Regardless of what your RFID application may be, its initial phase involves the data collection of tag information by the interrogating RFID readers. The readers can then synchronize to a data store of some kind such as a database or spreadsheet using the RFID middleware. Most applications collect a repository of RFID data that can then be analyzed based on the requirements of the particular application or in concert with an enterprise ERP system.
Data analytics – the examination of raw data with the purpose of drawing conclusions about that information – is now more and more being used to allow companies to make better business decisions about their data. An important part of this data analytics process is data visualization.
Data visualization is the process used to present to people the significance of their data by placing it in a visual context of graphs, charts, and tables. Patterns, trends, and correlations that might go undetected in text-based data or simple spreadsheets can now be exposed and recognized easier with data visualization techniques. Data visualization tools often have dashboard user interfaces to help tell the “data story”.
Data visualization is a powerful form of visual communication. It involves the creation and study of the visual representation of data, meaning “information that has been abstracted in some schematic form, including attributes or variables for the units of information”.
A primary goal of data visualization is the clear and efficient communication of information to users by using statistical graphics, plots, information graphics, tables, charts, and other “data viz” formats. Effective visualization helps users in analyzing and reasoning about their RFID data. It also allows the RFID data to be correlated with other enterprise data to form a bigger picture. Data Visualization is important because it makes complex data more accessible, understandable, and usable.
Engineers may have particular analytical tasks, such as making comparisons, making predications about future company behavior, or understanding causality, and the design principle of the graphic (i.e., showing comparisons or showing causality) is used to accomplish this task.
The rate at which data is generated has increased, driven by an increasingly information-based economy. Data created by RFID sources and other big data input sources such as sensors now require techniques such as data visualization to summarize and present the findings from all the input data in a timely and elegant fashion.
It is important to note that data visualization is both an art and a science. The newly emerging field of data science is now spawning a new generation of data scientists to help address this challenge. The ideal data scientist is an equal combination of computer scientist, statistician, and subject matter exert in their particular domain. The artist role comes in when choosing the best representation techniques and other important, yet subtle nuances as selecting the best colors, orienting of the directions of the graphs, font selection, infographics creation, and other related skills.
Data Visualization can be an important part of your enterprise IT system. Taking the time to understand this newly emerging field and how it can be used to better understand your RFID data can be a key part of your business.
Got any great tips on data visualization and its relationship to RFID? Let us know in the comments!