Internal analysts who grow
Your data staff gain extra capacity, specialist support and training, so they build skills instead of being replaced.
How We Work
Analytics as a service: a fractional data team embedded in your company
Analytics as a service from Hurtado Analytics gives you a fractional data team that owns your reporting, data pipelines and decision support for a predictable monthly scope.
A growing company often needs more data work than one analyst can cover, but not enough to justify a full department. Our analytics as a service model closes that gap. A fractional data team works inside your tools, joins your management rhythm and delivers against an agreed monthly plan.
The team covers dashboards, data integration, data governance and the analysis behind leadership decisions. You keep ownership of everything we build, and scope flexes as priorities change.
The service usually follows advisory work or a readiness assessment. We propose a monthly plan only once priorities are clear and your organization is ready to work with an external team.
What it is
Analytics as a service is a managed team, not a software subscription
Analytics as a service is a delivery model in which an external team runs part or all of a company's analytics function on an ongoing basis. Many sources use the term, often shortened to AaaS, for cloud analytics software with its own deployment models. We use it for something different: a managed team of people who design, build and maintain your reporting and data foundations.
In practice, analytics as a service combines capabilities that are usually bought separately. BI as a service and data visualization as a service cover dashboards. Data integration as a service and data warehouse as a service cover pipelines. Data governance as a service covers definitions, quality and access.
The difference from a one-off project is continuity. The same people learn your business, keep metrics consistent from month to month and respond when leadership asks a new question. The difference from a hire is breadth. You draw on analyst, engineer and strategist skills without recruiting for each role.
The result is outsourced data analytics with the accountability of an internal team and the flexibility of a service.
How we work
A monthly cycle keeps managed analytics services tied to your priorities
The cycle starts only after advisory work has confirmed priorities and a fit and readiness check shows the service will be adopted. From then on, our managed analytics services run on a monthly cycle that keeps work visible and tied to business priorities. Leadership sets the agenda, the team delivers inside your own tools, and every cycle ends with a short review of what changed. Nothing is built without a decision it supports, and nothing is left unmaintained after release.
One monthly cycle of managed analytics services
Select a stage, use Next or the arrow keysPrioritize. Work follows business value. Each month starts with leadership ranking reports, fixes and analysis requests by the decisions they support.
Step 1 of 4
- Prioritize. Work follows business value. Each month starts with leadership ranking reports, fixes and analysis requests by the decisions they support.
- Deliver. Releases arrive in short iterations. The team ships in short iterations inside your own Power BI, HubSpot, NetSuite or warehouse environment.
- Review. You see what changed and why. A monthly review covers what shipped, what moved in the numbers and which questions remain open.
- Maintain. Dashboards stay reliable between releases. Pipelines, refreshes and metric definitions are monitored, and we aim to fix issues before they reach users.
Types
Choose the level of support: fractional data analyst, data team or CDO
Analytics as a service scales to the level of support you need. A company with one reporting gap needs different help from one rebuilding its data foundations. A common starting point is one model, moving up or down as needs change. Think of the three as deployment models for one service. The models share the same methods, so switching does not mean starting again.
Reports maintained and questions answered
A fractional data analyst keeps dashboards current and prepares recurring management reporting, part time.
Part-time support for a company with one clear reporting gap.
- Keeps dashboards current
- Prepares recurring management reporting
- Answers ad hoc questions from leadership
Reporting, pipelines and governance owned end to end
A fractional data team blends analyst, data engineer and BI developer capacity in one service.
- Builds and maintains integrations and warehouse tables
- Owns dashboards and the data dictionary
- Logs and closes data quality issues at the source
A data roadmap leadership can act on
A fractional CDO or head of data sets priorities, guides internal staff and links data work to strategy.
- Sets data priorities with leadership
- Guides internal data staff
- Supports quarterly planning and board preparation
Comparison
For steady, varied demand, analytics as a service beats hiring or one-off projects
Each route can work. The right choice depends on how much ongoing demand you have, how fast you need results and how much risk you can carry in a single hire. It also depends on how specialized the work is. Data engineering, BI development and governance rarely sit in one person.
