Morgan RileyData analytics is a systematic way to look at raw data to find patterns, draw conclusions, and help make decisions. It involves analyzing data for human consumption using a variety of methods and algorithms. Data analytics can be used to look at any kind of information and find useful metrics and patterns that can be used to improve workflows, increase productivity, and cut costs.
This article talks about the basics of data analytics, including what you need to do before you start, how to collect and clean data, and the different types of analytics, such as descriptive, diagnostic, predictive, and prescriptive. It also shows some of the most popular methods and programs for analyzing data, such as Monte Carlo simulations, factor analysis, cohort analysis, and regression analysis.
In a Nutshell
- Data analysis is a method used in science to examine unprocessed data to look for patterns, make inferences, and support decision making.
- Data analysis can highlight important metrics and trends that can be used to streamline procedures, boost productivity, and cut costs.
- Data collection, organization, cleansing, and analysis are all steps in the data analysis process.
- Data analysis can be classified into four categories: descriptive, diagnostic, predictive, and prescriptive.
- Regression analysis, factor analysis, cohort analysis, and Monte Carlo simulations are a few examples of the numerous methods and tools that can be utilized for data analysis.
Understanding Data Analysis
Data analysis is a broad term that encompasses various types of data analysis. Data analysis techniques can be used on any kind of information to get useful information that can be used to make things better. Using techniques for analyzing data, you can find trends and metrics that would otherwise be lost in the huge amount of data. This information can be used to improve processes and make a company or system work better as a whole.
For example, manufacturing companies often record the uptime, idle time and work queue of several machines and then analyze the data to better plan workloads so that machines run closer to their maximum capacity.
Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set up rewards programs that keep most players active. Content companies use many of the same data analytics to keep you clicking, watching or rearranging content to get another view or another click.
Data analytics is important because it helps companies optimize their performance. Its implementation in the business model means that companies can help reduce costs by identifying more efficient ways of doing business and storing large amounts of data. A company can also use data analytics to make better business decisions and help analyze trends and customer satisfaction, which can lead to new and better products and services.
SQL
Some of the beginnings of modern data analytics can be traced back to SQL. Created in 1979, this computer language enables relational databases to be queried and the resulting data sets to be analyzed more easily. SQL is still widely used today.
Data Analysis Steps
The data analysis process consists of several phases:
- The first step is to determine the data requirements or how the data are grouped. The data can be separated by age, demographics, income, or gender. Data values can be numerical or broken down by category.
- The second step in data analysis is the data collection process. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.
- Once the data is collected, it must be organized so that it can be analyzed. This can be done in a spreadsheet or other software that can take statistical data.
- The data is cleaned before analysis. This means that they are reviewed and checked to ensure that there are no duplicates or errors and that they are not incomplete. This step helps to correct any errors before passing on to a data analyst for analysis.
Types of Data Analysis
Data analysis is divided into four basic types.
- Descriptive analysis: Describes what has happened in a given period of time. Has the number of visits increased? Are sales stronger this month than last month?
- Diagnostic analysis: Focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did the last marketing campaign influence sales?
- Predictive analysis: This refers to what is likely to happen in the short term. What happened to sales the last time we had a hot summer? How many weather models are predicting a hot summer this year?
- Prescriptive analysis: This suggests a course of action. If the probability of a hot summer measured as an average of these five weather models is greater than 58%, we should add an afternoon shift to the brewery and rent an additional tank to increase production.
Data analysis underpins many quality control systems in the financial world, including the ever popular Six Sigma program. If something is not measured correctly, whether it is weight or the number of defects per million on a production line, it is almost impossible to optimize it.
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Some of the industries that have embraced the use of data analytics include travel and hospitality, where change can be rapid. This industry can collect customer data and figure out where the problems are, if any, and how to fix them.
Healthcare combines the use of large volumes of structured and unstructured data and uses data analytics to make quick decisions. Similarly, the retail sector uses large amounts of data to meet the ever changing demands of shoppers. The information retailers collect and analyze can help them identify trends, recommend products and increase profits.In December 2021, the average total for a data analyst in the United States was just over $93,000.
Emily RobertsData Analysis Techniques
Data analysts can use a variety of analytical methods and techniques to process data and extract information. Some of the most popular methods are listed below.
- Regression analysis involves analyzing the relationship between dependent variables to determine how a change in one may affect the change in another.
- Factor analysis involves taking a large data set and reducing it to a smaller data set. The goal of this maneuver is to try to uncover hidden trends that otherwise would have been harder to see.
- Cohort analysis is the process of breaking a data set into similar data groups, often divided into a demographic group of customers. This allows data analysts and other users of data analysis to drill down into the numbers relating to a specific subset of data.
- Monte Carlo simulations model the likelihood of different outcomes. Often used for risk mitigation and loss prevention, these simulations incorporate multiple values and variables and are often more predictive than other data analysis approaches.
- Time series analysis tracks data over time and solidifies the relationship between the value of a data point and the occurrence of that data point. This data analysis technique is often used to detect cyclical trends or to project financial forecasts.
Data Analysis Tools
In addition to the many different mathematical and statistical ways to crunch numbers, data analysis has changed quickly in terms of how it uses technology. Today, data analysts have a wide range of software tools at their disposal to help them get data, store information, process it, and make reports.
Data analysis has always been linked to spreadsheets and Microsoft Excel. Now, data analysts also often interact with raw programming languages to transform and manipulate databases. Open source languages such as Python are often used. For statistical analysis or graphical modeling, more specific tools for data analysis, such as R, may be used.
Data analysts also have help when it comes to reporting or communicating results. Both Tableau and Power BI are data visualization and analysis tools that allow you to collect information, perform data analysis, and distribute the results through dashboards and reports.
Other tools are also emerging to help data analysts. SAS is an analytics platform that can help with data mining, while Apache Spark is an open source platform useful for processing large data sets. Data analysts now have a wide range of technology capabilities to further enhance the value they bring to their business.
Wrap Up
Data analytics is an important tool for many businesses, such as healthcare, retail, travel, and hospitality, because it helps them make quick decisions, spot trends, recommend products, and increase sales. In the United States, the median pay for a data analyst in December 2021 was just over $93,000. This was because the need for data analysts was growing.
FAQs about Data Analytics
Examining and interpreting data to derive important insights and conclusions is the process of conducting data analysis.
Data analytics is crucial since it aids in performance optimization, cost reduction, and better business decision making for firms. In order to develop new and improved goods and services, it can also help discover trends and levels of client satisfaction.
Regression analysis, factor analysis, cohort analysis, Monte Carlo simulations, and time series analysis are a few popular data analysis techniques.
The steps in the data analysis process typically include identifying the data requirements, gathering, cleaning, and organizing the data, doing an analysis of the data, and presenting the findings.
A number of businesses, including healthcare, retail, travel and hospitality, and finance, profit from data analytics. Data analytics can be used to pinpoint problem areas, streamline procedures, and improve business decisions.
Article Sources for Data Analytics
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