Data & Analytics Maturity Assessment
If two areas bring the same figure to a committee and the numbers do not match, the problem is not the report. We evaluate where your data comes from, what can be trusted and which decisions you could already be taking with what you have, and build the roadmap to close the gap.
The assessment walks the full path of the data —origin, quality, governance, model, consumption and decision— and rates the maturity of each leg. It identifies which decisions currently lack support and what it would take to sustain them, and delivers the roadmap agreed with the areas that consume the information.
Know what you can trust
Which sources are reliable, which are not, and which decisions currently depend on the latter.
Order the data's path
Where it originates, who owns it and which version prevails when two disagree.
Use what you already have
Which decisions you could take today with the existing information, without investing in a platform.
One surveys and maps the route. The other executes it.
These are two different services and it is worth knowing which one you need before contracting. The assessment does the first information-gathering exercise, builds the roadmap and aligns it with the business areas. The consulting practice executes that roadmap.
Data & Analytics Maturity Assessment — this page
Surveys, maps and aligns. It surveys the sources, measures quality, evaluates governance and model, identifies the decisions without support and builds the roadmap agreed with the areas that consume the data. It runs two to four weeks depending on the size of the organisation. It does not implement, does not configure and does not operate anything.
Analytics — execution
Executes the roadmap. It takes the roadmap and executes it: data engineering, modelling, building dashboards and reports, and continuous operation of the analytics platform. It works just as well if you already have a roadmap of your own.
Eight legs of the data's path.
Trust in a figure is lost at the weakest leg of the journey. We review each one with the same depth and rate it by maturity level.
Sources and origin
Which systems each relevant figure comes from, how often it refreshes and how complete it arrives.
Quality
How accurate, complete and consistent what you have is — measured, not assumed.
Governance and ownership
Who answers for each data domain and who decides which version is true.
Model and definitions
Whether a single definition exists for the business concepts, or each area calculates its own.
Integration and flow
How data travels between systems, what transformations it undergoes and where traceability is lost.
Analytics platform
What tools the information is exploited with today and whether they sustain what the business asks for.
Consumption and adoption
Who uses the reports, how often, and whether they use them to decide or to justify.
Privacy and compliance
What personal or sensitive data exists, how it is protected and what the applicable regulation demands.
Six phases, without interrupting your operation.
The assessment rests on interviews, source review and sample analysis. It never modifies data or systems.
Scope and preparation
We define which data domains and which decisions are in, and agree read-only access.
Business interviews
With the people who decide on the basis of data, to understand what they ask and what they cannot answer today.
Source review
Origin, frequency, completeness and transformations, checked against what the interviews said.
Quality analysis
Measurement on real samples: accuracy, completeness, consistency and duplicates.
Gap analysis
Comparison against DAMA-DMBOK and DCAM, leg by leg along the data's path.
Business alignment
Presentation of the roadmap with leadership and the areas, so priorities are agreed.
What your maturity is measured against.
The evaluation does not rest on the judgement of whichever consultant shows up, nor on a vendor catalogue, but on public and auditable frameworks your team can consult and your auditor will recognise. Data governance is also read against the law that applies to you —Habeas Data (Ley 1581) in Colombia, the LFPDPPP in Mexico— not only against a generic maturity model.
DAMA-DMBOK
Evaluation framework. The data management body of knowledge: governance, quality, architecture and lifecycle.
DCAM
Maturity framework. Data management capability model. It provides the scale that makes the result comparable.
ISO/IEC 25012
Quality guide. Defines the data quality dimensions that get measured: accuracy, completeness, consistency and the rest.
ISO/IEC 38505
Governance guide. Corporate governance of data: who answers for it and to whom.
Applicable data protection law
Regulatory guide. The local personal-data framework, which determines what may be collected, retained and processed.
Kimball / dimensional modelling
Modelling guide. Reference for evaluating whether the analytical model sustains the questions the business asks.
The frameworks are public and verifiable: anyone on your team can consult them and check the rating we deliver. That is the difference between an auditable evaluation and an opinion.
What it costs to decide on data nobody certified.
The cost of bad data does not show up on an invoice: it shows up in a decision taken badly that nobody connected back to its source.
Figures that contradict each other
Two areas present different numbers for the same thing and the committee argues about the source instead of the business.
Decisions without support
Choices get made on intuition because the data does not arrive in time or is not believed.
Reports nobody uses
Money goes into dashboards that do not answer the questions the business actually asks.
Invisible manual work
Hours every month consolidating spreadsheets by hand, an effort nobody counts as a cost.
Regulatory exposure
Personal data with no access control or retention policy, in a regulatory environment that keeps tightening.
Analytics that does not scale
Every new question demands development, because there is no model to sustain it.
What it costs you in time, said upfront.
An assessment that does not state the commitment it requires ends up delayed. This is what we need from your side to deliver on time.
Two to four weeks
Two weeks in organisations of up to 50 employees; four between 51 and 300. The timeline is agreed before starting.
Scheduled interviews
Sessions with the people who decide on the basis of data, with the owners of the source systems and with the technical team.
