau
Jupyter Notebooks
Microsoft Excel
SAS
AWS S3
Github

What does the future look like for data analysts?

Today’s data analysts must be flexible and adaptable. Analysts’ jobs are getting more complicated. Modelling and predictive analytics approaches are used by experienced researchers to produce valuable observations and decisions.

Then, they have to justify what they’ve found to a group of confused ordinary people, which isn’t an easy thing to do.

Since data analysts can work in a wide range of sectors, including banking, healthcare, intelligence, engineering, professional services, and retail, technological advancements have resulted in an increase in analyst roles.

The Future of Jobs Report by the World Economic Forum predicts that by 2022, 85% of companies will have adopted big data and analytics technologies.

Moreover, the report discovered that 96% of companies said they were certainly looking to recruit new permanent employees with appropriate expertise to fill potential big data analytics-related positions.

We gather data at every level, and the organization of that data, as well as the application of forecasting, helps the world evolve in a better way.

So, if you’re a tech-savvy and creative person with a logical mindset and strong attention to details, becoming a data analyst might be the real career path for you.