Data management systems in change: Strategies for the company's success in the Age of AI

Data management: Key to competitiveness in the digital doctor

In today's business world, which is characterized by digitization and exponentially growing data, data management has developed from a purely technical task to a strategic necessity. Data is no longer just a by -product of business processes, but the life elixir of modern companies. They are the basis for sound decisions, operational efficiency, innovation and competitiveness. Effective data management is therefore a crucial success factor.

What are data management systems (DMS)?

Data management includes the entire life cycle of data within a company: from recording and organization to storage, protection and validation to processing, analysis and final archiving or deletion.

Data management systems (DMS) are the technological tools and platforms that enable and control these complex processes. The term “DMS” is often widely grasped and can include a variety of system categories:

Master Data Management (MDM)

Solutions for the administration of central master data (e.g. customers, products, suppliers). MDM systems ensure that this data is consistent, correct and complete, which forms the basis for reliable analyzes and operational processes.

Customer Data Platforms (CDP)

Platforms that merge customer data from various sources (e.g. CRM, marketing automation, web analytics) and enable a uniform view of the customer. CDPs are mainly used for marketing, sales and customer service to enable personalized experiences and targeted campaigns.

Enterprise Content Management (ECM)

Systems for the management of unstructured documents and content (e.g. contracts, invoices, emails). ECM systems facilitate the search, approval and archiving of documents and contribute to compliance with compliance requirements. In the German -speaking world, these are often simply referred to as DMS.

Business Intelligence (BI)

Platforms for the analysis and visualization of data to support decision -making. BI systems make it possible to recognize trends, uncover patterns and monitor the company's performance.

Cloud database management systems (DBMS)

Databases that are operated in the cloud and offer scalability, flexibility and cost efficiency. Cloud databases are often used for analytical purposes because they process large amounts of data and can quickly answer complex queries.

Suitable for:

• Outdated IT systems: a stumbling block on the way to artificial intelligence

Why is effective data management indispensable?

Strategic and effective data management is essential for the success of modern companies for several reasons:

Foundation for operational processes

Every application, analysis and every algorithm in a company rely on the seamless access to high -quality data. Without a solid data basis, business processes cannot be efficient and digital initiatives fail. Data management forms the foundation on which operational excellence is built up. An example: A producing company requires precise and current data on inventory, production plans and delivery times to optimally control its production processes and avoid bottlenecks.

Basis for well -founded decisions

Data forms the basis for well -founded and comprehensible business decisions. By analyzing patterns and trends in well -managed data, companies can make better strategic course. A high data quality, ensured by DMS, leads directly to more precise analyzes, more precise forecasts and ultimately faster and better decisions. Converted data is thus transformed into valuable findings that create competitive advantages. An example: With the help of data analyzes, a retail company can better understand the buying behavior of its customers and optimize its range, its marketing campaigns and its branch locations accordingly.

Increase efficiency and productivity

Effective data management optimizes business processes, saves valuable time and reduces the need for resources. Conversely, defective data management leads to considerable loss of productivity. A study showed that employees in Germany spend an average of two hours a day looking for data, which reduces efficiency by 18 percent. Companies that have implemented intelligent data management report on cost reductions and productivity increases. Automation, a core component of modern DMS, reduces manual interventions and thus sources of error. An example: An insurance company can use automated processes to edit damage faster and make payouts faster, which increases customer satisfaction and lowers operating costs.

Ensuring data security and compliance

In a time of increasing cyber threats and stricter data protection regulations, the protection of corporate data is of existential importance. DMS play a central role in securing data against unauthorized access, loss or theft. At the same time, they are essential for compliance with legal and industry-specific regulations such as the General Data Protection Regulation (GDPR). Data governance, i.e. the determination of guidelines and responsibilities for dealing with data, is an integral part of data management and is supported by DMS functions. The non -compliance of regulations can lead to sensitive punishments and considerable reputation damage. An example: A financial service provider must ensure that customer data is protected in accordance with the applicable data protection regulations and that transactions are transparent and understandable to prevent money laundering and fraud.

Suitable for:

• AI systems, high-risk systems and the AI ​​Act for practice in companies and authorities

Support of digital transformation and innovation

Data is often referred to as the “elixir of life” of the digital transformation. Future -based technologies such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT) and advanced analysis require huge amounts of current, exact and secure data in order to be able to develop their full potential. Effective data management creates the necessary basis for these technologies. In addition, it enables the development of new, data -driven business models and innovations by enabling companies to capitalize on their data. An example: An automobile manufacturer can use data analyzes to analyze the behavior of its vehicles in real use and use these findings to develop new functions and services, such as personalized driver assistance systems or forward -looking maintenance.

