Area 16 · Data, AI and consulting

Data and analytics

Departments' figures disagree not because someone made a mistake but because each counts by its own formula and pulls data from its own system. We tidy the reference data, bring everything into one place and make sure a number in a report needs no explanatory note.

Discuss your reporting What we do

services in this area

One

the source for every report

Dashboards

for each department

Automatically

reading incoming documents

What the area consists of

Three items follow strictly in order: first the rules, then the warehouse, and only after that automatic processing of incoming documents.

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Data and data quality management

Single lists of partners, products and organisational units, the rules for adding to them and the catching of duplicates. Skip this stage and you get a system that simply processes rubbish faster.

Corporate data warehouses

Metrics from the accounting system, the online store and third-party services flow into a shared warehouse, from which datasets on sales, stock, money and production are built. The result no longer changes depending on who prepared the report.

Intelligent document processing

We read scanned source documents: delivery notes, invoices, certificates of completion. Numbers, dates and amounts land in the database by themselves while the employee only checks and approves.

What hurts and why

The request usually sounds like «build us a nice dashboard». Almost always one of the problems below is behind it, and that is what needs treating.

Departments have different figures

We agree on common definitions. The reason is simple: the same metric is calculated by different formulas.

The same counterparty entered three times

We clean the reference data and set the rules. Duplicates appear where nobody checks at the moment a record is created.

The report takes three days to assemble

We build the warehouse with scheduled refreshes. Assembling it by hand from exports eats whole days of an analyst's time.

By the meeting the figures are out of date

We set up scheduled loading. A manual export once a month cannot keep up with the decisions.

Accounts are drowning in paper

We switch on automatic document reading. Details and amounts are typed from paper by hand, line by line.

A pretty dashboard on top of dirty data is more dangerous than no dashboard at all. While a report is assembled by hand, a mistake gets noticed along the way. A finished chart is persuasive by its very look, and people act on it without bothering to check the underlying numbers.

How we build the reporting

We start with one department and a narrow set of figures. Trying to cover the whole organisation at once drags on for months and usually ends in nothing.

We agree the metrics

We agree what counts as revenue, as a shipment and as an active customer. The dullest and the most important step.

We look at the sources

We establish what is stored in which system and how usable it is. We estimate how much cleaning up is needed first.

The first dashboard

We start the scheduled data pipeline and build the first dataset around the questions the director actually asks.

We add the rest

We add further systems and departments. Each new connection is cheaper than the last because the foundation is already built.

Questions and answers

When all the information sits in a single system and nobody disputes its figures, no extra layer is needed. The need arises with several data sources: the accounting system, the online store, the warehouse system, advertising accounts. Pulling all of that into the live database is awkward, and large queries against it slow down everyday work.

With tidy data, about a month including agreeing the list of metrics. When the reference data has been neglected, the bulk of the time goes on sorting it out and the estimate shifts. That is why we study the contents of the databases first and only then quote a timeline.

On standard printed forms the accuracy is high; on handwritten notes and murky scans it is lower. That is why there is always human confirmation in the scheme: the software fills the fields, the accountant checks and posts. The time saving remains even with spot checks.

Both are possible. Under a support contract we do it: we watch the data pipelines, adapt to changes in the source systems and add metrics at your request. Or we hand everything over to your own specialists with the documentation. The choice is fixed at the start of the project.

Related by task

Browse the catalogue

Area 09 Information security Who has access to the data Area 13 Custom software development Exchange between the sources Area 15 Artificial intelligence What gets built on clean data Area 17 Consulting and audit Which metrics are actually needed

Let us sort out your reporting

Напишите, какие сводки готовятся вручную и в каких показателях возникают расхождения. Изучим системы-источники и предложим, с какого набора данных начать.

Phone
8 (777) 121 5771

Email
office@apply.kz

Office
Almaty, Bukhar Zhyrau boulevard, 62B

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