Read-only access
Queries against the platforms and existing documentation. At no point is a configuration modified.
A single point of contact
One person coordinating schedules and access. It is the factor that most affects hitting the deadline.
Whatever documentation exists
Data dictionaries, existing reports, indicator definitions and privacy policies, in whatever state they are in.
A closing session
The presentation of findings with leadership and the areas involved, where the roadmap priorities are agreed.
When it makes sense and when it does not.
It makes sense if…
Figures do not match across areas; there is monthly manual work to consolidate information; you are about to invest in an analytics platform and want to know whether the data supports it; or you want to use AI and need to know whether you have the foundation for it.
Probably not if…
You already have the data in order and want the dashboards built: go straight to Analytics. Or if your question is about artificial intelligence use cases, that is covered by the AI Readiness Assessment, for which this one is usually the prior step.
What you gain from the assessment.
Trust, measured
Which sources are reliable and which are not, by measurement rather than perception.
A single version
Single definitions for the business concepts, and one owner per data domain.
Decisions unblocked
The questions that cannot be answered today, with what is missing to answer them.
Less manual work
The by-hand consolidations identified, with their cost in hours.
A backed investment
Whether it is worth investing in a platform or whether the origin has to be fixed first.
A foundation for AI
A diagnosis of whether your data sustains artificial intelligence use cases, before attempting them.
Why this evaluation and not a sample dashboard.
The difference is not in showing a good-looking chart: it is in saying whether the number feeding it can be trusted.
Evaluation, not demonstration
A sample dashboard proves the tool. This evaluates whether the data feeding it can sustain a decision.
Quality measured
Quality is measured on real samples of your sources, not estimated in conversation.
Recognised frameworks
The comparison is against DAMA-DMBOK and DCAM, with quality dimensions from ISO/IEC 25012.
Business language
Each gap tied to the decision it blocks, not to a technical attribute of the model.
Vendor independence
The roadmap is not shaped by whichever analytics platform would suit us to sell.
Continuity into execution
If you decide to proceed, it connects with Analytics without surveying the sources again.
As-Is, To-Be and the plan to get from one to the other.
Every assessment closes with the same structure, whatever the practice: where you stand today, where you need to be, what separates the two states and in what order that distance gets closed.
Current state — As-Is
The starting point surveyed with evidence, not declared in an interview: what exists, how it operates and how far it sits from what the business needs.
Target state — To-Be
Where the organisation needs to get to, defined with the business areas rather than imposed by the consultant. It is the benchmark everything else is measured against.
Gap analysis
Every difference between the As-Is and the To-Be, with everything required to close it: technology, processes, people, governance and budget. No gap is stated without what it demands.
Risk matrix
Each gap rated by probability and business impact, so priority does not depend on who pushes hardest but on what it costs to leave it open.
Work plan
The concrete sequence to reach the To-Be: what comes first, what it depends on, how much effort it takes and who should answer for each front.
Business alignment
The plan is presented and agreed with the areas involved. A roadmap signed only by IT does not survive the first quarter.
How what you receive is structured.
The central deliverable is a report with a fixed structure, designed so leadership reads the first pages and the technical team works with the rest.
Executive summary
Two pages: maturity level, the three gaps that weigh most and which decisions currently lack support.
Maturity by leg
Rating of the eight legs with the gap made explicit against DAMA-DMBOK and DCAM.
Quality diagnosis
Measurement on real samples per source: accuracy, completeness, consistency and duplicates.
Data flow map
Where it originates, what it passes through and where it is transformed or loses traceability.
Roadmap
Gaps prioritised by decision unblocked against effort, with a suggested owner.
Immediate actions
What can be corrected or defined without a project or a new platform.
What you receive at the end.
- Current state (As-Is): data and analytics maturity across each of the eight legs, with the data's flow from origin to consumption.
- Quality measurement on real samples of the sources evaluated.
- Inventory of sources with frequency, completeness and owner.
- Diagnosis of definitions: where the business calculates the same thing differently.
- Inventory of decisions that currently lack support in data.
- Target state (To-Be): the level of quality, governance and availability those decisions demand, agreed with the areas.
- Gap analysis between the As-Is and the To-Be, with each gap, its evidence and everything required to close it: sources, quality, definitions, governance and capabilities.
- Risk matrix: quality, privacy and compliance, rated by probability and business impact.
- Work plan to reach the To-Be, prioritised by decision unblocked against effort.
- Executive presentation for committee and leadership.
- Alignment session with the areas that consume the information.
About the Data & Analytics Maturity Assessment.
What exactly do you evaluate?+
How does it differ from analytics consulting?+
Do you really measure quality, or estimate it?+
Do we need to have a data platform?+
Do you modify our data during the assessment?+
Who should take part on our side?+
Is it useful as a prior step for artificial intelligence?+
How often should we repeat it?+
Know which data you can trust before deciding with it.
Book your Data & Analytics Maturity Assessment and get the quality measurement of your sources, the data flow map and a roadmap prioritised by decision unblocked.
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