Reports live in silos, not in context
Salesforce reports answer Salesforce questions. The moment leadership wants pipeline data next to marketing spend or fulfilment data, someone is exporting CSVs into a spreadsheet by hand.
Salesforce · Business Intelligence
Amazon Quick Sight for Salesforce, built on one governed data model
Amazon Quick Sight — the business intelligence service formerly sold as Amazon QuickSight — turns Salesforce and AWS data into governed, interactive dashboards. Twopir Consulting builds the pipelines, SPICE datasets, row-level security and embeds behind them. Reporting that runs inside your Salesforce org, not alongside it.
- 12+ Years Delivery
- 500+ Clients
- Salesforce Partner
Sources
Salesforce & AWS data
Salesforce Redshift / S3 ERP & billing
Amazon Quick Sight
Model & govern
SPICE datasets Row-level security Dashboards
Where it lands
In the workflow
Lightning embed Scheduled reports Threshold alerts
Built around Your Salesforce org
500+
250+
40+
98%
Trusted by 500+ organizations — Twopir Consulting is a Salesforce Partner and HubSpot Gold Partner building reporting infrastructure across Salesforce and AWS.
Platforms & Capabilities We Build On
- Salesforce Partner
- HubSpot Partner
- AI Delivery
- Amazon Quick Suite
- Amazon Redshift
- Amazon S3
- SPICE Modelling
- Embedded Analytics
- Row-Level Security
The real cost is operational friction
A BI tool is rarely the missing piece. What breaks is the layer underneath it — the data model, the refresh schedule, and the question of who is allowed to see what. Every one of these is a build decision, not a licence decision.
Dashboards break when the schema changes
Native dashboards are tightly coupled to object structure. A renamed field or a new record type quietly breaks a filter nobody notices until a QBR is already under way.
Nobody owns the data model
Dashboards get built ad hoc by whoever asked first, so definitions of "pipeline", "active account" and "churn" diverge across departments and nobody agrees which number is right.
SPICE capacity and refresh schedules go unmanaged
Without someone actively tuning datasets, refresh cadence and SPICE allocation, dashboards either go stale or capacity creeps upward with nobody watching the bill.
Row-level security gets bolted on late
Teams build the dashboard first and ask "who should see what" second — so compensation, margin or account-level data ends up visible to the wrong audience until someone catches it.
What Amazon Quick Sight is, and who it is for
Amazon Quick Sight is Amazon Web Services' cloud-native business intelligence service. It connects to data sources, models them into in-memory datasets, and publishes interactive dashboards that can be secured per viewer and embedded in other applications. It is the service that was sold as Amazon QuickSight until October 2025.
It fits organisations whose reporting question has outgrown a single system — where Salesforce holds the pipeline, a warehouse holds the history, and finance or product data lives somewhere else again. It is a weaker fit where every question can be answered inside one object in Salesforce; those reports should stay native. Read the current capabilities on the AWS product documentation.
| What you may know it as | What AWS calls it now | What that covers |
|---|---|---|
| Amazon QuickSight | Amazon Quick Sight | The same business intelligence capability — dashboards, reports, SPICE datasets, row-level and column-level security, and embedded analytics. |
| No prior equivalent | Amazon Quick Suite | The wider workspace Quick Sight now sits inside, alongside Quick Flows, Quick Automate, Quick Index, Quick Research and Apps in Quick. |
| Your existing QuickSight account | Upgraded automatically | AWS moved existing customers across on 9 October 2025. Data connectivity, content, security controls, user permissions and privacy settings were unchanged — this was an interface and capability change, not a migration. |
Implement, configure, or build on it
These are three separate engagements, and most enquiries turn out to want only one of them. Twopir Consulting delivers all three, and will tell you which one you actually need.
Implement
You have no Quick Sight environment, or one that was switched on and never modelled. We build it from the data upward.
- Account, capacity and user role setup
- Connections to Salesforce, Redshift, S3, Athena and RDS
- The first governed dataset and dashboard set
- Refresh schedules and SPICE sizing
Configure
Quick Sight is running but does not match how the business actually reports. We reshape the model, not the chart.
- Dataset restructuring and calculated fields
- Row-level and column-level security rules
- Cross-source joins and blended models
- Scheduled reports and threshold alerts
Build On
You need Quick Sight to do something its console cannot do on its own. This is custom development, and it is scoped and priced as such.
- Embedding into Salesforce Lightning pages and portals
- Programmatic dataset and permission management via the AWS APIs
- Apex and Lightning Web Component work on the Salesforce side
- Custom pipelines where a native connector is not enough
Where the boundary sits
If it can be expressed in the Quick Sight console, it is configuration. Datasets, joins, calculated fields, security rules, refresh schedules, dashboards and alerts are all configuration work, however intricate they get. The moment it needs code, an API call or a deployment, it is custom development — embedding a dashboard inside a Lightning record page, provisioning users or permissions programmatically, or moving data a native connector cannot reach. The practical consequence is cost and change control: configuration is adjusted by your own admins after handoff, custom development is versioned, tested and released.
