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Data Management

Exploiting the potential of data

In today's economy, which is increasingly characterized by Industry 4.0, IoT and new technologies, companies are absorbing enormous amounts of data. Sensors, technologies and machines are continuously generating more data throughout the entire value creation process. To realize the full benefits of this data, effective and efficient data management is essential. It is not enough to simply collect data - it must be systematically organized, stored and evaluated in order to optimally support internal company processes and promote innovation.

A well thought-out data management strategy based on a solid data management plan (DMP) is essential. The plan ensures that data is managed efficiently and according to defined guidelines. This not only guarantees data quality, but also ensures compliance with data protection regulations. By using modern data management systems such as data warehouses and data lakes, companies can fully exploit the potential of their data and automate big data analyses and advanced analyses.

The challenge: fragmented data silos

However, many companies are faced with the problem that their data is stored in isolated silos without an overarching strategy for data storage and management:

  • Fragmented systems: different solutions tailored to individual requirements lead to isolated data silos. These systems often do not communicate with each other, which hinders data access and integration. A centralized data management system that integrates solutions such as data lakes or data warehouses can overcome this obstacle.
  • Slowed processes: The lack of access to merged and organized data slows down operational processes and decision making. Companies need to be able to use their data efficiently to accelerate data-driven business decisions and optimize their operations. By using AI and automated data management systems, many manual tasks can be optimized, which significantly increases efficiency.
  • Lost potential: Without effective data management and a clear data strategy, important insights from the data remain unused. The full potential of data use is not exploited, which hinders innovation and limits competitiveness. With structured data storage and management, data can be collected and made available in such a way that it can be used at any time for big data analyses and the further development of AI-based solutions.

The solution: Implementing centralized data management

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

1. Unified platform

  • Storage and integration: A central platform that stores and links all company data is fundamental.
  • Visualization: Clear visualization of data helps to identify patterns and trends and make informed decisions.

2. Improved decision making

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

3. Optimization of processes

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

By integrating AI and machine learning, many manual tasks in data management can be optimized and made scalable. These technologies make it possible to use data efficiently and make better, more informed business decisions. Especially in today's data-driven economy, it is becoming essential for companies to preserve their data in its original format while ensuring higher data quality through master data management and structured data access.

Sophisticated data management not only delivers clear competitive advantages, but also improves the customer experience by providing data in real time and turning it into innovative solutions. This paves the way for a data-driven, future-proof company that effectively masters the challenges of the modern economy.

CANCOM as your data management partner

With CANCOM , you can not only implement centralized 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 visualization tools.
  • Customization: Adaptation of the platform to your specific business needs.
  • Support and advice: End-to-end support and expert advice on how to make the best 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.

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