> For the complete documentation index, see [llms.txt](https://docs.mediarithmics.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mediarithmics.io/setup-and-monitor/understand-mediarithmics/platform-overview.md).

# Platform overview

What mediarithmics is, what it does with your data, and who works in it.

Start here if mediarithmics is new to you. This page explains what the platform is for, what happens to your data once it reaches the platform, and which teams work in it - before you get into how it is built or how to set it up.

## What mediarithmics is

mediarithmics is a **data-first marketing cloud**: a platform built so that an organisation can use all of the information it holds about its users to make better decisions across marketing.

It can be looked at in two ways, and most organisations use it both ways at once:

{% columns %}
{% column %}

### A set of marketing applications

Digital marketing applications that marketers use directly - to build audiences, activate them, automate user journeys, and measure results.
{% endcolumn %}

{% column %}

### A data marketing infrastructure

A highly customisable data marketing cloud that data engineers and data scientists tailor to the needs of their organisation.
{% endcolumn %}
{% endcolumns %}

## From collection to activation

The purpose of a data marketing platform is to make all available information usable when a decision has to be made. mediarithmics does this in four movements: collect, store, analyse, act.

### Collect

The platform can collect all of your data sources. That includes your users' online activity, and your back-office management systems - Content Management Systems (CMS), Enterprise Resource Planning (ERP), and Customer Relationship Management (CRM) software.

### Store

All of the data is stored for each user, in a graph model. This universal data model lets you connect everything the platform knows about one user:

* Account identifiers and device identifiers.
* Profile information.
* Activities and purchases.
* Consent - the [choices](/setup-and-monitor/ensure-privacy-and-compliance/manage-consent-and-processing-activities.md#user-choices) each user has made about the data processing your organisation declares, with their history, captured live or [imported from your consent platform](/setup-and-monitor/collect-and-manage-your-data/import-data-in-batch/import-user-consent.md).
* Data calculated on the fly - such as affinity scores, age and gender predictions, and recommendations of articles or other content.

<figure><img src="https://3807133775-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FqKdjXwF9sl7HD9IgXzK1%2Fuploads%2Fgit-blob-8768029c42f423197c3323e1e1c2ec8ca4eab25b%2Fcollection-to-activation.png?alt=media" alt="Data sources are collected into a per-user graph, then activated towards emailing, advertising, customer experience and BI tools"><figcaption><p>From data collection, to a per-user graph, to activation</p></figcaption></figure>

### Analyse

Because the data is centralised rather than scattered, you can run many kinds of analysis on it: visit analytics, uplift analytics, segment insights, and data discovery.

### Act

When the time comes to act, there are two routes:

* **Activation.** Send audience segments out through a rich ecosystem of connectors - advertising networks, communication tools such as notifications and emails, and personalisation tools such as on-site display and A/B testing on the user journey.
* **Automation.** Define orchestrations that trigger automatically, aligned with the highlights of the user experience: a first visit, the creation of an account, a drop in visit frequency, a [cart abandonment](https://app.gitbook.com/s/XSPACE_AUDIENCE/automate/cart-abandonment-automation).

## Why the data sits in one place

In many companies, the recent history of data marketing has been built by accumulating solutions that stack or overlap - one for acquiring new customers, one for improving conversion rate, one for retaining existing customers, one for building predictive models. Each application makes sense in isolation and serves a team.

After a while, though, that structuring in silos stops making it possible to be reactive and to innovate. The same data is multiplied across multiple systems, and those systems cannot speak to each other because of the sheer weight of the data involved.

mediarithmics was designed from the start to remove those structural difficulties. In the data-first model, the data gathered in a single [datamart](/setup-and-monitor/plan-and-integrate/set-up-the-environment/prerequisites-organisation-and-datamart.md#datamarts) is shared by all applications and all users. Both mediarithmics applications and partner applications read from the same source of truth - for the analytical queries that mobilise large amounts of data, and equally for the real-time personalisation micro-queries that only care about the characteristics of a single user.

### One place, not necessarily one product

The model asks that the data sit in one place. It does not ask that the place be inside mediarithmics.

The platform can interface natively with your cloud data warehouse and treat it as the source of truth, so the single shared store stays in your own environment while the applications keep reading from it. Which arrangement applies is a property of each datamart, and it is a decision to take early: see [Choose your deployment mode](/setup-and-monitor/plan-and-integrate/choose-your-deployment-mode.md).

## Who works in the platform

The platform is built for several audiences at once, and the documentation is written for all of them:

| Audience           | What they get from the platform                                  |
| ------------------ | ---------------------------------------------------------------- |
| Marketers          | Digital marketing applications they can use directly.            |
| Ad and digital ops | Day-to-day operation of campaigns, activation, and measurement.  |
| Data engineers     | The infrastructure to collect, model, and move data.             |
| Data analysts      | Querying, reporting, and analytics on centralised data.          |
| Data scientists    | A place to run and serve their own algorithms on the user graph. |

## Where to go next

<table data-view="cards" data-search="false"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>How mediarithmics works</strong></td><td>How an instance is organised, where your data lives, and how it is modelled.</td><td><a href="/setup-and-monitor/understand-mediarithmics/how-mediarithmics-works.md">How mediarithmics works</a></td></tr><tr><td><strong>Navigate the platform</strong></td><td>Find your way around the applications, the menus, and search.</td><td><a href="/setup-and-monitor/understand-mediarithmics/navigate-the-platform.md">Navigate the platform</a></td></tr></tbody></table>


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