When most people think about data analysis, they think about data manipulation and analysis in a tool like Microsoft Excel The reality is that data analysis involves a wide variety of tools and many different techniques for managing and understanding the story the data tells.
What is Data Analysis? Data analysis is used in a completely different way when you are talking about business data, production data, marketing data, or data related to the industry and business in which you operate.
In this article, you will learn about the different aspects of data analysis, what they mean, and how they are commonly used throughout.
Data Collection
The first step in any data analysis is data collection. It simply means collecting data from all the sources containing the information you need.
Data can include any of the following and more:
- Plant controllers
- Someone is manually entering data into a computer.
- Sensors that measure temperature, pressure and more.
- Cloud data sources.
- Information from the Internet, such as weather data or government databases.
- Databases hosted on your company’s network.
- supply chain needs or constraints;
- cost reduction
- sales increase
- customer needs and behavior
- Forecasting future sales or market demand.
- Logistics and delivery.
- Temperatures and pressures
- Parts or products manufactured
- Raw materials used.
- Defective parts scrapped.
- Fault counter and alarm.
- OSIsoft: This company has been around for decades and includes “integrators†or drivers that can get data from virtually any processor, sensor, etc. Etc. or database.
- Factorytalk: A longtime leader in automation, Rockwell Automation has released its own data historian called Factorytalk to help its customers collect data from machine processors.
- Aveva: Formerly known as Wonderware, AVEVA Historian promises to provide “open access” to machine data such as process data, alarms, events, etc.
- Iconics: smaller player. In the data historian market, Iconics promise to provide “high-speed archiving” so that the resolution of the stored data matches what was originally going on on the machine.
- Metabase: An open source (free) solution that touts itself as allowing people in your organization to “ask questions and learn from dataâ€.
- Tableau: A popular data visualization platform used across many industries. Connectivity to many different data sources is available.
- Whatagraph: Popular with marketing agencies because it makes it easy to create clear reports. The tool includes automatic generation of reports and can automatically email them to anyone.
- JasperReports: This is another open source reporting solution. Its strength lies in its ability to output reports in a variety of formats such as printed documents, PDFs, and web reports.
- Exploratory Data Analysis (EDA): This involves looking for patterns in the data to identify new trends or explore new information.
- Endorser. Data Analysis (CDA: This involves using all collected data to try to determine if the predicted correlations are correct.
- Rapid Miner: An excellent open source predictive analytics system written in Java. It supports machine learning, predictive analysis, and text mining.
- Sisense: Licensed business intelligence software that scales to large organizations. It includes an excellent reporting module.
- Oracle: One of the leading names in the data industry, Oracle offers a data mining feature in SQL that enables organizations to leverage the data stored in an Oracle database.
- IBM Cognos: This software is capable of processing large amounts of data to identify important trends. They can be used to create reports for management or others.
- SAS: Another big name in the data industry, Statistical Analysis System (SAS
The main challenge for many organizations is figuring out what technical tools are available to collect this information. In most cases, software is required to connect to this remote device or data source and then retrieve it into an internal database or data archiving system.
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These storage areas are often referred to as “data stores”.
Once the information has been collected in a data warehouse within an organization, various tools can be used to conduct the actual analysis of the data.
Business Intelligence
After collecting the data, the next step is to decide what to do with all this data. When it comes to business intelligence, the data you need should help your organization make better business decisions.
Business Intelligence (BI) reports and dashboards help managers and other business leaders better understand trends and gain insight into various aspects of the business.
These aspects include:
Collecting data from all of these different systems in your organization allows you to make connections between information that might not have been possible before.
Manufacturing Intelligence
The difficulty when it comes to collecting data on manufacturing processes is that there are usually a lot of them.
If you think of a typical manufacturing plant, every single machine in the shop collects tens to hundreds of data points, which include:
In most cases, production equipment is automated using a programmable logic controller (PLC). These devices not only control the equipment according to the way they are programmed, but they also collect and collect data from that equipment.
Receiving data from these PLCs requires software that runs on a server on the same network as these PLCs. There are many vendors who have written software to transfer data from these controllers to a data archiver or database.
Data historian leaders in this area include:
Almost all of these software vendors include data analysis tools in addition to their data archiving solutions. Choosing the right data collection and analysis solution for your manufacturing facility really depends on the controllers you use, how you want to store the data, and how much you are willing to spend.
Visual display of information
The most popular tool for collecting, analyzing and visualizing business data is Microsoft PowerBI
PowerBI is a powerful visualization tool offered by Microsoft that allows you to enter data from many different data sources. Then you can slice the data into various pie and bar charts, line charts, tables, and more.
The ability to combine information from different data sources allows you to find correlations that were previously impossible. This is the magic of modern data analysis. It allows you to get information that was previously impossible with tools that allow you to visualize data from many sources.
PowerBI is not the only application that can process and visualize data in this way. In fact, the market for just such instruments is constantly growing.
Leading data visualization tools today include:
The option you decide on really depends on the investment you or your organization wants to make. Fortunately, there are great open source options out there if you need to get started.
Data mining
One of the most effective new methods of data analysis is so-called data mining.
Data mining focuses on using statistical modeling to extract patterns and trends from a large amount of data in order to predict future trends.
Applications that can perform statistical mining analysis are highly specialized and often need to be customized for a specific application or situation.
Types of data mining analysis include:
Some of the leading data mining software available on the market today include:
As you can see, data analysis has many facets, and the tools you need to use really depend on what you hope to extract from that data.
Advances in data analysis continue to evolve every year, and any company or organization hoping to stay ahead in their industry should be aware of all available data analysis tools and make full use of them.
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