What is the process that a data analyst typically does?
Posted: Thu Dec 05, 2024 5:44 am
Now that we’ve covered the context of what it’s like to be a data analyst, let’s dive deeper into what the data analytics process actually looks like. Below, we’re showing you the 5 main steps of data analysis.
Step 1: Defining the question you want to answer
The first step is to identify why you are doing the analysis and what questions or challenges you want to answer. In this phase, you will take a well-defined problem and from it create a relevant question or hypothesis that you can test. You saudi arabia mobile phone number List will define what types of data you will need and from what source. For example: The problem is that a company’s customers are not subscribing to a service after a free trial. So your question could be based on this: “What strategies would increase customer retention?”
Step 2: Data Collection
Once you have a clear question, you are ready to start collecting data. Data analysts usually collect structured data from primary or internal sources, such as a CRM or email marketing tool. They may also use data from secondary and external sources, which include government portals, Google Trends , and data shared by large organizations such as UNICEF or the UN .
Step 3: Data cleaning
Once you have collected your data, you need to clean it to make it suitable for analysis. You need to clean your entire data set. There may be duplicates, anomalies, or missing data that can distort how the data set can be interpreted, so all of these things need to be cleaned. Data cleaning can be time-consuming, but it is crucial to the accuracy of your results.
Step 4: Data analysis
Now in data analysis, the way you will analyze the data depends on the question you are trying to answer and the type of data you are dealing with, there is regression analysis, cluster analysis and temporal analysis (these are just a few). In the next topic we will talk a little about the techniques.
Step 5: Interpret and share the results
This last step is where the data is transformed into valuable insights. Depending on the type of analysis you are conducting, you will need to present your insights in a way that non-experts in the field can understand, for example, as a graph or dashboard. In this step, you will demonstrate what the analysis says based on the initial question you asked, and you will also show how it helps the company's stakeholders and what the next steps will be. Here, it is also a good idea to highlight the limitations of your data analysis and show what the next analyses will be.
7. What tools and techniques do data analysts use?
Just like web developers , a data analyst uses a variety of different tools and techniques. What are they? Let’s take a look:
Data analytics techniques
Before we start talking about the techniques, let's distinguish the differences between the types of data you might work with: Quantitative and qualitative. Starting with quantitative, quantitative would be anything that is measurable, for example, the number of people who said “Yes” in a certain Facebook survey , or the number of sales in a given year. Qualitative data, on the other hand, cannot be measured, being things that a person said in an interview or the text in a part of an email. As a data analyst you will generally work only with quantitative data; however, sometimes you will need to work with qualitative data, so it would be good to master and understand both. Now let's talk about the most common data analysis techniques:
Regression Analysis: This method is used to estimate or model a relationship between a set of variables. You might use regression analysis to see if certain variables (such as how many Instagram followers a famous actor has and how their latest movie grew their following) can be used to accurately predict other variables (such as whether the actor's next movie will be a hit). Regression analysis is primarily used to make predictions; however, it is important to note that regressions only tell you whether there is a relationship or correlation between things, and do not tell you anything about the effect.
Factor analysis: This technique helps analysts uncover the hidden variables that drive people’s behavior and choices. Ultimately, it serves to condense many variables into a few “super variables,” making the data easier to work with. For example, if you have three different variables that represent customer satisfaction, you might use factor analysis to condense those variables into a single customer satisfaction score. This is why factor analysis is sometimes called dimension reduction.
Cohort analysis: A cohort is a group of users who have common characteristics during a given period of time. For example, all the people who bought a cell phone during the period of March, these people may be considered a distinct cohort or group. In cohort analysis, customer data is divided into smaller groups or cohorts; so, instead of treating all customer data as if it were the same, by using cohorts, companies can see patterns and trends over time. By recognizing these patterns, companies can offer a more customized service.
Cluster Analysis: This technique is all about identifying structure within a data set. This analysis essentially segments data into groups that are internally homogeneous and externally heterogeneous. In other words, objects within a cluster should be similar to each other but different compared to objects in other clusters. Cluster analysis allows you to see how data is distributed across a data set where there is no predefined class or group. In marketing, cluster analysis can be used to identify multiple target audiences within a customer base.
Time-based analysis: Simply put, time-based data is a sequence of data points that measure the same variable at different points in time. Time-based analysis, then, is the collection of data over different intervals over a period of time for the purpose of identifying cycles and trends. This analysis is useful because it allows you to make accurate predictions about the future. For example, if you want to predict the future demand for a product, you might use time-based analysis to see the demand for that product during certain periods.
