video1.0<iframe src="https://www.loom.com/embed/3ebade9714024df9841cfa6ef5d46a98" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/os/loom-media-content/loom/a436116f-02ce-4520-8fbb-7301462a1674/1621dc1f-3702-4525-84ba-fad829566798/sessions-thumbnails-3ebade9714024df9841cfa6ef5d46a98-98fea868ee040938.gif/binary?authToken=eyJraWQiOiJtaWNyb3MtZ3JvdXAvdGRwLW9zLWFyY2hldHlwZS9ibjgxbzFhNGpwMTAxZjFwIiwiYWxnIjoiUlMyNTYifQ.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.lxIw1gISgS8mipzleF5QeAz1yddQTE1_Ecn_8yFveoaQJQfIUjC-6seKoD2qOghZ1NgDLa3nD_5_EVspYHsb37YiAfgrSN7ekgr7Y8P_D4_jT8_uFTAUWT5CMh0-nf_sG9pFfCpwQBRtBApOTGFu6bFwr8_xfj3G785ZbWTKfvt-6GC3wBdhZuELswFX8zlb-ddcZnsqCSH_XeRLJzjgMBgQk2PiQOgXj092_dN8kd2mojFgcufpdBzATheyNTcEcY5QlcI6CVjFzduiyO0nwmnq3qnVfyo7B-t6S_0-4AxJnyBMkHykCR_Gpvr71aiP1vpqxtTXKVTyZ5DJl4OQPQ&Expires=1786911110&Signature=QqplBSwB8UThlXDQuIM7YzWN5MRK3MqU9tXvzWTv9d0TujzzqI9guSgLYgKylOYLOrX9ScYKr94i1qyzFHxs%7E1KUie0G8CxaFMeQ9HzSQ-wdJJ4NIiF7lN9m6seoyM5FDo6iGRIqZe9yMPQoKJnEGlacXjX0fOyS5dih5KbzbBOjcoD72UIfyPtYnzW5Ta7fPxBZsZIqKoOBCqRz7ZVrt3S06xgqjvbEbpK%7E7LCmRfbFa7UlcoKvLNi06RrV4MZFemugJZkWmQrsS7CQzi8APUVJ149TQLtvnnn6rlgm-yZzwTuVbW6uGCxVz3HudZQr51YAaD2dU9cvrTLeoRWM7A__&Key-Pair-Id=KQOSYIR44AIC03677.757The meeting presented the redesigned Assets Data Manager UI, highlighting terminology changes (adapters→data sources, ingest→fetch), a simplified menu, and cloud-enabled connectors that allow in‑UI fetches. Silvia demonstrated end-to-end workflows: fetching from a discovery tool, transforming, mapping, cleansing, merging data, creating saved searches and dashboards, and importing selected data into Assets schemas. Key next steps: use cloud-enabled adapters where available, schedule refreshes for regular imports, and consult provided how-to videos and blog for setup and deeper guidance. ### Data manager purpose and capabilities 2:34 - Data Manager ingests data from multiple sources (discovery tools, CMDBs, endpoint solutions, ERP, databases, flat files) to normalize, transform, reconcile and merge for reliable asset data. - The tool addresses gaps where discovery tools lack complete coverage by consolidating attributes from disparate systems (examples: Intune, Azure AD, endpoint tools). - Core workflow remains ingest/transform/merge/view, but terminology and UI flow changed to improve admin experience. ### New ui navigation and object classes 6:06 - Main Data Manager menu reorganized: Get started, Object classes, Data visualization, and Settings. - Object classes now surface five out-of-the-box classes (Compute, Network, People, Peripherals, Software); attributes within these classes are fully editable though creating new object classes is not yet available. - Data sources replaced the previous 'adapters' terminology; object class view now shows available data sources per class for clearer configuration and ingestion. ### Data sources and cloud-enabled connectors 10:49 - Over 20 built-in data sources are available (examples: Lansweeper, SCCM, ServiceNow, Intune, Azure AD); some connectors are marked cloud-enabled. - Cloud-enabled data sources allow fetching data directly from the Data Manager UI without using the external Data Manager Client. - Connector configuration tests the integration immediately on save; non-cloud connectors still require the Data Manager Client for fetches. ### Live fetch and transform demonstration 20:11 - Demonstrated fetching data from Lansweeper using 'Fetch from the Cloud' to bring raw records into Data Manager. - Transform step examples: concatenating fields to create a 'type' field and converting string fields to date/time formats; transforms run immediately and create new transformed columns. - UI shows raw, transformed and audit logs for ingestion; transforms are applied before mapping and cleansing. ### Mapping, cleansing and merging workflow 22:54 - Mapping: select only needed attributes from a data source to map into the object class; unmapped attributes can be omitted to avoid clutter. - Cleansing: recommended rules include removing records with missing primary keys and deduplicating primary keys; cleanse execution reports counts for eliminated and retained records. - Merge: Fetch/Transform/Map/Cleanse/Merge are accessible on one screen; merge reconciles records across sources, applies attribute priority, normalizes via dictionaries, and produces consolidated object records. ### Searches and data visualization 28:14 - Saved searches can filter merged object records (examples: records present in specific sources or missing antivirus) and are used as inputs for imports and charts. - Dashboards: create dashboard groups, add charts using saved searches and select chart type (bar, pie, Venn diagram); charts are based on object class attributes (e.g., OS version, data source). - Visualizations help identify discrepancies across sources (counts, missing fields, percentage at risk) to prioritize data remediation. ### Schemas and importing data manager data into assets 36:09 - Assets schemas (EAP) follow a common data model: system schemas include people, hardware assets, geography, external organizations; hardware assets include end-user compute, infrastructure, network, peripherals, models. - Import workflow: create Data Manager import, select a saved search, choose whether to auto-create object types/attributes or map manually (manual mapping recommended), then map Data Manager attributes to schema fields and enable import. - Imported records populate schema objects and relationships pre-modeled in Assets; import logs show history and enable auditability of imports. ### Relationships, modelling and q&a 43:23 - Relationships are not separately imported; Assets uses predefined schema relationships and attribute references so imported data populates relationships automatically when attributes reference related objects. - Best practice: perform modeling and import order (e.g., import chassis, models, OS, services separately) so referenced objects exist and relationships materialize. - Q&A highlights: custom object classes cannot yet be created (roadmap item), scheduling refreshes (daily/weekly/monthly) is supported, and Data Manager imports into schemas are the ones counted for usage. ### Closing, resources and next steps 50:47 - Silvia apologized for the Zoom glitch, confirmed session recording and availability of how-to videos and a blog post with step-by-step setup (~15 minutes total video length). - Next steps encouraged: use cloud-enabled adapters where possible, schedule refreshes for automated imports, and consult provided materials if any step needs repetition. - Action items: attendees to review blog/how-to videos, consider import/mapping strategy for their schemas, and expect schema features (hardware assets table) and custom object class support in future releases.