Most buy-side dashboards aren’t real-time because the BI tools displaying them were architected for an era of slow data warehouses. They pull data from the source on a schedule for an extract-based BI by design, store it in a proprietary engine, and serve users from that snapshot until the next refresh runs. The interface presents as live. The underlying data is anywhere from minutes to hours old.
Cloud data warehouses have eliminated the technical reason this architecture existed. Snowflake, Databricks, and warehouses hosted on AWS or Azure serve queries on billions of rows in seconds. The foundation has caught up. The reporting and analytics layer sitting on top has not.
The Architectural Gap
Traditional BI tools were designed around a specific assumption: data warehouses were slow, so the BI layer had to maintain its own internal storage to deliver responsive dashboards. That assumption shaped every downstream design choice. Extracts, refresh schedules, proprietary in-memory engines, pre-aggregated rollups. Most extract-based BI tools running at asset managers today are still built on this stack, refreshing nightly, hourly, or half-hourly and serving users from a copy of the warehouse that is, by definition, out of date.
The consequences are predictable:
Stale data: Dashboards show the state of the data at the last refresh, not the current state. Investment, risk, and IR teams have learned to mentally adjust for this gap, which works until a question becomes time-sensitive.
Data reconciliation challenge: Because the BI tool maintains its own data copy, that copy drifts from the source. Numbers in the dashboard don’t always match numbers in the underlying system. Reconciling them is a recurring operational chore that absorbs analyst time and erodes trust in the dashboards themselves.
Scaling limits: Most enterprise BI tools struggle with datasets that exceed a few million rows. Asset managers running multi-strategy books, with a very large number of rows of position, transaction, and exposure data, run into these performance limits constantly. Standard workarounds (earlier aggregation, narrower datasets, premium tiers) reduce the analytical flexibility that justified the BI investment in the first place.
Where Does The Gap Show Up Day-to-Day?
The architectural gap surfaces as operational problems that buy-side teams handle daily.
Real-time risk aggregation across a multi-strategy book hits the performance limits in the BI tool. Dashboards time out or return partial data. Risk teams either accept pre-aggregated rollups that mask underlying detail or build workarounds outside the BI tool entirely.
Different dashboards refreshed on different schedules report different AUM, NAV, or exposure numbers for the same point in time. None of them match the current warehouse state. Reconciling versions and explaining the differences to leadership consumes hours every week.
Multi-fund and multi-entity aggregation, a core buy-side requirement, is handled cleanly by almost no off-the-shelf BI tool. Rolling exposure or performance across funds, sleeves, share classes, and SPVs typically requires custom modeling or off-platform consolidation. The work often ends up in Excel.
Investor reporting bears the cumulative impact. IR teams need reliable reports with role-based views and scheduled distribution. Most modern BI tools do not support branded reporting natively. IR gets built outside the dashboard environment or with limited flexibility.
What Does a Cloud-Native Analytics Layer Look Like?
The architectural fix is straightforward in concept: close the gap between the cloud data warehouse and the cloud-native analytics layer that consumes it, with no extract layer in between.
In practice, that means a few specific things. Every dashboard interaction queries the warehouse live, not a cached copy. A governed semantic layer defines metrics, dimensions, and access rules once, so AUM, NAV, exposure, and performance carry consistent meaning across every dashboard and report. Performance scales with the warehouse rather than against it, so risk aggregations and drill-downs operate on the underlying data, not on pre-aggregated rollups. Paginated, audit-ready reports for IR and regulatory use are generated directly from the same live data. The interface is built for the business users who actually consume the data, not for BI specialists who model it.
The practical effect is that the analytics layer stops behaving as a separate system to reconcile against the warehouse and starts behaving as the warehouse’s natural front-end.
The Analytics Partnership
“Our clients need faster insights without compromising data integrity,” said Gurvinder Singh, Founder and CEO of Indus Valley Partners, when announcing the firm’s analytics partnership with Sigma.
The combined platform serves as the unified front-end for dashboards, reports, and embedded analytics, running live queries directly against the IVP Data Warehouse with no intermediate extract layer. The governed semantic layer is powered by IVP’s buy-side data model, so standardized metrics carry through every dashboard and report. For IVP clients, real-time analytics for alternative asset managers is available natively, with no integration project required.
The Next Phase
The infrastructure to deliver real-time analytics on the buy-side is no longer the constraint. The constraint is the reporting and dashboarding layer above it, the one designed for an era of smaller datasets and slower warehouses. Firms moving past extract-based BI now will spend the next decade making faster, better-supported investment, risk, and IR decisions.