This page covers one product. For the platform work it sits inside — org architecture, automation and the wider engagement model — see Twopir's Salesforce services.
What a Quick Sight build actually delivers
The capabilities below are Amazon Quick Sight features. What Twopir Consulting supplies is the modelling, security design and delivery work that turns them into something your team can run.
Salesforce data pipelines
Direct or warehouse-staged connections keep Salesforce objects in sync with the analytics layer on a refresh schedule you control.
SPICE dataset modelling
Datasets are structured and pre-aggregated in SPICE, the in-memory engine, so dashboards stay fast as record volume grows.
Row-level security
Access is scoped by role, territory or business unit at the dataset level, so one published dashboard shows different rows to different viewers.
Cross-source blending
Opportunity, case and campaign data joined with finance, product or support data from outside Salesforce into one queryable model.
Embedded analytics
Dashboards embedded into Salesforce Lightning pages, internal portals or customer-facing apps with permission-aware access.
Forecasting & anomaly detection
Quick Sight's built-in machine learning surfaces outliers and forecasts trends without a separate data science project.
Natural-language questions
Business users query a modelled dataset in plain language, cutting the backlog of one-off report requests to the analytics team.
Scheduled reports & alerts
Snapshot reports and threshold-based alerts push to the people who never open a dashboard, so a change reaches them without a login.
What moves between Salesforce and the analytics layer
Quick Sight can read Salesforce directly for simpler cases. We stage through Amazon Redshift or Amazon S3 when data has to be blended with other systems, or when refresh performance at volume starts to matter.
| Connection | Why it exists | What moves | Direction | Who consumes it |
|---|---|---|---|---|
| Salesforce & Amazon Quick Sight | Report on pipeline next to data Salesforce does not hold. | Opportunity, Case and Campaign records read into a Quick Sight dataset on a schedule; the finished dashboard returns to Salesforce as an embedded Lightning component. | Both ways | Sales leadership, RevOps |
| Amazon Redshift or Amazon S3 & Amazon Quick Sight | Stage and blend data when a direct connection is not enough. | Modelled warehouse tables and lake files read into SPICE, where they join to the Salesforce data. | Into Quick Sight | BI and analytics teams |
| ERP and billing systems & Amazon Redshift | Reconcile CRM revenue against billed revenue. | Invoice, billing and recognised-revenue records land in the warehouse, then join to Salesforce opportunity data inside the dataset. | Into the warehouse | Finance and FP&A |
| Amazon Quick Sight & email | Reach people who will never open a dashboard. | Scheduled report snapshots and threshold alerts leave Quick Sight on a cadence you set. | Out of Quick Sight | Executives, account owners |
Connecting Salesforce to systems outside it is its own discipline — see Salesforce integration services for the work that sits underneath this page.
We don't deploy software. We build operating infrastructure
Phase 01
Data audit & source mapping
We inventory every Salesforce object, external source and existing report that feeds a business decision, then map which belong in Quick Sight and which stay native to Salesforce.
Phase 02
Model & pipeline build
We build the pipelines — direct connectors, Redshift staging or S3 landing — and structure SPICE datasets around agreed definitions, not whatever a report author assumed last quarter.
Phase 03
Dashboard & security build-out
Dashboards are built against the modelled datasets, with row-level security, refresh schedules and embed points configured before anyone sees a version described as final.
Phase 04
Rollout, training & handoff
We train admins and analysts on dataset maintenance, SPICE capacity management and dashboard authoring, so the system does not depend on us to stay current.
Most Salesforce-to-Quick Sight engagements run six to ten weeks from data audit to trained handoff. The variable is almost always how many sources outside Salesforce are being blended in — not the dashboard count.
The reporting problem, solved in production
Case Study · Manufacturing
Mid-market manufacturer
Sales and customer operations ran across several teams, every rep tracked calls differently, storage was overflowing, and reporting was always a week behind. This engagement delivered Salesforce-native dashboards rather than Quick Sight — it is included here because the modelling problem underneath is the same one Quick Sight gets bought to solve.
- Last Contact Date and Days Since Last Contact now calculate automatically across all activity sources, instead of being maintained by hand.
- Each team got a dedicated real-time view, removing the dependency on manual reports and admin requests.
- A size-and-age-based cleanup cleared the attachment backlog, and switching off Email-to-Case auto-save stopped the storage growth at its source.
- A Screen Flow replaced a fragmented multi-step logging process and standardised call outcome options.
