Analytics, managed
DAaaS — we build, run and improve it.
Data engineering, managed analytics and ML/BI Ops — pipelines, platform support, DataOps monitoring and governance-as-a-service — that keep your analytics dependable, day after day.
Models and dashboards are easy to launch and hard to keep alive. We engineer the pipelines and run the operations — monitoring, incident response, enhancements and governance — so analytics stays trusted long after go-live. Delivered as Data Analytics as a Service when you want us to run it end to end.
The capabilities that keep analytics dependable in production.
For teams scaling analytics, covering a skills gap, or carrying recurring reporting they can’t keep up with: KEPLER embeds analysts…
Ongoing operation of your BI and ML estate, with SLAs and accountability — so analytics keeps running while your team focuses on decisions.
Tiered L2/L3 support that keeps your platforms, pipelines and reports healthy — with the depth to fix root causes, not just tickets.
Continuous monitoring that catches data issues before your users do — with fast, defined incident response when they happen.
A steady, prioritised stream of improvements to your analytics estate — so it keeps getting better, not just kept alive.
Ongoing data governance, quality and access control run on your behalf — so standards hold without standing up a whole function.
The practices we keep running in production.
Diagnostic, predictive and prescriptive models that turn data into decisions.
Forecasting, driver-based planning and scenario analysis.
Procurement, supply chain and manufacturing analytics.
Funnel, marketing-mix and profitability analytics.
Representative outcomes we engineer in this area.
DAaaS — we build, run and improve it.
Monitoring before users notice.
Automated, unattended, dependable.
Bring a real decision or dataset — we’ll show you how KEPLER would approach it, with no obligation.
Talk to our AnalyticsOps teamThe data story that survives go-live.
Pipelines and platforms are built to run reliably at scale.
Manual steps are automated out of the data flow.
BI and ML estates are run with SLAs and ownership.
Continuous monitoring catches drift and incidents early.
An enhancements stream keeps the estate moving forward.
The run handled, so the value keeps flowing.
The difference once we run it.
Pipelines and dashboards that just keep working.
Issues caught and fixed before they spread.
Your team works on decisions, not upkeep.
The technical engine that makes this work dependable in production.
The industries this work serves.
Representative problems teams bring to KEPLER here. Click any use case for the detail.
Managed run & support
View details →Projects deliver a platform and disband. With no one owning the run, pipelines break, dashboards go stale, and trust erodes within months.
Analytics that keeps working long after go-live, run to an agreed standard instead of drifting.
Flexible expert capacity
View details →Running a modern analytics stack needs scarce, expensive skills, and hiring a full in-house team for a variable workload rarely makes sense.
The expertise to run analytics reliably, without carrying a full permanent team for a variable load.
SLA-backed reliability
View details →Failures fall between teams with no clear owner or response time, so a broken pipeline can sit unfixed for days while blame circulates.
Clear accountability and defined response, so failures get fixed fast instead of debated.
Responsive L2/L3 support
View details →When a dashboard breaks or a number looks wrong, users have no real support line, so issues linger, workarounds spread, and confidence drops.
A dependable support line that keeps reporting trustworthy and users unblocked.
Fewer repeat incidents
View details →The same incidents recur because support only ever firefights symptoms and never gets to root cause, so effort repeats endlessly.
Fewer repeat incidents as root causes get fixed, freeing support from the same fires.
Protected engineering time
View details →With no support layer, expensive senior engineers spend their days on routine tickets instead of the work only they can do.
Senior engineering time protected for high-value work, with routine support handled cost-effectively.
Proactive failure alerts
View details →Pipeline failures are found when a user notices yesterday's data, by which point decisions have already been made on stale or missing numbers.
Failures caught and fixed before users ever see stale data.
Faster incident recovery
View details →Without observability, diagnosing a data incident is archaeology across systems, so mean time to recover stretches into a lost day.
Data incidents diagnosed and resolved fast, shrinking the recovery window from a day to an hour.
Caught before publish
View details →Bad data that doesn't crash a job, a wrong join, a duplicated load, flows straight into reports and quietly corrupts the numbers people rely on.
Silent data errors caught before publication, protecting the trust the reports depend on.
Steady enhancement flow
View details →A backlog of small, valuable tweaks, new fields, tweaked logic, extra views, sits behind big projects for months, so the platform stops keeping pace with the business.
A steady stream of improvements shipped, so the platform keeps evolving instead of freezing between projects.
Safe changes
View details →Without test and release discipline, each quick fix risks breaking a report elsewhere, so teams either avoid changes or ship anxiously.
Changes shipped quickly and safely, without the fear that a small fix breaks the estate.
Lower cost per change
View details →When every small request runs as a mini-project with full overhead, the cost per change is absurd and the business stops asking.
A low cost-per-change that makes continuous improvement affordable again.
Sustained governance
View details →Governance is stood up as a project, then quietly lapses once the project ends, so definitions drift and quality slips back to where it started.
Governance that persists as a living service instead of decaying after launch.
On-tap governance expertise
View details →Effective governance needs stewardship and tooling skills that are hard to hire and even harder to justify as full-time roles.
Access to governance expertise without carrying scarce specialists full-time.
Always compliant
View details →Regulations and data keep changing, so a compliance state achieved once erodes, and the next audit finds the gaps that opened since.
Compliance maintained as a continuous state, so audits find readiness rather than gaps.
No use cases match this filter yet — but the problem is almost certainly one we can help with.
Ask us about your problem →Let us operate your analytics so your team can focus on decisions, not upkeep.
Talk to our AnalyticsOps team
