DurgaPulse - BI as a service for modern enterprises
A fully managed analytics service from Durga Analytics. We ingest, model and visualise your data, deliver reliable dashboards and alerts, and run your BI platform end-to-end, so business teams focus on decisions rather than infrastructure. Most BI programmes stall in fragmented reports, ad-hoc Excel models and overloaded central teams. DurgaPulse turns BI into a predictable service: a governed, productised analytics layer with SLAs, clear ownership and reusable data products that business teams can actually trust.
Fixed monthly subscription - Data ingestion, modelling, dashboards, alerts and support - Power BI, Tableau, Looker or custom
DurgaPulse in four points
- End-to-end managed BI, from source systems to executive dashboards
- Covers finance, sales, operations, risk and customer-experience analytics
- Data pipelines, semantic models and governance included in the service
- SLA-backed support with incident, change and enhancement workflows
- Works with your cloud and your tools: Power BI, Tableau, Looker or custom
- Starts with one domain and scales to multi-country, multi-BU enterprises
- Optional advanced analytics - forecasting, churn and anomaly detection - layered on the same governed foundation
Why we built it
To make trustworthy analytics a utility that any enterprise can switch on, turning BI from a stalled internal project into a governed, productized service with clear ownership and predictable cost.
What it delivers
To own the full analytics lifecycle for our clients, from ingestion and modelling through visualization, access control and monitoring, so business teams consume governed BI on tap and act on numbers they trust.
Why BI programs stall
Most BI programmes stagnate. Reports multiply faster than anyone can govern them, ad-hoc Excel models proliferate, and a central team ends up firefighting requests instead of building anything durable.
Because there is no shared semantic layer, the same metric is defined three ways in three dashboards. Leadership loses trust in the numbers, and every important meeting starts with an argument about whose revenue figure is correct.
Pipelines are brittle and under-documented, so when a source system changes, dashboards break quietly and are only discovered when someone senior notices a number looks wrong. Reliability, not insight, becomes the daily preoccupation.
Meanwhile the business waits. New questions take weeks because every one becomes a small project, and the analytics team is too busy keeping the lights on to help with the decisions BI was supposed to support in the first place.
The organisation ends up in a frustrating place: it has invested heavily in BI tools and people, yet leaders still do not fully trust the numbers, analysts still spend their days reconciling extracts, and the questions that actually matter to the business still take weeks to answer.
Outcome-first, end-to-end, managed
DurgaPulse starts from business questions, not technology. We design dashboards and data products around the decisions they support - revenue, margin, risk, utilisation, churn - so what gets built maps to what leaders actually need to decide.
We own the whole chain end to end: ingestion, modelling, visualisation, access control and monitoring, integrated with your tools and your cloud. Your teams consume governed BI on tap instead of assembling it themselves.
Because the semantic layer is governed and version-controlled, a change to a metric definition is deliberate, reviewed and traceable, not a silent edit in one report that quietly diverges from the rest. That discipline is what lets a large organisation trust the same KPI across dozens of dashboards.
Everything sits on a governed semantic layer, so a metric is defined once, certified, and reused across every dashboard. Gold, silver and bronze data-product tiers make the level of trust explicit, and row-level security and masking keep the right data in the right hands.
It is run as a long-lived managed service with SLAs, a change backlog and quarterly roadmap reviews, so BI keeps improving rather than decaying. You get a predictable service and a partner, not another tool and another team to staff.
The net effect is that BI stops being a source of friction and becomes something the business can lean on. Governed, reliable data products are available on tap, the internal team is freed to do higher-value work, and the analytics capability improves quarter after quarter instead of slowly decaying.
Why BI programmes stall
Almost every enterprise has invested in BI, and almost every one is frustrated with the result. The tools are capable, but the programmes stall in fragmented reports, ad-hoc Excel models and a central team so busy keeping existing dashboards alive that it cannot build anything new.
The root cause is rarely the visualisation tool. It is the absence of a governed foundation and a shared semantic layer: without them, every metric is defined differently in different places, pipelines break silently, and trust in the numbers erodes until leadership quietly goes back to its own spreadsheets.
DurgaPulse treats BI as a service to be run rather than a project to be delivered and abandoned. By owning the data foundation, the semantic layer, the dashboards and the operations together, and running them to an SLA, it turns analytics from a recurring disappointment into a dependable capability that scales with the business.