These are just a few of the many techniques that data analysts use, and we’ve only scratched the surface in terms of what you can do with each technique and how it’s used. Other well-known techniques include Monte Carlo simulations, scatter analysis, discriminant analysis, and text and content analysis (the latter is for analyzing qualitative data).
Step 1: Defining the question you want to answer
The first step is to identify why you are doing the analysis and what questions or challenges you want to answer. In this phase, you will take a well-defined problem and from it create a relevant question or hypothesis that you can test. You saudi arabia mobile phone number List will define what types of data you will need and from what source. For example: The problem is that a company’s customers are not subscribing to a service after a free trial. So your question could be based on this: “What strategies would increase customer retention?”
Step 2: Data Collection
Once you have a clear question, you are ready to start collecting data. Data analysts usually collect structured data from primary or internal sources, such as a CRM or email marketing tool. They may also use data from secondary and external sources, which include government portals, Google Trends , and data shared by large organizations such as UNICEF or the UN .
Step 3: Data cleaning
Once you have collected your data, you need to clean it to make it suitable for analysis. You need to clean your entire data set. There may be duplicates, anomalies, or missing data that can distort how the data set can be interpreted, so all of these things need to be cleaned. Data cleaning can be time-consuming, but it is crucial to the accuracy of your results.
Step 4: Data analysis
Now in data analysis, the way you will analyze the data depends on the question you are trying to answer and the type of data you are dealing with, there is regression analysis, cluster analysis and temporal analysis (these are just a few). In the next topic we will talk a little about the techniques.
Step 5: Interpret and share the results
This last step is where the data is transformed into valuable insights. Depending on the type of analysis you are conducting, you will need to present your insights in a way that non-experts in the field can understand, for example, as a graph or dashboard. In this step, you will demonstrate what the analysis says based on the initial question you asked, and you will also show how it helps the company's stakeholders and what the next steps will be. Here, it is also a good idea to highlight the limitations of your data analysis and show what the next analyses will be.
7. What tools and techniques do data analysts use?
Just like web developers , a data analyst uses a variety of different tools and techniques. What are they? Let’s take a look:
Data analytics techniques
Before we start talking about the techniques, let's distinguish the differences between the types of data you might work with: Quantitative and qualitative. Starting with quantitative, quantitative would be anything that is measurable, for example, the number of people who said “Yes” in a certain Facebook survey , or the number of sales in a given year. Qualitative data, on the other hand, cannot be measured, being things that a person said in an interview or the text in a part of an email. As a data analyst you will generally work only with quantitative data; however, sometimes you will need to work with qualitative data, so it would be good to master and understand both. Now let's talk about the most common data analysis techniques:
Regression Analysis: This method is used to estimate or model a relationship between a set of variables. You might use regression analysis to see if certain variables (such as how many Instagram followers a famous actor has and how their latest movie grew their following) can be used to accurately predict other variables (such as whether the actor's next movie will be a hit). Regression analysis is primarily used to make predictions; however, it is important to note that regressions only tell you whether there is a relationship or correlation between things, and do not tell you anything about the effect.
Factor analysis: This technique helps analysts uncover the hidden variables that drive people’s behavior and choices. Ultimately, it serves to condense many variables into a few “super variables,” making the data easier to work with. For example, if you have three different variables that represent customer satisfaction, you might use factor analysis to condense those variables into a single customer satisfaction score. This is why factor analysis is sometimes called dimension reduction.
Cohort analysis: A cohort is a group of users who have common characteristics during a given period of time. For example, all the people who bought a cell phone during the period of March, these people may be considered a distinct cohort or group. In cohort analysis, customer data is divided into smaller groups or cohorts; so, instead of treating all customer data as if it were the same, by using cohorts, companies can see patterns and trends over time. By recognizing these patterns, companies can offer a more customized service.
Cluster Analysis: This technique is all about identifying structure within a data set. This analysis essentially segments data into groups that are internally homogeneous and externally heterogeneous. In other words, objects within a cluster should be similar to each other but different compared to objects in other clusters. Cluster analysis allows you to see how data is distributed across a data set where there is no predefined class or group. In marketing, cluster analysis can be used to identify multiple target audiences within a customer base.
Time-based analysis: Simply put, time-based data is a sequence of data points that measure the same variable at different points in time. Time-based analysis, then, is the collection of data over different intervals over a period of time for the purpose of identifying cycles and trends. This analysis is useful because it allows you to make accurate predictions about the future. For example, if you want to predict the future demand for a product, you might use time-based analysis to see the demand for that product during certain periods.
These are just a few of the many techniques that data analysts use, and we’ve only scratched the surface in terms of what you can do with each technique and how it’s used. Other well-known techniques include Monte Carlo simulations, scatter analysis, discriminant analysis, and text and content analysis (the latter is for analyzing qualitative data).