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In recent decades, the role of data analyst has exploded. This is due, in large part, to the fact that data has become more accessible and widely utilized by masses of organizations across the world.
As it stands now, the field shows no signs of slowing down and will likely continue to grow as we delve deeper into the future.
However, as important as the role of data analyst has become, many people are still unaware of exactly what those in the role do and the value they can offer organizations.
Developing a deeper understanding of these professionals, their skills, and their role in companies can give one a more comprehensive perspective on how the world is evolving.
What Is a Data Analyst?
Data analysts work across various industries and utilize data to meet specific goals. Their work typically includes gathering, preparing, analyzing, and presenting data.
Given that almost any organization and industry have data that it can use to its advantage, these professionals have become a normalized part of many different industries and focus on different outcomes in their jobs.
By gaining a deeper perspective on how these professionals take advantage of data, their value in today’s job market becomes more apparent.
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What Data Analysts Do
To truly understand the data analyst role, looking at their tasks and the processes they adhere to is essential.
While data analysts’ exact duties and functions may change depending on their industry, almost all data analysts engage in certain data processing practices to gain the most value from data.
Here is what the data processing practice typically involves.
1. Collecting Data
Before data analysts can utilize data to achieve specific goals, they must first collect it. Typically, data analysts will do this in several distinct ways.
One common source of data that most data analysts take advantage of is publicly available data. This can be data collected by government organizations or other organizations that publish their findings.
Another place data analysts typically look to collect data is through their own data collection efforts. This can include creating and conducting surveys and documenting various observations that can be used to create data sets.
The data type they collect will differ depending on what a data analyst wants to achieve. Data analysis has two distinct types: quantitative and qualitative data.
Quantitative data is data that can be translated into numerical values. In other words, it’s data that can be quantified. This form of data can come from research tools such as multiple-choice questionnaires, which offer the ability to quantify answers.
For researchers and data analysts, this form of data can sometimes be easier to work with as it is clear and distinct, which makes it ripe for analysis.
Conversely, qualitative data cannot be put into numerical values or quantified. This can include surveys that allow participants to respond to openly answered questions in which they can say whatever comes to mind.
Though this type of data and this data collection method can help point researchers in new directions and limit the role of their biases in data collection, it can be more complicated to use for analysis.
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2. Storing Data
Once data analysts have collected their data, the next step is storing it securely. This refers to physical documents and digital data, which must be kept safely.
Regarding physical documents, data analysts typically use file cabinets with locks — mainly when data contains confidential information.
For digital data, data analysts typically store it in their company’s secure databases. In some cases, data analysts may also use storage devices, such as flash drives, to make additional backups to ensure that data is never lost.
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3. Data Preparation
Data preparation is a stage where data analysts’ skills and expertise genuinely come into play. In this stage, these professionals must verify the accuracy of their data and prepare it for analysis.
Depending on what the analysts intend to use the data for, there will typically be other metrics and criteria they must ensure all data meets. This is a vital step for data analysis; skipping data preparation could result in faulty and inaccurate analyses.
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4. Data Processing
At this stage, data analysts must process their data to prepare it for analysis. In essence, processing data is manipulating and transforming data in crucial ways to make it usable for analysis.
Processing efforts can include organizing data into distinct sets along with verifying data sets. In some cases, artificial intelligence technology is utilized to perform these functions at a high level that humans may not be capable of without the help of this technology.
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5. Data Analysis
At this stage, data analysts can analyze and interpret their prepared data. This stage may look different for analysts, as what they seek will differ depending on their work industry.
The analysis will typically involve specific questions that data analysts want answers to. This can include questions such as “Which new product would consumers of a certain demographic be most willing to buy?” or “In what ways can we optimize the efficiency of a factory’s output processes?”
Analysts will also look for distinct patterns or trends in their data at this stage. Much like the data processing stage, many analysts utilize artificial intelligence technology to streamline this process and detect patterns that may be difficult or impossible for humans to interpret.
6. Data Presentation
Once data analysts have analyzed and interpreted their data sets, they must translate the data into easily understandable representations. This can include graphic representations, such as bar graphs and pie charts, or written representations, such as reports.
This is an essential stage in the process, as data analysts must present their findings in ways that are easily understandable to others in their organization.
Those utilizing the findings will typically be in other roles, such as CEOs, marketing managers, or factory owners. As such, effectively translating data analysis findings is a crucial aspect of the data analyst role.