| Service | In-house | Project | |
|---|---|---|---|
| How the engagement starts | advisory on your decisions, data and team, then a scoped monthly plan | a role definition and a recruiting process | a defined scope and a fixed-fee proposal |
| Fit check before implementation | we confirm that we can genuinely help and that your team is ready | depends on the hiring manager | varies by provider, often limited to scoping |
| When implementation is offered | once priorities are agreed and the organization is ready to adopt the work | once the hire is onboarded | from signature, within the agreed scope |
| Cost structure | predictable monthly fee scaled to scope | salary, benefits, recruiting and tooling | fixed fee for one defined deliverable |
| Speed to start | short, with an existing team and methods | months to recruit and onboard | short, limited to agreed scope |
| Skill coverage | analyst, engineering, BI and strategy in one plan | one or two skills per hire | only that project's skills |
| Continuity until adoption | a named team accountable through adoption, then ongoing improvement | strong while the person stays | ends at handover, knowledge leaves |
| Key-person risk | spread across a documented team | high when knowledge sits with one person | rises sharply after handover |
| Best fit | steady, varied demand without a full department | large, stable demand | one build, such as data modernization |
- How the engagement starts
- advisory on your decisions, data and team, then a scoped monthly plan
- Fit check before implementation
- we confirm that we can genuinely help and that your team is ready
- When implementation is offered
- once priorities are agreed and the organization is ready to adopt the work
- Cost structure
- predictable monthly fee scaled to scope
- Speed to start
- short, with an existing team and methods
- Skill coverage
- analyst, engineering, BI and strategy in one plan
- Continuity until adoption
- a named team accountable through adoption, then ongoing improvement
- Key-person risk
- spread across a documented team
- Best fit
- steady, varied demand without a full department
- How the engagement starts
- a role definition and a recruiting process
- Fit check before implementation
- depends on the hiring manager
- When implementation is offered
- once the hire is onboarded
- Cost structure
- salary, benefits, recruiting and tooling
- Speed to start
- months to recruit and onboard
- Skill coverage
- one or two skills per hire
- Continuity until adoption
- strong while the person stays
- Key-person risk
- high when knowledge sits with one person
- Best fit
- large, stable demand
- How the engagement starts
- a defined scope and a fixed-fee proposal
- Fit check before implementation
- varies by provider, often limited to scoping
- When implementation is offered
- from signature, within the agreed scope
- Cost structure
- fixed fee for one defined deliverable
- Speed to start
- short, limited to agreed scope
- Skill coverage
- only that project's skills
- Continuity until adoption
- ends at handover, knowledge leaves
- Key-person risk
- rises sharply after handover
- Best fit
- one build, such as data modernization
Analytics as a service suits steady, varied demand: recurring reporting, regular new questions and foundations that need upkeep. Hiring in-house suits sustained demand large enough to keep a full team busy, plus time to recruit it. A fixed-scope outsourced data analytics project suits one defined build, such as a migration or a first set of dashboards.
The routes can be combined. One common sequence is advisory first, then a modernization project to build the foundations, then analytics as a service to run and extend them, with internal hires added once demand is clear. The table compares the three routes on cost, speed, coverage and risk, the factors leadership teams usually weigh. In short: buy a service for steady, varied work, hire for large and stable work, and run a project for one defined build. If demand is uncertain, start with advisory or a readiness assessment. Once priorities are clear, the service is the easiest route to scale up, scale down or convert into internal hires. The rows below set out the trade-offs.
How to read the comparison
Look first at the best-fit row, then at key-person risk. Cost alone is misleading. A single hire can look cheaper on paper, but the true cost includes recruiting time, the months before that person is productive and the risk that knowledge leaves with them. A project can look cheaper too, until the first change request arrives after handover.
Analytics as a service trades a predictable monthly fee for continuity and breadth of skills. For a mid-sized company without a full department, the service is designed to lower key-person risk while it builds a data function. It also keeps the option to hire later, with a documented stack and a team ready to hand over. Use the table as a starting point for that conversation with your leadership team.
Benefits
CEOs, CFOs and COOs get one version of the truth and faster answers
Analytics as a service is bought by the executives who depend on the numbers, not only by data or IT leads. Each role gets something specific from a fractional data team, and all of them get one shared version of the truth.
That matters most in leadership meetings. When finance, sales and operations bring figures from the same governed model, discussion moves from reconciling numbers to deciding what to do. Questions that would otherwise need days of spreadsheet work can be answered from the shared model, often before the next meeting. The outcomes below are the ones the service is designed to deliver.
Each outcome below names the role, what changes for that role and the decision it supports. Each monthly cycle ends with defined outputs, so progress is visible from the first month. Each card is one example.
Strategic questions answered sooner
The CEO gets one view of performance and a team that answers new questions quickly, without adding permanent headcount or management overhead.
- No added permanent headcount
- Board packs from the same governed model
Reporting cost that is predictable
The CFO gets reconciled data, automated board packs and a monthly fee that scales up or down with priorities.
- Fixed monthly fee for an agreed scope
- Figures that reconcile across systems
Manual reporting removed from operations
The COO gets throughput, capacity and service-level dashboards, and teams stop maintaining spreadsheet trackers by hand every week.
- See where work is slowing down each week
- Teams stop updating trackers by hand
Forecasts and commissions that match
RevOps gets one definition of pipeline, bookings and revenue across CRM and finance, with fractional RevOps support when needed.
- CRM and billing reconciled into one view
- Fractional RevOps support when needed
Board meetings spent on decisions
Board packs come from the same governed model as internal dashboards, so directors discuss actions instead of questioning figures.
What you get
Every month ends with defined outputs you can check
Every engagement includes a defined set of monthly outputs. We agree them at scoping and review them each cycle, so you always know what the service delivered and what comes next. Each output is written up in plain English, so leaders who never open a dashboard still know what changed and why it matters.
A clear plan and a written summary
A prioritized backlog at the start, a delivery summary and review session at the end.
