Lansweeper provides Technology Asset Intelligence solutions that deliver visibility into IT, OT, and IoT assets, including hardware and software across the entire technology environment. Founded in 2004, Lansweeper helps organizations collect and organize asset data to support security, compliance, and operational decisions. The Lansweeper platform centralizes technology asset information and facilitates collaboration between IT, security, and finance teams. It is used by enterprises, governments, financial institutions, non-profit organizations, and educational institutions around the world.
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What I like most about Lansweeper is the automatic discovery of devices across the network, which helps keep the inventory updated without manual effort. It also provides detailed hardware and software information foe each asset, which is useful when we need to check versions, installed software, or device configuration. The reporting has also been helpful in our environment. For example, we use it to identify outdated operating systems, old software versions, and endpoints that do not have antivirus installed so the IT team can take corrective action.
It's a good price point for the market, and it will scan most things.
1. Strong asset discovery capabilities that help create a more complete picture of devices and installed software. 2. Centralized inventory visibility, which makes day-to-day tracking and review easier. 3. Useful reporting and audit support for IT asset management and internal review needs. 4. Helps reduce manual spreadsheet-based tracking and improve operational efficiency.
One area that could be improved is the interface for building custom reports. In some cases it is not very intuitive and requires a bit of trial and error to get the report exactly as needed. In larger environments like ours, which include many plants, tuning the discovery and scanning configuration can require some effort at the beginning to make sure all network segments are properly covered.
As mentioned above, it comes down to the difficulty with interpreting the data. The product didn't feel modular enough, and when you're scanning and evaluating code across the business it didn't always feel like a smooth fit.
1. Data quality depends heavily on proper configuration and ongoing maintenance, so results are only as good as the discovery coverage. 2. Some reporting and workflow expectations may require additional tuning to match internal requirements. 3. The platform is a strong one for visibility, but getting maximum value still depends on process discipline and ownership inside the organization. 4. There can be a learning curve for teams that want to move beyond basic inventory use into more advanced usage.