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Where Data Analysts Work
Data analysts can work across various industries to achieve different goals. Becoming aware of some of the distinct data analyst specialties can give one a more nuanced understanding of the broad application of data analysis in today’s evolving world.
Here are some different data analyst specialties that one can pursue in the field.
Business analysts are data analysts who help organizations improve business processes and achieve specific business-related goals with the help of data. These professionals can work as consultants or at a specific organization.
Some duties of business analysts may be weighing the risks of certain business decisions, finding ways to make an organization more efficient, or exploring ways businesses can achieve their goals by analyzing data.
This role is perfect for those with an analytical mind and a fundamental understanding of business processes.
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2. Operations Analyst
Operations analysts work with organizations to help improve their operations in various ways. This can include improving technical processes such as manufacturing processes and employee output.
These professionals work across a range of industries, including manufacturing and insurance. This is a significant job role with room for growth for those with keen attention to detail and an interest in the inner workings of organizational operations.
3. Economic Analyst
Economic analysts study trends to predict forecasts for what the economy will do. They can both work to predict what will happen in the short term and what will happen in the long term.
Many economic analysts work for government organizations, helping inform decision-making related to the economy. This is a great job opportunity for individuals who enjoy looking at the bigger picture and predicting society’s direction.
How to Become a Data Analyst
Becoming a data analyst is attainable for anyone willing to take the necessary steps to reach this goal. While it may not be the quickest career for one to become qualified, it is a career path growing and evolving rapidly to meet society’s demands.
Here is how to become a data analyst.
1. Have an Interest in Math and Statistics
Before considering a data analyst role, one must be interested in math and statistics.
The role requires many math-related processes, making it extremely tedious and grueling for individuals who don’t find these subjects stimulating.
As such, we are interested in math, and statistics should be a prerequisite for pursuing a career as a data analyst.
2. Obtain a Bachelor’s Degree
Once one has determined that math is a field they’re comfortable with, it’s time to pursue a bachelor’s degree. Typically, organizations looking for analysts will require that an individual has a bachelor’s degree in statistics or a closely-related field.
A baccalaureate program will prepare one for a career as a data analyst by equipping one with the proper skills to conduct research and analyze data.
However, while it is advisable for those interested in pursuing a career as a data analyst to study a closely related field, some may pursue a different degree as an undergraduate and later obtain an advanced degree in data analysis.
3. Obtain a Master’s Degree
While an advanced degree is not required for all data analyst roles, those hoping to grow into a senior position can significantly bolster their chances by obtaining a master’s degree.
Master’s degree programs will allow one to develop advanced skills that give one more perspective on data analysis and signal to employers that one has the skills to be put in advanced roles.
Alternative Routes to a Career as a Data Analyst
While traditional college degrees are a great way to qualify for a data analyst role, it’s not the only path one can take to get there. Today, there are new ways that one can gain the skills and training necessary to step into a data analyst role.
Data analyst bootcamps are becoming popular for many individuals pursuing a career. These bootcamps are typically significantly shorter than a college course and substantially cheaper.
This is an excellent option for professionals seeking a career change who don’t want to commit to a four-year university program.
In addition to bootcamps, online courses and other online resources are another way to pick up the skills necessary to become a data analyst.
However, it must be noted that pursuing a career in data analysis through this route takes significant determination and self-discipline.
In addition, it will be much more difficult to convince employers to hire you if you don’t have a degree or certificate proving your ability to do data analysis.
The field of data analysis is constantly growing and evolving in profound ways. While this can make this an exciting field to be a part of, it also means that it’s vital to continue learning — even beyond one’s collegiate career.
New technologies are constantly being integrated into data analysis processes, and being familiar with the newest trends can play a crucial role in succeeding in the field. Some areas of study that it’s vital to stay up-to-date with include:
- Artificial intelligence
- Machine learning
- New regulations in the field
While it may seem like a complicated task to keep up with all of the newest trends in data analysis, it is one of the components of the industry that can make it exciting.
As such, the role of a data analyst can be perfect for those interested in immersing themselves in technological developments and the evolution of artificial intelligence technology.
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The Role of Data Analyst Is Rising in Value
Data analysts are proving themselves to be integral parts of many different industries. Data analysts can apply their specialized skillset to different contexts, from helping businesses reach their goals to helping manufacturers become more efficient and optimized.
In addition to being a broad field, the role of a data analyst is also growing each year, making it a secure career path.
This being the case, any individual intent on developing a technical and specialized skillset in high demand can achieve their goal by pursuing a career as a data analyst.