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Advanced Analytics FAQ

This FAQ answers common questions about Advanced Analytics sources, latency, KPI definitions, and exports.

What Is Cloudaware Advanced Analytics?​

Advanced Analytics is a capability that builds dashboards, KPIs, and self‑service analysis on top of Cloudaware data using Salesforce CRM Analytics. It combines events, metrics, and shared dimensions from CMDB and other modules so you can track adoption, usage, operational health, and data quality across your environment. See Overview and Data Model for the conceptual picture.

Who Is Cloudaware Advanced Analytics For?​

Typical personas include Cloudaware administrators, product/analytics owners, SRE/operations teams, and read‑only stakeholders who rely on curated dashboards. RBAC and Salesforce permissions control who can open dashboards, edit datasets, or manage pipelines; see Operations – RBAC for details.

How Does Advanced Analytics Relate to CMDB and Other Modules?​

Most dimensions (account, application, environment, team, ownership, criticality) come from Cloudaware CMDB. Other modules emit events and metrics that are joined to CMDB attributes so you can slice analytics by business and technical context. The main subject areas are outlined in Data Model – Overview.

What Are the Prerequisites to Enable Advanced Analytics?​

At minimum, your Salesforce org must have CRM Analytics enabled and licensed (for example, Analytics Platform license and associated permission sets), and Cloudaware must be integrated with that org. You should also confirm:

  • Core Cloudaware modules (for example, CMDB, Cost Management, Compliance Engine) are enabled.
  • The integration/Analytics Platform users have the right permission sets and licenses.
  • Target users have access to the Analytics app and dashboards.

See Operations – RBAC and Ingestion – Data Manager for the operational view.

What Data Does Advanced Analytics Use and Where Does It Come From?​

Advanced Analytics relies on:

  • Cloud usage and cost feeds ingested through Cost Management and related integrations.
  • Module events from Cloudaware modules such as CMDB, Compliance Engine, Cost Management, Unified Monitoring, and others.
  • Pipeline events from ingestion jobs, dataset refreshes, and exports.

These signals are normalized into a consistent event schema and turned into metrics inside CRM Analytics datasets. See Ingestion – Event Sources and Events.

How Often Is Data Refreshed and What Latency Should I Expect?​

Data freshness depends on how jobs and recipes are scheduled in CRM Analytics:

  • Usage and module events are ingested via connections, recipes, and dataflows.
  • Recipes are typically scheduled on an hourly or daily cadence, depending on use case.
  • Dashboards always reflect the latest successful dataset refresh.

You can review the actual schedules and last refresh times in Data Manager (Jobs Monitor, Data Assets). For data‑quality guidance, see Data Health and Data Manager.

How Do I Check Whether Ingestion Is Healthy?​

Use Salesforce CRM Analytics Data Manager:

  • Jobs Monitor – check for failed or long‑running recipes and dataflows.
  • Data Assets – confirm that key datasets (usage, metrics, module subject areas) have recent successful refreshes.
  • Usage – monitor overall dataset and external upload usage relative to limits.

Within the Cloudaware docs, see Ingestion, Data Manager, and Data Health for step‑by‑step checks.

What Happens If an Ingestion Job Fails?​

If a recipe or dataflow fails, downstream dashboards that depend on its datasets may show stale data (last successful refresh) or error messages. The recommended workflow is:

  1. Review Jobs Monitor in Data Manager for error details.
  2. Check connections, credentials, and upstream modules/integrations.
  3. Fix the underlying issue, rerun the job, and confirm that the dataset refreshes successfully.

For recurring failures, consider simplifying recipes or adjusting schedules as described in Performance & Scale.

How Are KPIs Defined?​

Advanced Analytics maintains a catalog of KPIs. Each KPI is defined in terms of underlying metrics and events, including a clear description and default formula. See Dashboards & Reporting – KPIs for the authoritative list.

How Do Dashboards, Lenses, and Datasets Relate?​

  • Datasets in CRM Analytics store events and metrics at specific grains.
  • Lenses are exploratory views on top of a single dataset.
  • Dashboards combine multiple lenses, filters, and widgets to answer a set of questions.

