Data analysts and professionals in data-centric fields are in demand all over the world because of the key role that they play in turning raw information into meaningful insights. The US Bureau of Labor Statistics predicts that there will be a 25 percent rise in the demand for people who can carry out data analysis processes between 2020 and 2030.
In this article, you will find out what it means to be a data analyst, the data analysis processes used to manipulate data, and the benefits of these processes to businesses. You will also find some insight on the best educational paths for someone who wants to pursue a career in data analytics.
What Is Data Analysis?
Data analysis is the process of converting raw, undefined, or defined data into actionable insights that can fuel business decisions. It involves the use of advanced analysis tools to sift through large volumes of data and determine how data can help a business achieve its goals.
An expert in data analysis is called a data analyst and they use statistical and mathematical tools to make sense of data that may appear meaningless to untrained eyes. There are primarily six types of data analysis: descriptive analysis, predictive analysis, exploratory analysis, inferential analysis, mechanistic analysis, and causal analysis.
What Are the 5 Components of Data Analysis?
Several components of data analysis must be present in every data-centric organization. As someone who wants to learn data analytics, you will benefit from learning about the most important components and how they play a vital role in the process. Below is a list of these components.
Data Roadmapping
The most effective strategies for data analysis start with mapping which is the process of matching fields and elements within and across databases. Data mapping is an organizational strategy that prepares data for migration and similar procedures. It also makes it easier for a business to manage and track data that flows within and outside the organization.
Data Security
Data security is another vital element of data analysis and database management. It’s important that data in any organization’s pipeline remains secure throughout the process from collection to storage. In most cases, data security is handled by network and information security experts. However, a data scientist can learn network security and handle both roles at the same time.
Data Governance
An organization must take charge of its data by setting up rules, systems, and everything else that is necessary to preserve the quality of the data. Data governance is a collaborative effort that involves employees from different sectors of a company.
Data Storage
Data storage is a vital component of the data analysis process. There are different forms of data storage. The most popular options are object storage, file storage, and block storage. Data analysts ensure that valuable data can be stored and referenced in the future.
Data Optimization
Another vital component of data analysis is data optimization. It has to do with promoting the speed and efficiency of database management systems. Data optimization techniques can either be implemented by data analysts or database administrators.
What Is a Data Analysis Process?
Data analysis processes are all the stages involved in inspecting, analyzing, and transforming data into meaningful information and actionable insights. These processes are necessary because they ensure the smooth flow of data from its raw form to its visualized and valuable form.
The data analysis process might vary based on a company’s roadmap and data strategy but the outcome is usually the same. At the beginning of a fiscal year, companies rely on the insight provided by data analysts to make projections about their goals for the year.
What Are Data Analysis Processes Good For?
There are many benefits of the data analytics process. Some are specific to unique company needs while others are useful for a variety of purposes. Below, we list some of the most prominent benefits.
- Customer satisfaction. Data analysts often process customer data like user behavior and feedback using machine learning algorithms. This data is subsequently analyzed and handed over to the marketing and sales team so they can streamline their efforts based on customer needs. It also improves customer service.
- Risk mitigation. Several machine learning statistical models can be used to predict the consequences of certain actions within a company. The data analytics process helps companies gain a clear view of the risks attached to these actions and subsequently mitigate them.
- Data-driven decision-making. Companies can save on operating costs, increase profit, and improve overall efficiency by making data-driven decisions. The investment in a data science and data analytics team is always worth it in the short and long term. This is why experienced data analysts in the US earn up to $113,000, according to ZipRecruiter.
What Are the 5 Steps of the Data Analysis Process?
The stages of data analysis are also known as the data life cycle. These stages must be followed sequentially if the data analysis team is to complete its duties on time. The stages are explained below.
Data Mining
Data mining is the process of extracting data from all the necessary sources to prepare it for insertion into the database system for analysis. It is also called data discovery because it is at this stage of the process that the data analysts determine which data is useful for business strategy and goals. Exploratory data analysis may also be useful at this point. One of the most common forms of data mining is web scraping.
Understanding business goals is an essential step when experts begin data mining. After the data analyst gets a clear picture of a business’s needs, they will know which data to mine. Data can be mined from different sources depending on the services the business provides. Diagnostic analysis may play a role during this stage.
Data Cleaning
After extracting the data from numerous sources, the next stage of the process is data cleaning. A significant portion of the data that is collected may be inaccurate or incomplete. The cleaning process is designed to reformat the data and make sure that only the most relevant and accurate information is pushed over to the next stage.
Data cleaning can either be done manually or with data cleaning software. Some of the best software for data cleaning are RapidMiner, KNIME, Trifacta, TIBCO Software, Data wrangling, and Open Refine.
Descriptive Statistics
At this point, it is no longer about preparing the data but actual analysis. The data analysts will have to describe the data in quantifiable terms. This form of statistics provides information about what is in a particular dataset while also finding the relationship between variables within a dataset.
A data analyst may use a graphical format and summaries to make sense out of data during descriptive statistics. It simply prepares the data for quick and easy interpretation, especially when the analyst is dealing with large datasets.
Predictive Analytics
Predictive analysis is the fourth stage of the data analysis process. During this stage, the data that has already been described will be used to make business projections or forecasts. The forecast usually depends on the type of data that is being analyzed. For example, with data from an advertising campaign, the data analyst can draw up a forecast for future campaigns.
Presenting the Results
This is the final stage of data analysis and is also called prescriptive analysis. At this point, the data would have been modeled and visualized. The data analyst presents the data to the decision-makers in the company so that they can put it into consideration when making important decisions. Decision-makers subsequently come up with a plan that allows them to integrate the valuable insight.
How Can I Learn Data Analysis Processes in 2022?
You can learn data analysis processes by learning data analysis itself. It is usually the standard procedure to learn about the data analytics processes during your initial education. Universities, data analytics bootcamps, and online course providers all offer the knowledge you need to kickstart your career. The one you choose should depend on your circumstances.
You can opt for a Bachelor’s Degree in Data Analytics or Data Science. Those who cannot afford a degree will benefit significantly from attending a bootcamp. Alternatively, if you have the capacity to teach yourself and learn from home, data analytics online courses from platforms like Udemy and Coursera are ideal.
Data Analysis Processes FAQ
Data analysts should know SQL, Python, and Microsoft Excel. They should have critical thinking and presentation skills for data visualization and modeling. A data analyst has to be proficient in using statistics since it is the foundation of data analytics. Also, technologies like machine learning and artificial intelligence can be handy for a data analyst.
No, advanced coding is not required for anyone who wants to become a data analyst. The focus should be on learning how data manipulation software works and having advanced computer literacy skills. It also helps to be well-grounded in statistics and mathematics.
Yes, data analysts use Excel for data entry. When certain datasets do not make sense, using Excel can provide a more precise picture. It is also great for adding rows and columns to a dataset. However, data analysts do not need to use Excel for statistical analysis since there are more advanced tools for that.
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Yes, Python is one of the most popular programming languages used by data analysts and other data experts. Python has a host of libraries and functions that make it excellent for data visualization. It is also an ideal programming language for data analysis and finding the relationship between multiple datasets.
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