Ask three departments about the same customer, product code or KPI and the chances are you get three different answers. Data sits across applications, files and systems that don't line up properly. What one department calls 'active' means something else somewhere else.
That costs time every day. People search, check, correct and retype data. Reports contradict each other and every new application starts the same detective work all over again. And if you let AI loose on data that isn't right? You get an answer faster, but still not the right one.
Data Engineering & Data Management puts a stop to that. Not with a one-off clean-up, but by setting up the whole data chain in a smart, reliable and manageable way. So you can build on your data today and tomorrow.
What does Data Engineering & Data Management cover?
Data management and data integration
Separate data sources that don't talk to each other cost time, money and trust. Bring them together in one clear data chain: structured, modelled and connected. That way everyone always works with the same information.
Data quality and validation
Good data doesn't happen by itself. See straight away where information is missing, out of line or simply wrong. With validation rules and smart data cleansing you fix errors and stop them coming back.
What does Data Engineering & Data Management deliver?
One reliable data base for reporting, analysis and AI applications.
Less time spent searching, checking, correcting and retyping.
One language for your whole organisation: the same definitions, the same data.
Data quality that is monitored continuously instead of repaired afterwards.
New data sources and applications connected faster, because the foundation is already there.
Decisions made on data you can trust.
Why Indicia?
Indicia is a tech, data and AI partner that helps clients stay ahead in the AI era. We think strategically with you, build what is needed and support the people who work with it. For results in weeks, not months. With privacy and security as the foundation. Driven by senior experts who are always right beside you.
We know what complex data landscapes look like in practice: legacy systems that can't simply disappear, data sources that don't line up and high demands on security and reliability. So we look beyond the technology. We make sure data is not only unlocked, but stays usable, reliable and manageable for good.
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Proven expertise
More than 30 years of experience in data, technology and AI.
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End-to-end
From analysis and architecture to integration, quality assurance and management.
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Dependable
ISO 27001 certified and holder of an ISAE 3000A Type II report.
Let's talk
Time for action. Let's look together at how we can make your organisation smart in digital. No obligations.
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Data management covers all the agreements, processes and techniques you use to structure, manage and keep data usable. Think data modelling, metadata, master data, data quality and connecting different data sources. The goal: information your organisation can rely on.
Data engineering is mainly about unlocking, processing and making data available technically. Data management makes sure that data stays unambiguous, reliable and manageable. Data engineering gets the data moving; data management makes sure it keeps its quality and meaning along the way.
No, and not all at once either. We map your data sources first and look at where the biggest quality problems, dependencies and opportunities are. Then we tackle what delivers most, step by step.
We map the sources, data flows and definitions and connect them with suitable integrations. Then we model the data into a coherent structure. With validation rules, data cleansing and monitoring we make sure the quality improves demonstrably and stays that way.
We start with which core data is authoritative, who is responsible for it, and which systems use it. Then we set up definitions, ownership, processes and technology so changes flow through the whole organisation in a controlled way. That is how you avoid several versions of the same truth.
By making quality rules and automatic checks part of the data chain. Gaps in completeness, consistency and currency then show up immediately. So you can step in before errors reach reports, business processes or AI applications.