Most curated dashboards for Advanced Analytics are built on top of a small number of shared, analytics‑ready datasets created via recipes. For a tour of the workspace, see Analytics Studio and Dashboards & Reporting.

Can I Do Deeper Analysis Beyond the Standard Dashboards?​

Yes. You can:

  • Explore datasets directly in Analytics Studio using lenses.
  • Build custom dashboards on top of the same datasets and KPIs.
  • Use segmentation, cohort, funnel, and retention techniques in CRM Analytics Studio.

Can I Use Advanced Analytics Data in External BI Tools?​

Yes. Cloudaware supports multiple patterns:

  • Use Salesforce CRM Analytics connectors and Integrations to expose data to tools such as Tableau, Power BI, and Looker.
  • Export datasets to warehouses like Snowflake or BigQuery for broader analysis; see Data Warehouse.
  • Use API‑based exports documented in APIs & Exports.

For BI‑specific options, also review BI Tools and Salesforce CRM Analytics Data Connectors.

What If I’m Approaching CRM Analytics Limits?​

If Usage in Data Manager shows that you are nearing row or external upload limits, consider:

  • Aggregating events into coarser‑grained metrics (for example, per‑day per‑service instead of per‑event).
  • Dropping unused fields and datasets as part of Data Governance.
  • Moving very long‑term history to external warehouse/BI exports instead of keeping everything in CRM Analytics.

See Limits & Quotas and Performance & Scale for guidance.

How Does Security and RBAC Work for Advanced Analytics?​

Advanced Analytics inherits Salesforce security and Cloudaware RBAC:

  • Dataset and dashboard access is controlled by apps, folders, permission sets, and sharing rules in Salesforce.
  • Row‑level security (security predicates and sharing inheritance) ensures users see only the data they are allowed to see.
  • Cloudaware’s own RBAC model determines which accounts, modules, and objects a user can access in the first place.

For configuration steps and examples, see Operations – RBAC and Data Model – Attributes for ownership and scoping dimensions.

How Do I Prevent Datasets from Becoming Too Large or Sensitive?​

Use a combination of:

  • Data governance – only ingest useful fields, standardize names, and document ownership; see Data Governance.
  • Recipe design – filter out irrelevant records early and aggregate where possible; see Data Preparation.
  • Security settings – apply row‑level predicates and app/folder permissions instead of copying sensitive datasets.

Where necessary, keep highly sensitive details in source systems and expose only derived metrics or masked attributes to Advanced Analytics.

Do You Support Custom Reporting?​

Yes. Users can create detailed custom reports and dashboards on top of the same datasets that power curated views. You can start from existing datasets and KPIs in Analytics Studio and build lenses or dashboards tailored to your own questions.

Do I Need to Create Dashboards Myself?​

Not necessarily. Many dashboards are available out of the box. In addition, your Cloudaware account team can help design and configure new dashboards for your environment as part of onboarding or ongoing enablement.

Are Cloudaware Dashboards Interactive?​

Yes. Dashboards are fully interactive: when you click on charts, tables, or other widgets, filters and facets are applied across the dashboard so that data is reorganized based on your selection. You can also adjust time ranges, apply filters, and drill into underlying records.

Dashboards Look Stale or Wrong. Where Should I Start?​

Use this sequence:

  1. Confirm that source systems (cloud providers, CMDB, billing exports, ticketing, etc.) have the expected data.
  2. In Data Manager, check that relevant connections and recipes are running successfully and that target datasets refreshed recently.
  3. Use Data Health to check freshness and common quality issues (missing data, discrepancies, permission problems).
  4. Inspect dashboard filters, bindings, and date ranges in Analytics Studio.

If only a single widget looks wrong, it is often a binding or filter issue rather than a dataset problem.

How Do I Plan for Growth and Performance?​

Follow the guidance in Performance & Scale:

  • Filter and aggregate data as early as possible.
  • Separate raw vs analytics‑ready datasets.
  • Schedule heavy recipes thoughtfully and avoid overlapping long‑running jobs.
  • Monitor dataset growth and adjust retention and aggregation strategies as usage increases.

Where Can I Find Definitions for Specific Fields or Tables?​

Use the reference docs: