Governance programs are built around the tools everyone knows about. The risk sits in what isn't tracked: a CSV export feeding a live dashboard, an asset with no clear owner, a report whose access history nobody has reviewed. Datalogz uses semantic intelligence to read the meaning, relationships, and business logic behind every BI asset, not just its metadata.
Static files like CSVs and Excel exports quietly feed live reports and semantic models while sitting outside standard governance tracking. Datalogz flags the high-use ones so they get reviewed like any other asset.
Every BI asset is mapped to what depends on it, from the semantic model down to individual reports, so before a source changes or an owner leaves, it's clear which reports and decisions would be affected.
Full access and change history for any report, so governance questions like who changed this or who accessed it have a documented answer instead of a guess.
Row-Level Security controls what a user can query in the semantic model. It says nothing about what they do with the results once a report renders. Datalogz watches the activity layer that RLS doesn't reach.
Access and usage are compared against normal patterns for that user and role, so abnormal behavior stands out instead of blending into routine traffic.
Activity consistent with compromised credentials or insider risk is surfaced early, giving security teams a starting point instead of a full access log to sift through.
Downloads and shares of sensitive data are logged and monitored, so exports don't happen invisibly.
Two dashboards reporting the same metric under different metric definitions isn't a formatting problem. It's a trust problem rooted in semantic inconsistency, and it's usually invisible until someone asks why the numbers don't match. Datalogz surfaces the mismatch before that conversation happens.
Duplicate reports and datasets across the BI environment are identified through semantic similarity, not just the ones a team happens to remember building.
Every calculation and metric definition in BI is catalogued, so it's possible to see exactly which formula and business logic sit behind a metric, not just what the metric is called.
Convoluted or inconsistent calculations, including conflicting semantic definitions of the same metric, are flagged for review, so a broken formula gets caught before it drives a real decision.
Once duplicate reports are found, the next step is deciding what to do with them, covered in more depth on the BI Similarity page. The semantic intelligence that connects metric definitions across reports is covered on the Semantic Intelligence page.
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Datalogz addresses three categories of BI risk: governance gaps like ungoverned static files and unclear asset ownership, security risks such as abnormal access patterns and sensitive data exposure, and misreporting risks like duplicate reports, inconsistent calculation logic, and conflicting metric definitions. Each is surfaced automatically rather than requiring a manual audit, backed by semantic intelligence that understands the business logic behind every BI asset.
Datalogz flags high-use static files, such as CSVs and Excel exports, that feed live reports and semantic models while sitting outside standard governance tracking. It also maps the blast radius of every BI asset and keeps a full access and change history, so ownership and dependency questions have documented answers.
Yes. Datalogz tracks when sensitive data is downloaded or shared and compares access patterns against what's normal for a given user or role, so unusual activity is flagged rather than lost in routine traffic.
Datalogz compares current access and usage against typical patterns for each user and role. Behavior that doesn't fit, like sensitive data being accessed or downloaded outside a normal pattern, is surfaced for review instead of blending into standard BI traffic.
Datalogz identifies duplicate reports and datasets, inventories the calculations and metric definitions behind every metric, and flags business logic that's convoluted or inconsistent with how the same metric is semantically defined elsewhere. That gives teams a chance to catch a conflicting number before it reaches an operational or executive decision.
Yes. Datalogz surfaces duplicate reports and datasets across the BI environment through semantic similarity, along with a full inventory of the calculations and business logic behind them, making it possible to see exactly where two versions of the same metric diverge in semantic meaning and why.
A one-time audit captures a single point in time and goes stale as soon as new reports, users, semantic models, or data sources are added. Datalogz monitors governance, security, and reporting risk continuously, so gaps introduced after the last audit are caught as they appear rather than at the next scheduled review.
Semantic intelligence is how Datalogz understands the meaning, relationships, and business logic behind every analytics asset, not just its metadata. For BI risk reduction, it's what makes it possible to compare metric definitions and calculation logic across reports, so conflicting versions of the same metric are surfaced before they reach a decision.
