Why is structured data management essential for companies today?

In an increasingly data-driven economy, it is no longer enough to simply collect information - it must be organized, consolidated and analysed in a targeted manner. This is the only way to improve operational processes and fully exploit innovation potential.

What challenges typically arise in data management?

Many companies struggle with fragmented systems and isolated data storage, which leads to inefficient processes and limited decision-making ability. A central platform can specifically remove these obstacles.

What is the difference between a data lake, data hub and data warehouse?

These three concepts serve different purposes: The data lake processes large volumes of raw data, the data hub acts as a hub for central company data, while the data warehouse provides structured information for evaluations and reports.

How does CANCOM support the introduction of centralized data management?

CANCOM accompanies companies from integration to analysis and adaptation to specific requirements. In addition, the team provides continuous support and helps to get the maximum benefit from data.

To what extent can artificial intelligence improve data management?

By automating repetitive tasks, AI helps to use data more efficiently. It facilitates well-founded decisions and makes it possible to recognize and use complex correlations in real time.

Data Management

Exploiting the potential of data

Realising the potential of data

In today's economy, characterised by Industry 4.0, IoT and new technologies, enormous amounts of data are being generated. Efficient data management is essential in order to fully utilise the benefits of data. Data not only needs to be collected, but also organised, stored and analysed in a structured manner - for example via a data management plan (DMP) or modern data management systems to provide optimal support for business processes.

A DMP ensures that data is managed efficiently and processed in accordance with guidelines, thereby maintaining high data quality and data protection. Data warehouses, data lakes and data management platforms enable advanced analyses and valuable insights.

DMPs also help in research data management - for example at the University of Vienna - to collect data in such a way that it can be reused and combined. Cloud solutions and modern systems now offer companies better opportunities to utilise data efficiently and increase their innovative strength.

The challenge: fragmented data silos

Many companies are faced with the problem that their data is stored in isolated silos without an overarching data management strategy:

Fragmented systems: different solutions often lead to isolated data silos that do not communicate with each other. A centralised data management system that integrates data lakes, data warehouses and cloud solutions can overcome this hurdle and improve IT scalability.

Slowed processes: Lack of access to merged data slows down operational processes and decisions. With AI, automated data management systems and DBAs, manual tasks can be reduced and business processes optimally supported.

Lost potential: Without effective data management and a clear data management plan (DMP), valuable insights remain unutilised. Structured data storage enables big data analyses, research data management and AI applications to be used effectively. At the same time, compliance with data protection guidelines such as Data Protection or the California Consumer Privacy Act is ensured, enabling companies to work efficiently and in compliance with the law.

The solution: Implementation of centralised data management

Comprehensive, centralised data management is required to overcome these challenges:

1. Unified platform

Storage and integration: A centralised platform that stores and links all company data is fundamental.
Visualisation: Clear visualisation of data helps to identify patterns and trends and make informed decisions.

2. Improved decision-making

Predictions and transparency: More accurate forecasts through data analysis make it possible to predict future trends and behaviour.
Measurable processes: Transparent and quantifiable processes increase the effectiveness of business management.

3. Optimisation of processes

Process efficiency: Processes can be continuously improved by analysing integrated data.
Increased innovative strength: Faster recognition of innovation potential through effective utilisation of all available data.

By integrating AI and machine learning, many manual data management tasks can be efficiently automated and made scalable. This enables companies to utilise data efficiently, gain valuable insights and make well-founded business decisions. In today's data-driven economy, it is essential to keep data in its original format and ensure high data quality through master data management and structured data access.

Sophisticated data management not only creates competitive advantages, but also improves the customer experience by providing data in real time and translating it into innovative solutions. With a clear data strategy and a data management plan (DMP), big data, research data management and different types of data can be managed effectively. This enables companies to optimally support internal processes, increase efficiency and successfully master the challenges of today's economy.

CANCOM as your data management partner

With CANCOM , you can not only implement centralised data management in your company, but also gain full control over your data and use it effectively for your business. Our solutions offer

Integration service: complete consolidation and integration of your data landscape.
Analytical tools: Advanced data analysis and visualisation tools.
Customisation: Adaptation of the platform to your specific business needs.
Support and advice: End-to-end support and expert advice to help you optimise the use of your data resources.

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The 3 concepts for centralized data management

In order to manage data uniformly, companies need to consider three concepts that differ in terms of function and area of application: the data warehouse, the data lake and the data hub. While the data warehouse and data lake focus more on collecting and analyzing data, the data hub primarily serves as a mediation and data exchange point.

DATA WAREHOUSE

Business Intelligence

The data warehouse is a central repository for integrated and structured data from two or more different sources. This system is mainly used for reporting and data analysis and is considered a core component of business intelligence applications. Data warehouses implement predefined and repeatable analysis patterns that are distributed to a large number of users in the company.

DATA HUB

Data exchange and data governance

Data hubs serve as a point of contact for core data within a company. They centralize application-relevant company data and enable seamless data exchange between different endpoints. At the same time, they are the main source of trusted data when it comes to data governance initiatives. Data hubs provide master data for enterprise applications and processes. They are also used to connect business applications with analytics structures such as data warehouses and data lakes.

DATA LAKE

Advanced Analysis

The data lake is a repository of all structured and unstructured company data. It hosts unrefined data with limited quality assurance and requires the consumer to process and manually enhance the data. Data lakes create the foundation for data preparation, reporting, visualization, analytics and machine learning.

FAQs

The most frequently asked questions about data management

Why is structured data management essential for companies today?
In an increasingly data-driven economy, it is no longer enough to simply collect information - it must be organized, consolidated and analysed in a targeted manner. This is the only way to improve operational processes and fully exploit innovation potential.
What challenges typically arise in data management?
Many companies struggle with fragmented systems and isolated data storage, which leads to inefficient processes and limited decision-making ability. A central platform can specifically remove these obstacles.
What is the difference between a data lake, data hub and data warehouse?
These three concepts serve different purposes: The data lake processes large volumes of raw data, the data hub acts as a hub for central company data, while the data warehouse provides structured information for evaluations and reports.
How does CANCOM support the introduction of centralized data management?
CANCOM accompanies companies from integration to analysis and adaptation to specific requirements. In addition, the team provides continuous support and helps to get the maximum benefit from data.
To what extent can artificial intelligence improve data management?
By automating repetitive tasks, AI helps to use data more efficiently. It facilitates well-founded decisions and makes it possible to recognize and use complex correlations in real time.

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