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Benefits of the axxessio Group

Data Management

The systematic organization, management and maintenance of data within a company to maximize their efficiency and use.

Data Quality

The assessment of the condition of data in terms of their accuracy, consistency,
timeliness, completeness and reliability, to ensure that they meet the requirements and objectives of a company.

Data-Driven Decisions

The process of decision-making based on a thorough analysis and evaluation of relevant data to enable informed, objective and effective decisions in companies.

Enterprise Search

A comprehensive search solution that enables the efficient and fast findability of information across various data sources and systems within a company.

Why companies use data management

In our digitally networked world, data has gained immeasurable value. Data management, the structured approach to collecting, storing, using and securing data, has become an indispensable part of modern business strategies. Effective data management ensures that companies can optimally use their data and generate real business value from it.

Companies often access data from different, decentralized sources and store it in diverse systems. The decision about which data to consolidate and unite, which to duplicate and distribute - for reasons of fail-safe operation or parallel access from different regions, for example - which data to protect particularly and which to make easily accessible to a wide range of users, as well as which to retain in the long term and which to regularly clean up, is a very individual, company-specific decision, so 'optimal data management' means something different for everyone.

Data is the heart of every organization, and its quality is crucial for business success. Data quality generally refers to ensuring that the data is accurate, current, complete and consistent. This also means that duplicates, errors and inconsistencies must be identified and corrected regularly. High data quality enables companies to make informed decisions, serve customers better, and ensure more efficient operations.

The regulatory landscape, particularly in the area of data protection, is becoming increasingly complex. Companies are under constant pressure to comply with requirements such as the GDPR in the EU. Well-structured data management ensures that data is stored, processed and transmitted in accordance with the applicable legal regulations. This minimizes the risk of legal consequences and protects the company's reputation.

Repetitive, manual data processes can be time-consuming and prone to error. By automating these processes as part of a data management system, companies can not only save time and costs, but also reduce human errors. In addition, it enables companies to free up valuable resources for more strategic initiatives, rather than focusing on routine data maintenance.

Main Focuses in Data Management

  1. Data Integration
    With the rise of various data sources - from CRM systems to social media to IoT devices - integration becomes a central issue. Data integration is about consolidating data from different sources and providing it in a unified, coherent structure. This allows a 360-degree view of business information and facilitates complex analyses and reporting.

  2. Data Archiving
    Over time, companies generate enormous amounts of data, not all of which are constantly needed. Data archiving refers to the process of securely and efficiently storing data that is no longer regularly accessed but needs to be retained for legal or business reasons. An effective archiving system ensures that this data is easily accessible when needed while maintaining the integrity and security of the data.

  3. Data Migration
    Whether due to the introduction of new systems, mergers or other business changes - data often has to be transferred from one place or format to another. Data migration deals with the process of transferring this data, ensuring that the data is not lost or impaired during the transition and that it works correctly in the new system. A successful migration process considers both technical and business requirements to minimize downtime and ensure business continuity.
  4. Master Data Management (MDM)
    Master Data Management refers to the comprehensive management of central business data, referred to as "master data". These are fundamental, non-volatile data that is used across various business areas and systems. MDM aims to ensure that these key data, such as customer information, product master data or supplier information, are consistent, accurate and up-to-date.

    The goal of MDM is to minimize data inconsistencies and redundancies to provide a unified and reliable source of information for enterprise-wide processes. This enables better decision-making, increased efficiency and the assurance of data quality. MDM includes activities such as identifying, consolidating, quality assurance and distribution of master data throughout the company.

Key aspects in data management: Consistency, validation and profiling

Data Consistency: In complex enterprise environments, it is crucial that data is consistent, i.e., that information is the same and free of contradictions across all platforms and systems, to prevent confusion, reduce errors and enable seamless data integration.

Data Validation: Data validation is an ongoing process in which data is checked against specific criteria to ensure its accuracy and relevance. This process helps companies detect inaccuracies early and correct them to ensure consistently high data quality.

Data Profiling: Data profiling refers to the detailed process of examining data to understand its structure, content, and quality. By generating metadata, insights into data patterns, anomalies, and relationships are gained to quickly detect quality issues and serve as a starting point for data cleaning initiatives.

The importance of data management for companies

Building trust: In a time when data leaks and breaches are becoming increasingly common, trust in data security is of crucial importance. When customers and stakeholders know that a company invests in the quality and security of its data, this boosts trust and the credibility of the company.