The principles behind it
Outcome-first
Start from the decision and the KPI, then build the data product, so what gets built maps to what leaders actually need.
Governed foundation
One certified semantic layer defines each metric once, so trust replaces the argument about whose number is right.
Reliability built in
Monitored, incremental pipelines and clear data-product tiers mean dashboards do not break silently.
Managed end-to-end
Ingestion, modelling, visualisation and operations are run as one service to an SLA, not handed over as a project.
Scales cleanly
Start with one domain and extend, with multi-region and compliance needs part of the design from the outset.
Tool-flexible
Works with your existing cloud and BI tools, so people keep what they know while the foundation improves.
DurgaPulse at each level
The service is structured as four pillars that cover the full analytics lifecycle, from a governed data foundation through semantic models, insight delivery and ongoing operations.
DurgaPulse is organised into four pillars that span the full analytics lifecycle: a reliable data foundation, a governed semantic layer, the dashboards and alerts people consume, and the operations that keep it all improving. Each is delivered as part of the managed service rather than handed over as a project artifact.
The chain runs from onboarding and stabilising your data, through defining and certifying the metrics that make it trustworthy, into the cockpits and alerts that reach decision-makers, and finally into the SLA-backed operation that keeps the whole estate healthy and evolving.
Data and platform foundation
The foundation is where reliability is won or lost. DurgaPulse onboards your source systems - ERP, CRM, core banking, trading, custom apps, flat files and APIs - into cloud-native or on-prem pipelines with incremental loads and built-in data-quality checks, so refreshes are predictable and breakages are caught early. The data lands in a central, governed lake, warehouse or lakehouse layer that becomes the single place downstream models and dashboards draw from. Where the tooling supports it, metadata, lineage and a business glossary are wired in from the start, so it is always clear where a number came from and what it means. Get this layer right and everything above it becomes cheaper to build and easier to trust.
The foundation onboards your source systems - ERP, CRM, core banking, trading, custom apps, flat files and APIs - into pipelines that load incrementally and check quality as they go, so refreshes are predictable and a broken feed is caught before it reaches a dashboard. This is the unglamorous layer where BI reliability is actually won.
The data lands in a central, governed lake, warehouse or lakehouse that becomes the single place everything above it draws from, with metadata, lineage and a business glossary wired in where the tooling allows. Getting this layer right is what makes every dashboard above it cheaper to build and easier to trust, because there is one governed source rather than a dozen extracts.
- Source onboarding from ERP, CRM, core banking, trading, custom apps, files and APIs
- Cloud-native or on-prem pipelines with incremental loads and data-quality checks
- Central governed lake, warehouse or lakehouse layer as the single source
- Metadata, lineage and business-glossary integration where available
- Monitoring and alerting on pipeline health and freshness
- Reproducible, documented pipelines rather than brittle hand-built jobs
- Backfill and reprocessing for historical corrections
- Environment separation for development, test and production
Reliability is won or lost here. A governed, well-monitored foundation is what stops dashboards from breaking silently and lets everything above it be cheaper to build and easier to trust.
Semantic models and governance
Above the raw data sits the layer that makes BI trustworthy. DurgaPulse builds subject-area models for Sales, Finance, Risk, Operations, HR, Marketing and Supply Chain, with reusable metrics and calculations - gross-margin percentage, net interest margin, AR days, churn, NPS, SLA adherence and the rest - defined once and certified rather than reinvented per report. Role-based access, row-level security and masking are applied where required, so people see exactly the data they should. Data-quality scorecards and a gold, silver and bronze certification scheme make the reliability of each data product explicit, so consumers know which numbers are board-ready and which are still experimental.
The semantic layer is what turns raw data into trustworthy analytics. Subject-area models for Sales, Finance, Risk, Operations, HR, Marketing and Supply Chain carry reusable, certified metrics - gross margin, net interest margin, AR days, churn, NPS, SLA adherence - defined once so the same KPI means the same thing wherever it appears.
Governance is built into this layer rather than added later. Role-based access, row-level security and masking keep the right data with the right people, and a gold, silver and bronze certification scheme makes the reliability of each data product explicit, so consumers know at a glance which numbers are board-ready and which are still experimental.