The costs of neglect

The neglect of data management has noticeable negative consequences. According to the Experian, poor data quality is due to the costs of an average of 15 percent of the sales of companies. Outdated data management solutions (“Legacy Systems”) bind valuable IT resources for maintenance and troubleshooting and prevent companies from pulling the full value out of their data. In addition, such systems increase susceptibility to risks, from dissatisfied customers to serious security violations. The complexity and the high manual effort in outdated systems lead to inefficiency and hinder the agility of the company.

Market leader in data management systems

The selection of the right DMS solution is crucial for the success of a company. However, the market is dynamic and fragmented, which makes the decision difficult. There are a variety of providers that differ in terms of functionality, technology, price and target group.

In the following, some of the leading providers are presented in the field of data management systems, whereby the focus is on their market position, their strengths and their unique selling points:

Computer

A leading provider in the area of ​​MDM, data integration, governance and quality. Informatica uses a AI -controlled approach to improve data accuracy and consistency. The company is viewed as a comprehensive platform provider and achieves high user ratings. According to Forrester, users report a 70%improvement in data quality.

Microsoft

A strong cloud provider with a broad portfolio that includes Azure Data Factory for data integration and orchestration, power BI as a leading analytics/BI platform, Sharepoint for document and content management as well as SQL Server (incl. SSRS) for database management and reporting. Microsoft's strength lies in deep integration within the Azure ecosystem. Azure Data Factory users report 60% faster data processing.

SAP

Dominant in the enterprise segment, especially when integrating with SAP ERP/S/4HANA. SAP offers SAP MDG for master data, SAP DATA Services for data integration and transformation as well as SAP business objects for BI. The focus is on operational efficiency and seamless integration with other SAP products. SAP Data Services users report 25% efficiency increase in data processing.

Salesforce

Leading in the CRM area and expanding heavily into data platforms. Salesforce Data Cloud as CDP integrates AI with CRM data. Tableau is a top solution for BI and data visualization. Salesforce has a strong focus on improving customer interaction and is often highly rated in CDP analyzes.

Oracle

Offers robust tools for data integration, quality and MDM. The Autonomous Database reduces administrative effort and improves security through automation. Cloud solutions offer flexibility and scalability. According to IDC, users experience a 40%increase in surgical efficiency. Oracle is considered a comprehensive platform provider.

IBM

Comprehensive suite for data integration, quality and government. Infosphere MDM is highly rated by users. IBM offers strong analysis skills and integration with other IBM products and the Watson AI platform. It is reported by a 30%acceleration of data -controlled decisions. IBM is classified as a platform provider.

Snowflake

A cloud native data platform, known for high performance and scalability. Snowflake supports data integration, data warehousing and analysis. The unique architecture separates storage and computing power, which optimizes costs and performance. A Barc study resulted in a 50%reduction in query processing times for users. Snowflake often serves as the basis for newer, “composable” CDP architectures.

Semarchy

Highly rated MDM solution, awarded by Gartner as “Customers' Choice 2024”. Semarchy specializes in data integration and MDM with a uniform platform for efficient data management.

Stibo Systems

Established MDM provider that enables data transparency. The solutions form the backbone for companies that want to draw strategic value from their master data.

Enaio

In German tests Top-rated DMS/ECM system. Enaio offers a modular ECM solution for document management, import, indexing and revision-proof storage. The solution is suitable for different company sizes and specific industries such as pharmaceutical or medicine.

Platform vs. Best-of-Breed

When choosing a DMS, companies are faced with a strategic decision regarding architecture. The market shows a tension between two main sentences: integrated platforms and specialized “best-of-breed” solutions.

Large providers such as Informatica, IBM, Oracle and SAP offer extensive platforms that combine a wide range of data management functions (such as MDM, data quality, integration, cataloging). The advantage is the potentially simpler integration and a single contact, but these platforms are often more expensive and can bind companies more to a provider.

This is opposed to “Pure Play” providers who focus on specific areas such as MDM or data integration. These solutions can often be more flexible and inexpensive, but may require more integration effort.

A recent development that breaks out this dichotomy is the “Composable Architecture”, especially in the CDP area. This approach relies on not to save data yourself, but to activate directly in existing data warehouses. This offers maximum flexibility and uses existing infrastructure, but requires corresponding data Warehouse capacities and know-how.

The choice between the platform, best-of-breed or composable depends heavily on the existing IT landscape, internal skills, the budget and the strategic prioritization of integration depth versus flexibility.

