Imagine that all the data in your company — from customer lists to sales reports — is not just rows in tables, but a valuable asset. Like money in an account or equipment in a warehouse. Data Asset Management is an approach where this information is treated like a product to maximize its value. How does this work in practice? Through the "Data as a Product" approach.

The idea is simple: to get the most out of your existing data, you need to stop thinking of it as a byproduct of your work or ignoring it altogether. Instead, create ready-to-use “products” from it for your colleagues (analysts, marketers, managers).

What is a “data product”? It is a set of information that:

- Is made for a specific “consumer”
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- Is simple and understandable: the data is clean, accurate, and includes an easy-to-follow description of what it is, where it comes from, and how often it is updated
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- Is easy to find: all “data products” are collected in a unified catalog (tools like DataHub, Collibra, or Atlan are used for this), where they can be quickly located, like items in an online store
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- Has the same lifecycle as regular products: idea generation, product creation, active usage, obsolescence, archiving, and replacement with a new product.

If you don’t yet have experience setting up master systems, we will guide you through the process from start to finish. We will create a plan, outline step-by-step actions, define system responsibility areas, and, if necessary, help technically migrate data from one system to another, filtering out unnecessary and outdated data beforehand.

Garbage In – Garbage Out (GIGO) is a common situation when poor data quality causes the final product to fail, or the company underuses its potential and ends up with weak results.
To avoid such situations, we help implement data quality management, which includes:

- Data contracts and schema governance

- Automated testing and quality checks

- Scorecards that help assess how trustworthy the company’s data is at different stages

This approach is especially useful when integrating data from different sources, migrating data, building reports, or developing analytics systems. For example, during a client base migration from an outdated CRM to a new platform, we can automatically detect duplicates, missing values, or inconsistencies in formats. In reporting — we configure regular checks for key metrics so that any deviations are detected immediately, not at the decision-making stage. All this enables the business to make decisions based on reliable, verified data.

Regulators are paying increasing attention to data protection and ensuring privacy. To meet these high standards, companies must continuously improve their approaches to data collection, storage, and processing:

- Minimize data flow between systems

- Ensure auditability and control over data access

- Minimize access to personal data

- Ensure encryption (masking) of sensitive data

- Ensure user anonymization — not only of identifiable data but also of data combinations that could potentially identify a user.

This level of data handling requires careful planning of data flow organization and access level configuration. It not only helps meet regulatory requirements but also guarantees the ability to quickly detect violations in data handling, avoid fines and reputational damage, and significantly increase customer trust in the company.

Imagine you are in a library with no classification or categorization. In that case, to find a specific book, you would have to sift through everything available. Obviously, this is inefficient and very time-consuming.

Now think about how quickly company leadership gets answers when a new question arises. Teams don’t know where to look for data, whom to contact, or how to process what they find.

Classification and categorization allow you to:

- Significantly reduce the time it takes to find information

- Improve data security by identifying vulnerable categories

- Ensure minimum necessary access levels to data

- Comply with regulatory requirements for data usage

- Standardize data management policies

-Ensure the reliability of critical data and reduce costs for storing unnecessary data

- Set up effective archiving

We offer Data Classification and Categorization through the implementation of modern data catalogs, automatic tagging, the introduction of a corporate glossary, and the establishment of data governance policies.