- Subject-area models: Sales, Finance, Risk, Operations, HR, Marketing, Supply Chain
- Reusable, certified metrics (GM%, NIM, AR days, churn, NPS, SLA adherence and more)
- Role-based access, row-level security and masking where required
- Data-quality scorecards by domain and system
- Gold, silver and bronze data-product certification
- A single, governed definition of each metric reused across dashboards
- Version-controlled metric definitions with change history
- Certification workflow before a data product goes gold
This layer is what ends the argument about whose number is right. Defining each metric once and certifying it is the difference between trusted BI and a pile of conflicting reports.
Dashboards, stories and alerts
This is what most of the organisation actually sees. Executive cockpits give CXOs and business-unit leaders a clear, web and mobile-ready view of the metrics that matter, while operational dashboards support the daily and weekly monitoring and exception management that keep the business running. Governed self-service templates and certified datasets let power users explore safely without spawning yet another ungoverned report. And because insight is only useful if it reaches people in time, threshold-based and anomaly-based alerts push the important changes to email, Teams or Slack and mobile, so decisions are prompted by the data rather than waiting for someone to open a dashboard.
This is the layer most of the organisation actually experiences. Executive cockpits give leaders a clear, mobile-ready view of the metrics that matter, operational dashboards support daily and weekly monitoring, and governed self-service lets power users explore certified datasets without spawning yet another ungoverned report.
Because insight only helps if it arrives in time, alerting is a first-class part of this layer. Threshold-based and anomaly-based alerts push the important changes to email, Teams or Slack and mobile, so a drifting collections metric or a margin dip prompts action the same day rather than waiting to be noticed in next month's pack.
- Executive cockpits for CXO and BU leaders, web and mobile ready
- Operational dashboards for daily and weekly monitoring and exceptions
- Governed self-service templates and certified datasets for power users
- Threshold-based and anomaly-based alerts via email, Teams/Slack and mobile
- Narrative and storytelling views that explain the why, not just the what
- Consistent, branded design across the dashboard estate
- Scheduled distribution of packs to inboxes and channels
- Accessibility and mobile-responsive layouts as standard
This is what most of the organisation experiences. Clear cockpits and timely alerts are how a governed foundation actually changes decisions rather than just existing.
Operations and continuous improvement
A dashboard suite that is not operated decays within months. DurgaPulse wraps the whole platform in SLA-backed production support with proper incident and problem management, and manages a change and enhancement backlog against a visible release calendar, so the service evolves in a controlled way. Usage analytics and adoption reviews show which dashboards and data products are actually used, so effort goes where it earns its keep and dead reports are retired. Quarterly roadmap reviews cover cost optimisation and new use-case discovery, and optional advanced analytics - machine learning, forecasting, anomaly detection - can be layered on top once the governed foundation is delivering reliably.
A dashboard suite that nobody operates decays within months, so DurgaPulse wraps the platform in SLA-backed support with real incident and problem management, and runs a change and enhancement backlog against a visible release calendar. The service evolves in a controlled way rather than through ad-hoc firefighting.
Continuous improvement is driven by evidence. Usage analytics and adoption reviews show which dashboards and data products earn their keep, dead reports are retired, and quarterly roadmap reviews cover cost optimisation and new use cases. Once the governed foundation is reliable, optional ML, forecasting and anomaly detection can be layered on with confidence.
- SLA-backed production support with incident and problem management
- Change and enhancement backlog with a visible release calendar
- Usage analytics and adoption reviews for dashboards and data products
- Quarterly roadmap reviews, cost optimisation and new use-case discovery
- Retirement of unused reports to keep the estate lean
- Optional ML, forecasting and anomaly detection on the governed foundation
- Service reporting on uptime, freshness and response times
- A prioritised enhancement backlog reviewed with stakeholders
Unoperated BI decays within months. Running the platform to an SLA with a managed backlog is what keeps it improving instead of quietly rotting into broken dashboards.
Key differentiators
Any team can build a dashboard. What makes DurgaPulse different is that it delivers governed, reliable, outcome-first analytics as a managed service that scales with the enterprise, so BI stops being a firefight and becomes something you can depend on.
Outcome-first
We start from business questions and KPIs - revenue, margin, risk, utilisation, churn - and design data products around decisions, not around whatever the source systems happen to expose. Because the work starts from the decision rather than the dataset, dashboards answer real questions instead of simply displaying whatever the source system happens to expose.
