Business Intelligence & Decision Analytics

Decisions stop queueing behind analysts

Business users answer their own follow-up questions against governed metrics, so analyst capacity goes to new modelling rather than to repeat requests.

One number for each measure

Metrics are defined once in a semantic layer and consumed everywhere, so revenue, margin, active customer and on-time delivery mean the same thing in every report and every conversation.

Attention goes to the exceptions

Anomaly detection and alerting surface what has changed, so managers stop scanning dashboards for problems a rule could have found for them.

Planning moves from extrapolation to forecast

Statistical and machine-learning forecasts replace informal extrapolation from last year, with the uncertainty shown rather than hidden inside a single figure.

Analysis reaches people who would never open a BI tool

A natural-language interface delivered inside the tools people already use lowers the barrier for a group that self-service analytics has historically excluded.

The decision is recorded, not only the chart

Decision workflows capture what was decided, on what evidence and by whom, so the organisation can review its own judgement afterwards.

Decisions

Map the recurring decisions the business makes, who makes them and what evidence each one needs.

Decision inventory

Metrics

Define and agree each measure once, with a named owner and a written calculation.

Certified metric definitions

Semantic layer

Implement the definitions over governed data, with row-level and column-level security applied at the layer.

Governed semantic model

Interfaces

Deliver the analysis where the decision is made: dashboards, alerts, embedded views and natural-language querying.

Analytics and conversational interfaces

Intelligence

Add forecasting, anomaly detection and scenario analysis where a decision needs more than description.

Forward-looking analytics services

Adopt and measure

Enable users, certify metrics, measure usage and retire what is no longer trusted or opened.

Adopted, governed decision layer

Can business users really ask questions in plain language and get correct answers?

They can, but only within the boundary of what has been defined for them, and this is the part that is usually sold dishonestly. A natural-language interface is a translator: it maps a question onto metrics and dimensions that already exist, and where a metric has not been defined it either fails or improvises, and improvisation is the dangerous outcome. With a well-built semantic layer beneath it, the interface answers reliably across the questions the business actually asks and says clearly when a question falls outside its scope. Without one it will answer anyway, and nobody will be able to tell the good answers from the bad.

We already have a BI platform. Why is this different?

Most BI estates are a collection of reports rather than a decision layer, which is why adding another dashboard rarely changes anything. This offering starts from the decisions the business repeats, works back to the measures those decisions need, defines each measure once with an owner, and only then builds the interfaces. Your existing platform is normally kept and used as one of those interfaces rather than replaced. What changes is the layer beneath it and the discipline around definitions, which is where the disagreements originate.

How do we stop teams disagreeing about the numbers?

By moving the disagreement to where it belongs, which is the definition rather than the report. When each metric has a single written calculation and a named owner, two teams quoting different figures becomes immediately diagnosable: either one of them is not using the certified metric, or the definition itself needs to change and that change goes through a controlled process. This does not eliminate disagreement and it should not, because legitimate differences exist between a finance view and an operational view of the same object. What it eliminates is the unproductive version, where two figures differ and nobody can explain why.

Is forecasting reliable enough to plan against?

A forecast is a statement of probability rather than a prediction, and it becomes useful when it is presented that way. ARRIX publishes forecasts with a range and with the assumptions visible, so a planner can see how confident the model is and where it is most likely to be wrong. Accuracy depends heavily on the history available and on whether the underlying pattern is stable, which is why the model is backtested against your own data before it enters a planning process. Where the data cannot support a credible forecast, the honest answer is to say so rather than to publish one anyway.

Will this replace our analysts?

No, it changes what they spend their time on. Analytics teams commonly lose much of their capacity to repeat requests and variations on questions already answered, and that is precisely the work a governed semantic layer and a self-service interface absorb. What is left is the work that needs judgement: defining and owning metrics, investigating anomalies, building models and challenging conclusions that look wrong. In practice the analyst role moves closer to that of a data product owner, and organisations that plan for that shift get more from the investment than those that do not.

Does our data have to be perfect first?

No, but it does have to be trustworthy for the specific measures in scope, which is a far smaller requirement. ARRIX scopes the first decisions against data that is already reliable enough, and where a required field is not, that becomes remediation work with a known cost rather than a silent defect inside a dashboard. The failure to avoid is launching a self-service or conversational interface across an estate whose quality has never been measured, because the interface exposes every existing data problem at once and to the widest possible audience. Sequencing those two things properly is usually the difference between adoption and abandonment.

How long before people actually use it?

Adoption is the usual failure point rather than the technology, so it is designed for from the start instead of treated as training at the end. The most reliable pattern is to begin with one decision made repeatedly by an identifiable group, deliver it end to end, and let that group's use of it carry the next one. Retiring the reporting it replaces matters as much as delivering it, because a superseded dashboard left running keeps the old numbers alive and the argument going. ARRIX measures usage explicitly, because a decision layer nobody opens is a cost with nothing on the other side of it.