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Data and analytics

We turn scattered data into reports you can trust — from data quality to BI and predictive analytics.

Through data strategy, data quality management and the implementation of analytics solutions, we help companies turn data into actionable insights.

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Data as a business resource

Data is now one of a company's most important assets. Companies that manage data successfully and use analytics for decision-making achieve greater efficiency, better control and a greater ability to adapt to market changes.

Where data gets lost

In practice, many organizations face disconnected data sources, inconsistent information and a lack of clear KPIs, which makes sound planning and business management harder.

Our approach

Our approach covers data organization and governance, defining KPI models, implementing BI solutions and developing advanced analytics, with the aim of giving companies a clearer view of the business and a stronger foundation for growth and development.

Start organizing your data

across key areas of work

Data strategy and governance

We define a data management strategy and establish governance models that ensure the quality, security and consistency of information.

Data is the foundation of modern business, but without a clearly defined strategy and governance rules, the result is often inconsistency, unreliable information and harder decision-making.

Through a data strategy and governance approach, we define how data is collected, organized, used and managed within the company. The goal is to establish clear rules, responsibilities and standards that ensure the quality, security and consistency of information across the whole business.

A well-designed governance model gives companies more control over their data, better analytics and a more solid foundation for automation, AI and further digital growth.

Data quality management

We improve the quality and reliability of data to enable more precise analytics and better business decisions.

Inaccurate, incomplete or inconsistent data can significantly affect the quality of analytics, reporting and business decisions. When information is unreliable, the result is process errors, harder planning and lower business efficiency.

Through data quality management, we analyze existing information, identify problems in data structure and consistency and set up models to improve and maintain data quality. The goal is for data to be accurate, up to date and reliable across the whole business.

High-quality data enables more precise analytics, more confident decisions and better-performing business systems, while also being the basis for automation, BI and AI solutions.

BI and analytics solution implementation

We implement BI and analytics tools that give a clearer view of the business and enable more efficient decisions.

Many companies hold a significant amount of data, but without the right analytics tools that information often stays unused or hard to access for day-to-day business management.

By implementing BI and analytics solutions, we help companies bring together data from different sources and turn it into clear, readable and useful business insights. The goal is for management and teams to have timely access to relevant information that supports better decisions.

BI and analytics tools enable more efficient tracking of performance, KPIs and business trends, with more transparency and better control over the business.

Defining KPI and reporting models

We create KPI and reporting models that enable performance tracking and better management of business results.

Without clearly defined KPIs and structured reporting, companies struggle to track business performance and make timely decisions based on relevant information.

By defining KPI and reporting models, we establish a system for tracking key business indicators in line with the company's goals and priorities. The goal is for management and teams to have a clear view of results, performance and trends through standardized, readable reports.

A well-designed reporting model enables greater business transparency, more efficient management of results and faster recognition of risks and opportunities for improvement.

Advanced analytics and predictive models

We develop advanced analytics models that predict trends, customer behavior and future business results.

Modern business requires more than a view of historical data. Companies need tools that let them anticipate future trends and plan better.

Through advanced analytics and predictive models, we use data, statistical methods and AI approaches to identify patterns, predict customer behavior and analyze potential business scenarios. The goal is for companies to make proactive decisions based on real data and trends.

Predictive models enable better revenue planning, resource optimization, an improved customer experience and timely recognition of business opportunities and risks, giving companies more confidence and a competitive edge in the market.

Business benefits

Better forecasting of business results

Analyzing historical and current data enables more accurate forecasting models and timely recognition of trends.

Faster, more efficient reporting

Automated reporting models provide timely access to data.

Better decision-making

Business decisions are based on accurate, relevant data.

A foundation for AI and advanced automation

Quality data is the foundation for developing advanced analytics and AI solutions.

More reliable, better organized data

A consistent, high-quality information structure is established across the whole business.

Greater business visibility

Clear insight into performance, processes and KPIs.

This service is for companies that

use multiple disconnected data sources and lack a single view of the business
have problems with data quality, accuracy or availability
want better KPIs and clearer reporting
make decisions without sufficiently reliable information and analytics
are planning to develop BI, AI and advanced analytics solutions
want to improve data management and business decision-making

Frequently asked questions about data and analytics

Where should we start with analytics?

With the questions management wants answered, not with the tool. First we define the key performance indicators (KPIs) and the decisions they should support, then we check where the data comes from and how reliable it is, and only then do we choose and configure a BI solution.

Why do reports from different departments show different numbers?

Most often because departments use different definitions of the same indicator, different data sources and manual processing in spreadsheets. The solution is unified KPI definitions, a single source of truth for each piece of data and clear rules on who enters, changes and approves data.

What is data governance?

It is the set of rules, roles and responsibilities for managing data: who owns which data, how terms are defined, who may change them and how quality is monitored. Without it, even the best BI tool shows numbers nobody trusts.

Which BI tools do you use?

As a Tableau partner we have particular experience with that tool, but we choose the tool based on your needs, existing systems and budget. Well-organized data and clearly defined indicators matter more than the tool.

Is our data ready for AI?

AI is only as reliable as the data it works with. Before introducing an AI solution, we assess the quality, completeness and structure of your data and the rules for managing it, and tell you what needs fixing for the results to be usable.

How long does it take to organize data and implement BI?

It depends on the number of data sources, their quality and the number of reports. We work in phases, so the first reliable reports arrive before the whole model is finished, and you get an exact plan and timeline after the initial analysis.