End-to-end managed
We own ingestion, modelling, visualisation, access control and monitoring, integrated with your tools and cloud, so your teams consume governed BI on tap instead of maintaining it. Owning the whole chain also means one accountable partner when something needs to change, rather than finger-pointing between a tool vendor, a pipeline team and a report author.
Governed by design
A single certified semantic layer means each metric is defined once and reused everywhere, with row-level security, masking and gold/silver/bronze data-product tiers built in. Certification tiers make trust explicit, so a board-ready gold metric is visibly different from an experimental bronze one, and nobody is misled by a polished-looking chart.
Scales with the enterprise
Start with one domain and expand. Multi-country, multi-BU, data-residency and compliance requirements are part of the architecture and operating model, not an afterthought. Multi-region and compliance needs are designed in from the start, so growth does not force a re-architecture once a second country or business unit comes on board.
SLA-backed service
Production support, a managed change backlog and a release calendar mean the platform keeps improving rather than decaying into a pile of broken dashboards. The SLA turns support from best-effort into a commitment, with defined uptime, refresh schedules and response times you can actually plan around.
Tool-flexible
Works with Power BI, Tableau, Looker, custom React dashboards or embedded analytics on your existing cloud, so you are not forced onto a single vendor stack. Keeping your existing tools lowers change-management friction, so adoption is about better data underneath rather than retraining everyone on a new interface.
What changes for the business
Indicative shifts our clients target when they adopt DurgaPulse. Actual results depend on scope, data quality and starting maturity.
The clearest shift is trust: when every dashboard draws on one certified definition of a metric, leadership stops arguing about whose number is right and starts acting on it. Decisions speed up because the data is no longer in question.
The second is capacity. When Durga Analytics runs ingestion, modelling and operations to an SLA, your internal team stops firefighting and starts building - new use cases, forecasting, the analysis the business has been waiting months for - which is where BI was always meant to add value.
The value of managed BI is easiest to see in a few practical measures: how much of the dashboard estate draws on certified, single-definition metrics rather than ad-hoc extracts; how reliably data refreshes on schedule; how quickly incidents are resolved against the SLA; and how much of the central team's time has shifted from firefighting to building new use cases.
Alongside those, adoption is the ultimate test. Usage analytics show which dashboards and data products are actually relied upon, dead reports are retired, and quarterly reviews turn that evidence into a roadmap, so the service is steered by how it is really used rather than by assumption.
Agreeing a handful of these measures at the start, and baselining them, is what turns the outcomes above from aspiration into something you can track quarter by quarter on your own data and your own decisions.
In short, the measures that matter are trust, reliability, speed and adoption, and each of them is observable on your own estate. Agreeing them early gives both sides a shared, honest scorecard for whether the service is delivering, which is exactly the accountability a managed model should bring.
Example enterprise use cases
- Executive MIS and board packs from a single governed source
- Branch and region performance dashboards
- Customer profitability and segment analytics
- Risk and exposure monitoring: limits, breaches and trends
- Operations efficiency and turnaround-time analytics
- Collections performance and early-warning indicators
- Digital-channel funnel and conversion analytics
- Campaign performance and marketing attribution
- Regulatory and internal reporting automation
Executive MIS and board packs
Leadership gets board-ready MIS from a single governed source, with the same certified metrics behind every slide, so packs are produced faster and trusted more.
Branch and region performance
Operational dashboards give consistent branch and region performance views drawn from the same definitions, so comparisons are fair and drill-downs reconcile to the top line.
Risk and early warning
Risk and collections teams monitor limits, breaches, trends and early-warning indicators with anomaly alerts, so issues surface the same day rather than in next month's pack.
Customer and channel analytics
Marketing and product teams analyse customer profitability, segments, digital funnels and campaign attribution on governed data, so decisions rest on trusted numbers.
Who DurgaPulse is for
- Enterprises whose BI has sprawled into hundreds of ungoverned reports and want to consolidate onto a trusted, managed layer, with a partner that runs the platform to an SLA so the burden does not fall back on an already-stretched team.
- Leadership teams tired of meetings that start with an argument about whose number is right, who want one certified version of each KPI.
- Organisations with a stretched central data team that is firefighting requests and cannot get to strategic work.
- Multi-country or multi-business-unit groups that need consistent metrics with data-residency and compliance built in.