Ki-Gamechanger: The most flexible AI platform-tailor-made solutions that reduce costs, improve their decisions and increase efficiency

Independent AI platform: Integrates all relevant company data sources

• This AI platform interacts with all specific data sources
  • From SAP, Microsoft, Jira, Confluence, Salesforce, Zoom, Dropbox and many other data management systems
• Fast AI integration: tailor-made AI solutions for companies in hours or days instead of months
• Flexible infrastructure: cloud-based or hosting in your own data center (Germany, Europe, free choice of location)

• Highest data security: Use in law firms is the safe evidence
• Use across a wide variety of company data sources
• Choice of your own or various AI models (DE, EU, USA, CN)

Challenges that our AI platform solves

• A lack of accuracy of conventional AI solutions
• Data protection and secure management of sensitive data
• High costs and complexity of individual AI development
• Lack of qualified AI
• Integration of AI into existing IT systems

Future trends in data management

The area of ​​data management is subject to constant change, driven by technological progress and changing business requirements. The following trends significantly shape the future:

Cloud Dominance

The trend towards cloud-based data management solutions is unmistakable and continues. Cloud platforms offer decisive advantages such as scalability, flexibility and cost efficiency. Companies are increasingly relying on multi-cloud strategies to avoid dependencies, optimize costs, increase reliability and to select the best available services for specific tasks. At the same time, hybrid cloud platforms retain their importance, especially in heavily regulated industries.

Handling Volume and Variety

The amount of data generated worldwide continues to explode exponentially. This data is also extremely diverse and includes structured, unstructured and semi-structured formats from a wide variety of sources. Traditional data warehouses reach their limits here. Therefore, architectures such as Data Lakes and Data Lakehouses become more important. Data lakes can save huge amounts of raw data from various formats. Data lakehouses try to combine the flexibility of data lakes with the structuring and management skills of data warehouses in order to create a uniform platform for storage, processing, analytics and machine learning.

Increasing Velocity

The speed at which data can be processed and analyzed becomes a decisive competitive factor. The trend is clear from traditional batch processing towards real-time processing of data streams (stream processing). This enables companies to react directly to events, to make well -founded decisions at the moment of what is happening, to improve customer experiences through immediate personalization and to proactively recognize and solve problems.

Architectural Shifts

In order to master the complexity of distributed data landscapes, new architectural concepts are established:
Data Fabric: A Data Fabric is an architecture that aims to intelligently combine disparate data sources, applications and systems in order to enable a uniform, consistent view of all company data, regardless of where they are stored. It is said to break down data silos, simplify data integration and improve the data governance.
Data Mesh: In contrast to the rather centralized perspective of the Data Fabric, the Data Mesh pursues a decentralized approach. Here the responsibility for data products is distributed to specific business areas (domains). Each domain manages your own data and provides you with other areas via defined interfaces. The aim is to increase the agility, scalability and speed of gaining knowledge by solving monolithic, centralized data teams and data lakes.

Automation and AI integration

The integration of artificial intelligence (AI) and machine learning (ML) is one of the overarching and most important trends in data management. AI is increasingly used to automate tasks in all phases of the data life cycle, from data integration and quality check to governance to analysis and even schemad design. “Augmented Analytics”, in which AI supports human analysts in data preparation and knowledge acquisition, is also becoming more important.

Heightened Focus on Data Governance, Quality, Security, and Privacy

With the increasing strategic importance of data and its distribution over various environments, the need to ensure their quality, security and compliance. Important developments in this area are automated data governance, data observability, improved security measures, robust data protection frameworks, data quality as a priority and dataops.

AI integration: transformation of data management

The integration of artificial intelligence (AI) in data management systems is no longer a futuristic vision, but is becoming a fundamental strategic necessity for companies that want to remain competitive in the digital age. In view of the exploding amounts of data, the increasing speed of data production and the growing variety of data formats, AI is essential to manage this complexity and effectively manage data.

AI transforms data management from an often reactive, manually shaped process to a proactive, highly automated system. It is the key to open up the full value from the databases of a company and to establish a really data -controlled culture of decision -making and innovation. Companies that strategically use in data management get significant advantages.

Suitable for:

• KI, the hut is on fire! The AI ​​age is here and how important is the human factor? 20x more important for marketing and retail in the AI ​​age?

AI-based improvements

KI offers concrete improvements in central areas of data management:

Improved data quality

AI algorithms can automatically recognize and correct errors, inconsistencies and duplicates in large data records, which significantly improves data quality. Machine learning (ML) identifies anomalies and outliers that indicate quality problems. Stand up AI-based tools automatically. In particular, generative KI (Genai) can automate and improve the creation and annotation of metadata and data origin (lineage), which is crucial for the evaluation and ensuring of data quality.

Improved data organization and integration

AI automates time -consuming tasks such as mapping of data fields between different systems, comparing schemes and transformation of data formats. AI systems can understand the structure and semantics of data from different sources and thus facilitate integration. AI-based data modeling and automated schema design help to organize data logically and efficiently. AI also plays an important role in the integration of structured and unstructured data, which is essential for modern analyzes and genai applications.

