Data is an increasingly important part of our economy, business operations, and daily lives. A greater ability to collect data of consumers through digital technologies has meant that marketers and managers have more information that they can use to reach potential customers. Businesses can use data to optimize their supply chains to enhance quality assurance.
In order for data to be actionable, two main processes must occur. Data must be collected, and it must be analyzed. Data collection can be conducted in a variety of ways. Data loggers, IoT sensors, and other digital devices are often used to collect environmental data, which can then be logged, recorded, and stored in a database.
Data analyzers and software can then be used to organize datasets, recognize patterns, and draw conclusions from data that has been gathered. This process is quite different from how data was gathered in the past, when analog environmental monitoring devices such as thermometers were used to collect data, which was typically recorded using an old-fashioned pen and paper.
Luckily, data loggers and data analyzers have become much easier to use in recent years, even for the technologically uninitiated. Let’s explore data analyzers and data loggers in more detail and look at some of the developments in the world of data that have democratized the use of these helpful devices.
What is a Data Analyzer?
A data analyzer is a software that helps users to review and organize data, identify patterns in datasets, and model or forecast future outcomes based on data. Some data analyzers are specifically made for non-analysts and are very intuitive to use.
In the past, data analysis represented a significant challenge to nontechnical individuals. Often, data was stored in a relational database that a user would have to query in order to conduct an analysis of that data. In order to do so, users would have to be familiar with SQL or other programming languages in order to view, extract, or segment a database.
Today, this is not the case. Instead, data can be uploaded into a data analyzing software in a CSV, XML, or other compatible file format and it can be viewed or manipulated from a visual interface that is intuitive and user-friendly. Data analyzers often give users the option to sort data based on different categories so that analysts can get specific questions answered quickly.
Data analyzers are being used by some companies to fast-track their adherence to government compliance standards. That’s because some regulators are showing leniency to companies that introduce programs to detect corruption and arm their compliance departments with reliable data.
Many data analyzers also boast visualization functionalities that allow users to generate charts, graphs, or other visual representations of data quickly and efficiently. These data visualizations can be easily shared with team members or collaborators so that they can get an accurate, easily understandable picture of the data without having to always reanalyze it themselves.
Some data analyzers also feature predictive analytics capabilities which can be used to extrapolate what current data means for the future. Scenario generation and predictive capabilities are invaluable for business and military organizations since they allow them to plan for possible future externalities and challenges.
What is a Data Logger?
Simply put, a data logger is an electronic device that collects and records environmental data over set periods of time and at specific time intervals. They are the digital evolution of the analog data sensors of the past. Throughout history, organizations have used thermometers and other data sensors to monitor and record data about the environment.
As part of the global trend toward digitization, businesses and consumers found that digital data loggers were far more accurate, reliable, and convenient than their analog counterparts. Today’s data loggers are also easier to use than the data loggers of the past. That’s because many of them feature visual interfaces that allow users to set them up without necessarily possessing programming or computer science knowledge.
High-quality data loggers are crucial in many industries, often serving an important role in meeting quality assurance standards for products in a supply chain. The food and beverage industry, for example, must keep products at low temperatures while they’re being transported to retail stores and consumers. Similarly, companies in the healthcare industry must keep sensitive medical products at low temperatures at each step in the supply chain to prevent them from degrading or expiring.
How Does the Energy Industry Use Data Loggers?
One of the industries that benefit most from data loggers and data analyzers is the energy industry. Data loggers are used extensively to measure and record the power, current, and voltage of products in this industry.
Often, this data can be looked at in a data analyzer to identify inefficiencies and determine how energy infrastructure can be updated to minimize energy costs. Additionally, this data can be modeled in a data analyzer to predict future energy usage trends and forecast energy needs in specific regions or properties.
In some cases, data analyzers can even be used in conjunction with artificial intelligence and IoT software to automatically distribute energy and automate the functioning of smart office equipment.
Many data loggers can also be programmed to notify owners of power outages or interruptions by SMS or email.
What Importance do Safety, Reliability, and Measurements Play in Data Analytics?
In the world of data analytics, reliability refers to data that is complete and accurate. A number of confounders can make data unreliable. One example would be to look at data that is only collected for an insufficient amount of time. Drawing definitive conclusions from such a dataset might leave an analyst vulnerable to cognitive bias.
Data that is safe is free from tampering, manipulation, or surveilling by unwanted or malicious third parties. Data that is insecure can be accessed or compromised by hackers or other bad actors. This is why eliminating security vulnerabilities is key to maintaining data integrity.
Measurement in data analytics refers to how data is scaled, grouped, and categorized. Data can be categorized as quantitative and numerical, or qualitative. Qualitative data can be either nominal, meaning that it is used to name specific values, or ordinal, meaning that it is used to describe the order of values.
Furthermore, quantitative data can be either discrete, meaning that it is represented by whole numbers that are indivisible, or continuous, meaning that it’s represented by numbers that are representative of certain characteristics such as height.
Cost Savings and Ease of Use of Data Loggers
One of the primary ways that data analyzers and data loggers can cut costs for businesses and consumers is by lowering the barrier to entry when it comes to collecting and organizing data. According to Dickson Data, data loggers can be configured to operate in an almost completely automated way. This cuts down on the labor costs associated with collecting data.
Similarly, data analyzers can make the process of organizing and analyzing data far less labor-intensive. Moreover, highly technical employees are often not required to operate them. Many data analyzers also include built-in features designed to keep data secure.
In the energy industry, data loggers and analyzers can be used to identify opportunities to make electronic storage systems more efficient. They can also be used to accurately forecast energy needs for a particular property, region, or organization. That can save money down the road by ensuring energy storage systems match the needs of those using it.
In conclusion, it’s clear that data loggers and data analyzers offer increasingly user-friendly options for gathering business intelligence, cutting costs, and maximizing operational efficiency. They can protect consumers by facilitating safe supply chains, and provide the energy industry the insights it needs to continue growing.