- Functions - finance, sales, risk, operations, marketing - that want reliable dashboards and alerts without owning the plumbing.
- Companies that want a path to ML and forecasting but know they need a governed, reliable data foundation first.
What each team gets
Executives and BU leaders
Clear, mobile-ready cockpits showing the KPIs that matter - revenue, margin, risk, utilisation, churn - drawn from one certified definition, so leadership acts on the numbers instead of debating them.
Analysts and power users
Governed self-service templates and certified datasets to explore safely, without spawning ungoverned reports, on top of a semantic layer that already defines the metrics correctly.
Finance, risk and operations
Reliable operational dashboards and alerts for daily and weekly monitoring, with thresholds and anomaly detection that push exceptions to the right person the same day.
Central data team
Relief from firefighting: Durga Analytics owns ingestion, modelling and operations to an SLA, freeing the internal team to build new use cases and advanced analytics.
IT and security
Role-based access, row-level security and masking, integration with your existing cloud and ITSM tooling, and a governed foundation that reduces shadow BI and ungoverned extracts.
Data and analytics leadership
A predictable, SLA-backed service with usage analytics and quarterly roadmap reviews, so BI investment is visible, governed and continuously improving rather than sprawling.
A quarter with DurgaPulse
A regional bank runs its month-end MIS on a patchwork of extracts and spreadsheets. Every cycle, three teams produce slightly different revenue and cost-of-risk numbers, and the CFO's pack is only finalised after days of reconciliation and a few tense calls.
With DurgaPulse, the source systems feed a governed foundation on incremental pipelines, and finance, risk and operations metrics are defined once in a certified semantic layer. The executive cockpit shows the same revenue, margin and cost-of-risk figures to everyone, tagged as board-ready gold data products, and the branch and region dashboards drill from exactly the same definitions.
When a collections indicator drifts past its threshold mid-month, an anomaly alert reaches the right manager on Teams the same day, not in next month's pack. The central data team, freed from assembling spreadsheets, spends the quarter standing up a churn-forecasting model on the same foundation, and the roadmap review turns to what to build next rather than what broke last week.
What changed was not the reporting tool but the foundation beneath it. One governed source, certified metrics and an operated service meant the numbers could be trusted, the alerts arrived in time, and the internal team was finally free to build forward rather than firefight, which is the whole promise of BI delivered as a dependable service.
How DurgaPulse fits your estate
DurgaPulse is built to work with the stack you already have rather than replace it. It runs on your existing cloud - Azure, AWS, GCP or hybrid - and supports Power BI, Tableau, Looker, custom React dashboards and embedded analytics, so people keep the tools they know while the governance and reliability underneath improve.
On the data side it has experience with Snowflake, Databricks, BigQuery and traditional warehouses, and it can integrate with your ticketing and ITSM tools so end-user requests flow through proper workflows. Delivery is deliberately incremental: a two-to-six-week pilot proves value in one domain such as Sales or Finance, then coverage scales across domains and business units with the operating model, SLAs and shared KPIs agreed as you go.
What is inside
- Source onboarding from ERP, CRM, core banking, trading and custom systems
- Governed data lake, warehouse or lakehouse foundation
- Incremental pipelines with data-quality checks and monitoring
- Certified semantic models across finance, sales, risk and operations
- Reusable, single-definition metrics and calculations
- Row-level security, masking and role-based access
- Executive and operational dashboards, web and mobile ready
- Governed self-service for power users
- Threshold and anomaly alerts to email, Teams, Slack and mobile
- SLA-backed support with incident, problem and change management
- Usage and adoption analytics with quarterly roadmap reviews
- Optional ML, forecasting and anomaly detection add-ons
- Data-product catalogue with clear ownership and certification
- Branded, consistent dashboard design across the estate
Foundation
Source onboarding and governed lake, warehouse or lakehouse with incremental, monitored pipelines.
Semantic layer
Certified subject-area models and reusable metrics defined once and reused everywhere.
Governance
Row-level security, masking, role-based access and gold, silver and bronze data products.
Dashboards
Executive and operational cockpits, web and mobile ready, plus governed self-service.
Alerting
Threshold-based and anomaly-based alerts to email, Teams, Slack and mobile.
Operations
SLA-backed support, a managed change backlog and quarterly adoption and roadmap reviews.