Deeper and faster insights

In a short time, AI can extract valuable insights from huge amounts of data that would be difficult or not at all for human analysts. It reveals hidden patterns and correlations and enables more precise predictions and forecasts. AI also automates the creation of reports and visualizations, which makes knowledge more available and understand more quickly. Augmented analytics tools use AI to support human analysts in their work and increase their productivity.

Automated data governance and compliance

AI automates the identification and classification of sensitive or personal data, which is essential for compliance with data protection regulations such as the GDPR. It can monitor data access and use patterns in order to recognize potential guideline violations or security violations at an early stage and trigger alarms. AI supports the establishment and enforcement of data governance frameworks and helps to manage compliance requirements. Genai can improve compliance monitoring and document management based on metadata and lineage by automatic tagging based on metadata and lineage.

Surgical advantages

The automation of routine tasks by AI in data management offers significant operational advantages, especially with regard to personnel resources:

Combating personnel lack

AI can take on repetitive, time -consuming tasks for which often difficult to find staff or who are considered unattractive. This helps to bridge a shortage of skilled workers and qualification gaps.

Reduction of low -value work

Employees often spend a lot of time with low -threshold tasks such as data search or manual data entry and correction. AI can reduce or eliminate these activities.

Focus on employees on strategic tasks

The automation of routine work relieves employees of monotonous tasks and can concentrate on higher -quality, strategic activities that require human judgment, creativity and empathy.

Improvement of efficiency and reduction in costs

Automation leads to an increase in surgical efficiency and lowers costs caused by manual work and human errors.

Strengthening employees

The integration of AI into data management not only relieves the company operatively, but also strengthens the employees:

Elimination of tedious tasks

AI takes on tasks such as data extraction, adjustment, transformation, standard reporting, email sorting or scheduling.

Increased focus and job satisfaction

Employees recover time and mental capacities that they can use for more demanding problem solutions, creative tasks, strategic planning and interaction with customers. This can increase job satisfaction because less time is spent with monotonous work.

Data democratization

AI-based analysis tools, self-service platforms and low code/no-code solutions also allow employees to access data, analyze them and gain knowledge without profound technical knowledge. This promotes broader data -controlled culture in the company.

Acceleration of business processes

The integration of AI into data management-supported processes accelerates processes in almost all areas of the company:

Sales & marketing

AI can automatically evaluate and prioritize leads, pronounce personalized product recommendations, adapt prices dynamically, automate marketing campaign releases and analyze customer moods from texts.

Customer service

AI chatbots take over the initial processing of inquiries, tickets are automatically categorized and forwarded to the right processors, and Ki suggests suitable answers for frequent questions.

Finance & procurement

Invoices can be read out and processed automatically, the entire Procure-to-Pay process can be automated, and AI supports the risk assessment and credit check.

Hr

CVs can be scanned and evaluated automatically, and workflows for the onboarding and offboarding of employees can be automated.

Operations

AI optimizes the warehouse management through demand forecasts, supports the supply chain planning and enables forward -looking maintenance (predictive maintenance) of machines.

Suitable for:

• Too many goals and objectives in product management: sources of error and innovative approaches to optimization – with AI and SMarket

Strategic recommendations for AI-based data management

In order to successfully use the transformative power of AI in data management, companies should pursue a strategic approach:

Building a AI-capable data basis

The basis for every successful AI initiative is high-quality and well-managed data. Companies should therefore prioritize data quality and data governance, invest in modern data architectures, focus on data integration and determine clear responsibilities.

Selection of suitable AI-capable DMS solutions

Choosing the right technology is crucial. Companies should specifically evaluate potential DMS providers according to their integrated AI skills, which are relevant for their specific requirements, take into account the architectural fit, ensure seamless integration and evaluate user-friendliness and democratization.

Overcoming implementation hurdles

The introduction of AI-supported data management is often associated with challenges. Companies have to deal with data challenges, build up specialist knowledge and know-how, plan costs and resources and promote trust and change management.

Start small, scale quickly

The complete switch to AI-driven data management can be a huge task. A more pragmatic and often more successful approach is to start targeted and scalate gradually. Identify specific business processes that are currently being slowed down by manual data processing or have high error quotas. Concentrate on achieving improvements in these areas by using AI quickly and a clear ROI.

AI strategies that make companies sustainable

The analysis illustrates the inseparable connection between robust data management, the strategic integration of artificial intelligence and the sustainable business success in today's digital economy. Effective data management is the essential basis on which companies have to build in order to fully exploit the potential of AI. The future belongs to the organizations that understand data as strategic capital and use artificial intelligence to intelligently manage and activate this capital. The implementation of a AI-driven data management strategy is therefore no longer an optional step, but a decisive course for future success.

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