Automation and artificial intelligence

Data clean-up and data flow

One agreed dataset on customers, products and sales, fed from your systems by a data flow that shows whether the data has arrived, ready for reports and AI models.

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Scope

What clients come to us with

  1. Three departments, three sales figures

    Sales reports revenue from the CRM, finance from the ERP, marketing from the campaign tool. The figures differ because one system counts an order on the day it is placed, the second on the invoice date, and the third leaves out returns. Each department defends its own figure, because in its own system that figure is correct.

  2. An AI project stalled on the data

    The company wants to launch a model that predicts customer churn. The contractor receives extracts from several systems: the same customer under two numbers, empty fields, a purchase history cut off after a system change. A model trained on such data will repeat its errors, so the project waits until someone sorts the data out.

  3. Data comes in faster than anyone can read it

    Tills, the online shop or devices on the network generate data non-stop, but it reaches the report once a day, in a nightly export. By the time someone notices in the morning that some devices have stopped responding, a whole night has passed since the event.

Scope

What we do

  1. We start by agreeing what each data item means, because ‘customer’, ‘active contract’ and ‘sales’ have a different definition in every department. For every data item on customers, products and sales we establish the system where it originates, the person in the company responsible for it and a definition accepted by all departments. We also write quality rules for a record with missing data, a customer recorded twice and two systems with conflicting values. Each such case gets a rule: reject it, flag it for review, or take the value from a specified source.

  2. Next we build the data flow: the route data takes from the systems where it originates to the place where reports and AI models use it. That place is a data warehouse, a single database of checked data from all systems, in which every report and model sees the same figures. The transport itself is ETL: extracting data from the source, checking it against the quality rules, transforming it into a common form and loading it into the warehouse. The flow runs periodically or in real time, meaning that a change in the source system reaches the warehouse as soon as it happens.

  3. Systems already in use stay where they are. Connecting systems so that they exchange data with one another in daily work is a separate scope within the Systems and integrations service; here the focus is data for reports and models. Management reports and dashboards on this warehouse are a separate scope. The outcome of the work is a description of the sources, the quality rules and a working flow with a view of whether the data has arrived.

Scope

What you get

    • A description of data sources: for each data item, the system where it originates, a definition agreed between departments and the person responsible on the company's side.
    • Data quality rules: rules for gaps, duplicates and conflicts, and a list of the people notified about rejected records.
    • A data warehouse in the cloud in the European Union or in your server room, with one dataset for reports and AI models.
    • A data flow from each source system to the warehouse, periodic or real-time, with a description of every step.
    • A flow monitor: which data has arrived, how many records were rejected and why, with a notification when nothing has come from one of the systems.

Scope

How the work runs

    1. We establish where each data item comes from. We sit down with the people who put reports together today and check how each system records customers, products and sales. Result: a description of the sources and a list of conflicts between systems for you to resolve.
    2. We agree the definitions and quality rules. For every disputed data item we name the source and the person responsible; for gaps and duplicates we write rules. Result: quality rules that you approve before we start building.
    3. We build the flow and test it on your data. We bring the systems into the flow one by one and compare the figures in the warehouse with those the departments have in their reports today. Result: a warehouse with checked data and an explanation of every difference from the previous figures.
    4. We bring the flow into daily work. Report users and the AI team move to the warehouse, the old exports are switched off, and the monitor and notifications go to a designated person. Result: one data source in use and a person responsible for overseeing it.

Scope

Team experience

  1. For a client in the financial sector on the European market, members of the team led the consolidation of customer and campaign data, combining it into one dataset. In the cloud and security area, on the US market, the team built telemetry, the continuous collection of data on how a network is performing, and a real-time network ETL.

Scope

Questions the board should ask the contractor

    1. Which system will be the source of customer data when the CRM and the ERP give different values, and who on our side will decide?
    2. What will the flow do with a record that has missing data or is recorded twice: reject it, flag it or let it into the warehouse?
    3. How long after a change in the source system will the data appear in the warehouse?
    4. Who will find out that data from one system has not arrived, and how soon?
    5. Where will the warehouse data be stored, which of it is personal data, and who grants access?
    6. Will the definitions and quality rules be written down so that another contractor can add a new data item without asking the author?

  2. Which data, from how many systems and how quickly it should reach one dataset is settled in the process analysis, which ends with a recommendation and a scope of work.

Parent service

Automation and artificial intelligence

AI agents, document recognition and automatically assembled reports take over repetitive work; after launch we measure quality and maintain the models.

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Book a meeting

Book a conversation about the problem

You describe the process or system that no longer keeps up and what has to change. The conversation costs nothing.

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Phone: +48 455 406 404

Automation and artificial intelligence

Other scopes of this service

See the service: Automation and artificial intelligence

Natural language processing

A program that reads your company's scans, e-mails and internal rules, turns them into data and answers that cite their source, and hands uncertain cases to a person.

See the scope

Looking after AI after launch

Your agent or model looked after once it is live: answer quality checked on current samples, usage costs under control, and a decision log ready for an audit or a complaint.

See the scope

How we start

  1. A conversation about the problem

    You describe the process or system that no longer keeps up and what has to change. The conversation costs nothing.

  2. Analysis under an NDA

    We examine the process and the systems and hand over a recommendation with a scope of work. You know the scope, deadline and price of the analysis before it starts.

  3. Offer

    You receive the scope, team, schedule and price of the implementation. You decide whether we go ahead.

The cost of the analysis is credited towards the implementation if you order it.

Let's talk about your project

Phone: +48 455 406 404 E-mail: contact@aliaxit.com

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