Cost optimisation
Ongoing tuning of pipelines, storage and refresh schedules so the platform stays cost-efficient as usage grows, reviewed each quarter.
Knowledge transfer
Documentation, glossary mappings and champion training so your teams understand and can extend the analytics they consume.
How you run it
What it connects to
How it fits your technology estate
DurgaPulse works with the technology you already have rather than replacing it. It runs on your existing cloud - Azure, AWS, GCP or hybrid - and supports Power BI, Tableau, Looker, custom React dashboards and embedded analytics, so users keep familiar tools while the governance and reliability underneath improve.
On the data side it has experience across Snowflake, Databricks, BigQuery and traditional warehouses, and it onboards sources from ERP, CRM, core banking, trading and custom systems through APIs and files. Integration with ticketing and ITSM tooling routes end-user requests through proper workflows, so the service is governed and auditable rather than ad-hoc.
The result is an analytics capability that fits your estate and your ways of working, delivered incrementally so a single domain proves value before scope widens across sources, domains and business units.
Where you already run a data catalog, governance or MDM capability, DurgaPulse aligns to it rather than duplicating it, so the analytics layer inherits your definitions and lineage. And where those foundations are missing, the same team can help stand up the minimum needed, so BI is never blocked waiting on a larger programme while still moving in the right architectural direction.
Enterprise controls
Where it is going
Governed pipelines, semantic models, dashboards, alerts, SLAs.
Expanded connectors, metric governance and adoption analytics.
AI-assisted insight, forecasting and anomaly detection within governance.
How we engage
DurgaPulse is delivered as a long-running managed service, starting small and scaling with a clear operating model, shared KPIs and governance. The point of the phased approach is to prove production-grade value in one domain quickly, then extend it without losing quality.
A discovery and pilot of a few weeks stands up a minimal viable stack and delivers real dashboards for one domain such as Sales or Finance. From there, scope scales across sources and domains with semantic models, a standard report catalogue and access controls, and the service settles into continuous improvement with quarterly roadmap reviews and optional advanced analytics.
Phase 1 - Discovery and pilot (2 to 6 weeks)
Assess data sources, define KPIs, stand up a minimal viable analytics stack and deliver first dashboards for one domain with production-grade quality.
Phase 2 - Scale and standardize
Add more sources and domains, roll out semantic models, a standard report catalog and access controls, and train business champions for adoption.
Phase 3 - Continuous improvement
Quarterly roadmap reviews, new use-case discovery, performance tuning and cost optimization, with optional ML, forecasting and anomaly detection.
Tangible deliverables
- Managed data pipelines from priority systems into a governed analytics store
- Documented semantic models and metric definitions with glossary mappings
- A curated dashboard suite and report catalogue aligned to business roles
- Alert configurations and runbooks for key operational and risk indicators
- An SLA covering uptime, refresh schedules, response times and enhancements
- Quarterly usage and adoption reports with recommended actions
- A certified data-product catalogue with gold, silver and bronze tiers
- Onboarding, knowledge transfer and governance-alignment materials
Managed pipelines
Data pipelines from your priority systems into a governed analytics store, run and monitored as part of the service rather than handed over as scripts.
Models and glossary
Documented semantic models and metric definitions with business-glossary mappings, so every KPI has one clear, shared meaning.
Dashboards and alerts
A curated dashboard suite and report catalogue aligned to business roles, with alert configurations and runbooks for key operational and risk indicators.
Service and adoption
An SLA covering uptime, refresh schedules and response times, plus quarterly usage and adoption reports with recommended actions.
Engagement-based and transparent
DurgaPulse runs on a fixed, predictable monthly subscription, with plans aligned to the number of data sources, named users, domains and the SLA you require, from a single department up to large, regulated, multi-country enterprises.
Every plan includes onboarding, knowledge transfer and governance alignment, and larger engagements add dedicated service management, workshops and custom compliance handling. Volume discounts are available for multi-year commitments, and the right plan is scoped during a short discovery rather than sold as a rigid package.
Who is behind DurgaPulse
DurgaPulse is run by a cross-functional squad of data engineers, BI developers, product owners and service managers with deep experience across BFSI, energy, retail and digital businesses. They have built and operated analytics at scale, which is why the service is designed around reliability and adoption, not just first delivery.