Dashboards shipped
Sales Manager RFQ Pipeline Task & Events MSI AR & Shipping Definite Growth Call
Pipeline data next to marketing spend
When a RevOps lead needs opportunity data beside ad spend and lead conversion from outside Salesforce, we build the blended dataset once instead of repeating a spreadsheet exercise. The dashboard updates on schedule and embeds into the Salesforce pipeline review page.
Renewal risk across three systems
Customer success leaders need case data, product usage and renewal dates in one view to catch at-risk accounts early. We model that join and layer in anomaly detection, so unusual drops in usage or spikes in case volume surface on their own.
CRM revenue against ERP revenue
Finance rarely trusts a Salesforce-native revenue report on its own, and shouldn't have to. We connect opportunity data to warehouse-staged ERP and billing data so FP&A works from one reconciled model rather than two disagreeing systems.
Moving off a legacy BI tool
Firms migrating from another BI platform usually have years of calculated fields and filter logic buried in old dashboards. We rebuild that logic inside the Quick Sight data model rather than starting from a blank canvas, so nothing is silently lost in the switch.
A dashboard is only as good as the model under it
We model the data before we touch a dashboard
Most BI engagements start with a chart. We start with what "pipeline" or "active account" means across your org, because a good-looking dashboard on a bad model just displays the disagreement faster.
Salesforce fluency, not only BI fluency
As a Salesforce Partner, we read object relationships, record types and sharing rules well enough to model them correctly on the analytics side — rather than querying whatever the API happens to return.
Security is configured before anyone sees the dashboard
Row-level access is scoped at the dataset layer during the build, not requested as a fix after someone notices the wrong person can see compensation data.
We hand off a system your team can run
Every engagement ends with your admins and analysts able to maintain datasets, tune SPICE capacity and author new dashboards without waiting on us.
We will tell you what should stay in Salesforce
Not every report belongs in a BI tool. Simple single-object reporting usually stays native; Quick Sight earns its place on cross-system, high-volume or externally shared reporting. We draw that line in Phase 01, before anything is built.
Answers before the first call
Yes. AWS renamed the service on 9 October 2025: the business intelligence product previously called Amazon QuickSight is now Amazon Quick Sight, and it sits inside a wider workspace called Amazon Quick Suite alongside Quick Flows, Quick Automate, Quick Index, Quick Research and Apps in Quick. Existing customers were upgraded automatically, and data connectivity, content, security controls, user permissions and privacy settings were unchanged. If you are searching under the old name you are looking at the same service.
Most Salesforce-to-Quick Sight engagements run six to ten weeks from data audit to trained handoff. The variable is how many data sources outside Salesforce are being blended in, not how many dashboards are on the list — adding a chart to a modelled dataset is quick, adding a source that nobody has reconciled before is not.
Quick Sight can connect to Salesforce directly, and for simpler reporting that is the right answer. We stage data through Amazon Redshift or Amazon S3 when it has to be blended with other systems, when refresh performance at volume starts to matter, or when the same modelled data needs to serve more than one consumer. Staging is a cost and a maintenance commitment, so we only recommend it when the direct connection genuinely will not hold.
Usually not, and we do not recommend it by default. Simple single-object reports are better off staying native to Salesforce, where they sit next to the record and need no pipeline at all. Quick Sight earns its place on cross-system, high-volume or externally shared reporting, where native dashboards hit real limits. We draw that boundary during the data audit, before anything is built.
SPICE is billed per gigabyte per month, with an allowance included with each provisioned Author licence and additional capacity charged beyond it. As of September 2026 that allowance is 10 GB per Author and extra capacity is 0.38 US dollars per GB per month, but AWS revises this — check the current AWS pricing page before budgeting. We size and tune the allocation during the build so you are not paying for capacity nobody queries, and hand that tuning over to your team at the end.
If it can be expressed in the Quick Sight console it is configuration — datasets, joins, calculated fields, security rules, refresh schedules, dashboards and alerts, however intricate they become. The moment it needs code, an API call or a deployment it is custom development: embedding a dashboard inside a Salesforce Lightning record page, provisioning users or permissions programmatically, or moving data a native connector cannot reach. The practical difference is change control — configuration your own admins adjust after handoff, custom development is versioned, tested and released.
Your admins and analysts do. Every engagement includes hands-on training on dataset maintenance, SPICE capacity management and dashboard authoring, so the system does not depend on us being on retainer. Where a team wants ongoing support anyway, that is a separate managed services engagement rather than a condition of the build.
Next Step
Ready to run reporting on one trusted data model?
Book a discovery call and we will map what is currently living in spreadsheets, siloed dashboards and disconnected exports — then tell you which of it belongs in Amazon Quick Sight and which should stay in Salesforce.
Speak with a team that models the data before it builds the chart