Efficiency enhancement: High-quality data enables companies to use their resources and efforts in a targeted manner. Whether it's launching targeted marketing campaigns, efficiently managing sales, or optimizing the supply chain - accurate and up-to-date data enables companies to streamline processes and optimally use resources, leading to a significant increase in efficiency.

Mitigation of business risks: Wrong decisions based on faulty or outdated data can be costly for companies - both financially and in terms of reputation. By ensuring high data quality standards, companies can minimize such wrong decisions. In addition, solid data management allows companies to identify potential risks and threats early on and take proactive measures to mitigate these risks.

We help you improve data quality

Continuous monitoring: Data quality requires ongoing attention. Implementation of processes and tools allows for regular checks to quickly identify and correct discrepancies or errors.

Stakeholder involvement: Involving all relevant stakeholders, including business areas, IT and other groups, ensures that data requirements and expectations are taken into account. This not only promotes data quality, but also acceptance and use throughout the company.

Beware of outdated data: In a rapidly changing business world, companies must be careful to use old data. Processes should ensure that data is regularly updated and outdated information is removed or archived.

Clear responsibilities: The clear assignment of responsibilities ensures that the maintenance and monitoring of data quality is not neglected. There are clear contacts for questions or problems regarding the data.

Data-driven decisions in modern companies

In today's data-driven economic landscape, data-enabled decisions are not just a wish, but a necessity. The ability to make informed decisions based on data and analysis enables companies to work more efficiently, minimize risks and identify growth opportunities.


Risk Mitigation

In our dynamic business world, companies are constantly exposed to risks - be it due to market volatility, competitive pressure or technological disruptions. Data provide an objective perspective and enable companies to recognize patterns, anomalies and trends in their industry or market segment. A deep understanding of these data enables companies to identify potential sources of danger or risks early on, take preventive measures and develop proactive strategies to effectively mitigate these risks. This increases business resilience and ensures survival.


Efficient resource utilization

Every company, regardless of size or industry, has limited resources - be it time, capital or labor. Data-driven decisions enable companies to use these resources optimally. By analyzing performance data, inefficient processes can be identified, cost centers isolated and areas with high growth potential highlighted. The result is optimized operations management, where resources are channeled where they have the greatest impact and ROI.


Competitive Advantage

In a densely populated market environment, data-driven decisions are crucial. Companies that effectively use and analyze data have a clear advantage over their competitors. They are able to quickly adapt to market changes, identify new market gaps or opportunities, and align their offerings accordingly. While other companies may rely on traditional wisdom or intuition, data-driven companies are more agile and can respond faster to market demands.


Customer orientation

Companies that understand their customers and respond to their needs are more successful. Data allows for a deep understanding of customer behavior, preferences, and needs. This can range from purchasing habits to feedback patterns. With these insights, companies can tailor their products, services, and marketing strategies to provide a personalized customer experience. A satisfied customer is a loyal customer and significantly contributes to business success.

Enterprise Search

Efficient information gathering for time savings and productivity

Time savings: In complex enterprise environments where data is distributed across various systems, Enterprise Search allows for centralized access, enabling employees to find needed information in seconds. This significantly reduces time expenditure and allows focus on more important tasks.

Improved productivity: Enterprise Search accelerates and simplifies the search process, allowing employees to complete their work more efficiently. This leads to an increase in productivity by using valuable hours that could be spent on data searches for other business-critical tasks.

Efficient decision-making: By providing instant access to relevant data, Enterprise Search enables data-driven decisions, reduces the risk of wrong decisions and contributes to efficient decision-making, especially in situations that require quick reactions.

Optimization of customer service: For customer service, an important differentiator, Enterprise Search provides quick access to all relevant customer information. This not only shortens response times but also enables better, personalized service.

Efficient data access and enhancement of company performance

Enterprise Search enables unified access to distributed data in large companies, simplifies information access, and saves valuable time. It also creates transparency in data stocks and promotes a better understanding of the existing data. The system serves as a catalyst for business intelligence by enabling companies to quickly access relevant data and gain important insights. Last but not least, Enterprise Search contributes to employee satisfaction by increasing efficiency and making everyday work more pleasant.

Key aspects of Enterprise Search

The integration of different data sources is crucial to ensure consistent search results across different platforms. Relevance and ranking optimize the display of relevant information through machine learning. High scalability is important to keep pace with the data growth of companies. User-friendliness is ensured by an intuitive interface and convenient features such as auto-completion.