The squad works with your existing cloud and tools rather than imposing a stack, and stays with the service long-term, so the people who understand your data and definitions are the ones improving them each quarter.
- Data engineering across ERP, CRM, core banking, trading and custom systems
- BI development on Power BI, Tableau, Looker and custom React dashboards
- Data platforms such as Snowflake, Databricks, BigQuery and warehouses
- Service management with incident, change and adoption workflows
Frequently asked questions
What exactly is BI as a service?
It means Durga Analytics runs your analytics end to end - data pipelines, semantic models, dashboards, alerts, access control and support - as a managed service with SLAs, so your business teams consume governed BI without owning the underlying infrastructure or operations.
Do we have to change our BI tool?
No. DurgaPulse works with Power BI, Tableau, Looker, custom React dashboards or embedded analytics on your existing cloud, so people keep the tools they know while governance and reliability underneath improve.
How do you keep metrics consistent?
Through a governed semantic layer where each metric is defined once, certified, and reused across every dashboard, with gold, silver and bronze data-product tiers making the level of trust explicit. That is what ends the argument about whose number is right.
How does it handle security and multiple regions?
Role-based access, row-level security and masking are applied where required, and multi-country, multi-business-unit and data-residency requirements are part of the architecture and operating model rather than bolt-ons.
How quickly do we see something useful?
A focused pilot typically runs two to six weeks, standing up a minimal viable stack and delivering production-grade dashboards for one domain such as Sales or Finance, before scope scales to more sources and domains.
Can we add machine learning later?
Yes. Once the governed foundation is delivering reliably, optional advanced analytics such as forecasting, churn models and anomaly detection can be layered on top of the same certified data products.
Who does the ongoing support?
Durga Analytics provides SLA-backed production support with incident and problem management, a managed change and enhancement backlog, and quarterly roadmap and adoption reviews, so the platform keeps improving rather than decaying.
How is it priced?
On a fixed, predictable subscription reflecting the number of data sources, users, domains and SLAs, agreed during discovery. Onboarding, knowledge transfer and governance alignment are included, and multi-year commitments can include volume terms.
Will DurgaPulse replace our internal BI team?
No. It augments them. Durga Analytics takes on the run-and-maintain burden to an SLA, which frees your internal team to focus on higher-value work such as new use cases, deeper analysis and advanced analytics, rather than firefighting broken dashboards.
How do you handle data residency and compliance?
Multi-country, multi-business-unit and data-residency requirements are part of the architecture and operating model. Data can stay in the required regions, and access controls, masking and compliance handling are configured to your obligations.
What if our data quality is poor to begin with?
The data-and-platform foundation includes data-quality checks, and the semantic layer certifies data products as gold, silver or bronze so reliability is explicit. Poor-quality sources are made visible and improved over time rather than hidden behind a polished dashboard.
How do you prevent dashboard sprawl?
By centring everything on a governed semantic layer and a certified data-product catalogue, and by retiring unused reports based on usage analytics. Governed self-service lets power users explore certified datasets without spawning ungoverned copies, so the estate stays lean.
What industries do you support?
The delivery squad has deep experience across BFSI, energy, retail and digital businesses, and the outcome-first approach applies to finance, sales, operations, risk and customer-experience analytics in any sector, because it starts from your decisions and KPIs rather than a fixed vertical template.
Can DurgaPulse work with our existing warehouse?
Yes. It works with Snowflake, Databricks, BigQuery and traditional warehouses, and can build on the data platform you already run rather than requiring a new one, so it strengthens your existing estate rather than replacing it.
How to begin
Getting started is deliberately low-risk: a short discovery of your current BI landscape and priorities, followed by a pilot of a few weeks that stands up a minimal viable stack and delivers production-grade dashboards for one domain such as Sales or Finance.
That pilot proves the model on real data and real decisions before scope widens. From there, sources and domains are added, semantic models and a standard report catalogue are rolled out, and business champions are trained for adoption, so growth is controlled and quality is preserved.
The service then settles into continuous improvement, with SLA-backed support, a managed enhancement backlog and quarterly roadmap reviews, and optional advanced analytics layered on once the governed foundation is delivering reliably.
Whichever domain you begin with, the aim of the first phase is a genuinely production-grade result, not a throwaway prototype, so the pilot becomes the first increment of the live service rather than a demo to be rebuilt later.
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