Dashboards people can rely on
New and updated dashboards, with refresh issues tracked and fixed as soon as they are detected.
Pipelines that keep running
Integrations, warehouse tables and data quality checks maintained across your source systems.
One meaning for every KPI
A maintained data dictionary that every team and report uses.
Decisions backed by analysis
Ad hoc analysis for pricing, hiring, budgeting or expansion, with a plain-English summary.
Data issues logged and closed
A running log of data quality issues, their cause and the fix, so recurring problems are solved at the source.
Use cases
Analytics as a service supports every function that runs on recurring reports
Analytics as a service supports any function that relies on recurring reporting and analysis. These are common areas for a fractional data team.
Finance closes and reports faster
Management accounts, cash and profitability reporting built from your ERP, with reconciliation built in so finance trusts every figure.
Sales sees one pipeline
Pipeline, conversion, retention and forecast reporting that reconciles CRM and billing data into one view for sales leadership.
Operations finds bottlenecks early
Capacity, delivery and service-level dashboards replace spreadsheet trackers and show where work is slowing down each week.
Marketing links spend to pipeline
GA4 and CRM reporting with consistent attribution rules, so budget decisions rest on the same numbers as sales.
Leadership plans from evidence
Executive dashboards and quarterly planning support, with a fractional CDO available for strategic questions and board preparation.
HR and people analytics
Headcount, hiring, attrition and cost reporting from HR and payroll systems, linked to finance for workforce planning.
A common starting point is one function, such as finance or sales, extending to others once the shared data model is in place. Each new use case reuses the same integrated data and definitions, so it costs less to add than the one before. That is how a small monthly plan grows into a company-wide analytics function without a large up-front investment.
Each use case below names the team, the outcome and the data behind it. They are examples, not a fixed menu. If a function in your company depends on recurring numbers, it can be covered by the same service and the same model.
Pricing
You pay a fixed monthly fee for an agreed scope, not for hours
Pricing is based on scope, not on hours logged. During scoping we estimate the monthly capacity your priorities require, then agree a fixed monthly fee for that level of service. You know the cost in advance, and there are no surprise invoices for extra hours.
Three factors drive scope: the number of data sources and systems involved, the volume of recurring reporting and maintenance, and how much strategic support you need from a fractional CDO or head of data. A company with a few systems and a defined report set needs a lighter plan than one running several entities, currencies and platforms. We share the scope assumptions in writing, so you can see exactly what the monthly fee covers.
Engagements can start small and grow. Capacity is reviewed at agreed points and can be adjusted up or down as priorities change. If you are not sure where to start, an AI and data readiness assessment gives you a baseline and a recommended service level before you commit to a monthly plan.
Common challenges
The usual concerns about an external data team have clear answers
Leaders considering an external data team usually raise the same five questions. Each one has a practical answer built into how we work. The concerns center on business knowledge, control of data, internal skills, flexibility and retained knowledge, and each is addressed in how the engagement is set up from the first week.
- 1
An external team that knows your business
Structured onboarding on your model, systems and KPIs, and we aim to keep the same people on the account.
- 2
Your data stays under your control
We work inside your environment and access controls. You own everything we build.
- 3
Internal staff gain skills
We work alongside your team and can add training.
- 4
Scope that changes with you
Capacity is reviewed regularly and adjusts up or down.
- 5
Knowledge stays with you
Every model, pipeline and definition is documented, so nothing depends on one person, inside or outside your company.
Frequently asked questions
Analytics as a service: questions buyers ask before handing over reporting
The basics3
Not in our case. Many providers use the term for cloud software. Our analytics as a service is a managed team of people who build and run your reporting, data pipelines and governance using the tools you already have or the ones we recommend together.
Outsourced BI usually means a vendor builds dashboards to a specification. A fractional data team takes ongoing ownership: it maintains pipelines and definitions, answers new questions and joins your planning rhythm, so the work keeps pace with the business instead of ending at handover.
We work inside your environment, under your access controls and policies. Access is role-based and reviewed at agreed points, credentials stay in your systems, and data is not copied to our own infrastructure unless you approve it. Every pipeline and permission is documented, so your IT or security team can audit the service at any time.
Scope, cost and working with us2
Yes, once a short advisory review has confirmed the priorities. The service can then begin with part-time analyst capacity focused on a few critical reports. As the value becomes clear, you can add data engineering or a fractional chief data officer without recruiting, because the service is designed to scale within the same team.
No. We usually complement internal staff by covering skills they lack, such as data engineering or governance, and by taking on maintenance work. Where useful, we also train your team so it can take over more of the work over time.
One call shows whether analytics as a service fits your team
Tell us which decisions depend on data today and where reporting falls short. In one call we outline the right engagement model, the first month's priorities and how analytics as a service would fit alongside your current team and tools. You leave with a clear next step, whether that is a readiness assessment, a defined project or a monthly plan, and no obligation to continue. If a monthly plan would be premature, we say so and recommend advisory or training first.
Next-step finder
Which next step fits your team?
3 questions. We suggest one next step. Nothing is sent anywhere.