The basic definition of data simulation is taking a large amount of data and using it to simulate or mirror real-world conditions to either predict a future instance, determine the best course of action or validate a model.
There are many different forms of simulation of data.
Some seek to approximate known conditions to determine, for example, the likelihood that oil, gas or mineral resources might be present within geological strata.
Others take large troves of data and run a variety of scenarios to see how different approaches might work. You see this kind of simulation in climate projections. Modelers run different scenarios based on existing emissions, increasing emissions and lowered emissions to estimate temperature levels decades into the future.
The purists think of data simulation in a far narrower way. They use it as a methodology to prove out a given model. The model has to perform as expected under the data simulation.
Data simulation features
There are a number of different tools that perform data simulation and their features vary depending on the desired end result. In general, the features may include:
Graphical user interface: The days of data simulation tool complexity are largely over. These days, the tool must be accessible and used by more than just a data scientist. The interface has to make it easy to formulate and run various simulations.
Model building: Data simulation is all about modeling. Model building should be easy to accomplish and should be done rapidly, supported by adequate compute power and memory.
Scalablity: Like new roadways designed to reduce traffic congestion that end up encouraging more people to drive, better and faster models have resulted in the demand to execute even large data simulations. Thus, tools have come into the market that can massively scale to accommodate the need for large data sets and huge research experiments or simulations.
Analytics integration: Data simulation goes hand in glove with analytics, most tools offer the capability.
Data import and export: Models require that data sets be imported to the model and exported from it.
See more: What is Data Modeling?
Data simulation benefits
- The ability to model behavior across complex systems.
- Using simulated data to produce a model that is relatively realistic.
- Visualization of trends and model results
- Comparison of different scenarios to determine the ideal course of action or the consequences of an intended course.
- Business insight to illuminate top management strategy, and direct promotional, sales, or marketing efforts.
Data simulation use cases
- Running what-if scenarios to look at various alternative strategies, approaches and configurations
- Assessment of simulated data to determine the factors that are most influential to a given system or course of action.
- Discrete-event data simulation zeroes in on a specific scenario like sales rates following a campaign announcement or a Black Friday sale.
- To provide proof that a model is fully understood: For example, models of voting behavior have been bad as of late, as have many hurricane pathways. The simulation of data sets can be done on such models to unearth unforeseen factors or prove their validity.
“For example, engineers can model where data is generated and visualized to see how a model, forecast or analysis works,” said Greg Schulz , an analyst with the consulting firm StorageIO Group. “The resulting data is then compared to known or expected results.”
- Application development: Another variation is where data for subsequent analysis does not exist yet, so for downstream app tool development, data is created and simulated to feed into the apps as they are being developed.
- Oil and gas: The oil and gas industry performs large-scale simulations of data sets. Over the decades, the industry has amassed databases of rock formation using older methods. New simulation and modeling tools can go through this data and simulate it against modern 3D scans of formations to zero in on potentially overlooked areas of hydrocarbons. The value in this is that it often can be done without sending out another drilling team. In fact, it is being used to avoid the norm of dozens of drilling failures in trying to find oil or gas reserves.
- Digital twins: These are data simulations of actual physical equipment, such as a gas turbine, a power plant or other industrial facility. The idea is to gather data from the physical system and create a digital copy or twin of the real thing. Engineers can then run simulations on the twin based on running it hotter, faster, changing certain configurations, figuring out how to reduce maintenance costs, increase output or other scenarios. And all without actually doing anything to the physical equipment. The twin helps those involved to better know how to proceed in the real world.
Data simulation software makers
There is an active and thriving market for data simulation tools, and there are tools for many verticals.
These are some of the top providers of data simulation software:
- Siemens EDA
- Rockwell Automation
- Hexagon Manufacturing Intelligence
- 4X Diagnostics
- Dassault Systemes
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