Modern data management to ensure data quality, consistency and security.
The aim of data management is to integrate data as a valuable resource into business processes, maximising its utilisation potential and ensuring optimal utilisation during ongoing operations. This must go hand in hand with ensuring data consistency across the entire processing chain. A variety of systems and processing steps are involved, from the generation of data to its evaluation, and to achieve the greatest benefit, a wide range of measures must be understood and applied.
The terms data governance, data strategy, and data management are often not clearly defined in companies, leading to a lack of clear communication on these topics. At synvert, data management encompasses data strategy, data governance, as well as other aspects such as data modelling, data quality, master data management, and metadata management.
Maximise the business processes in your company through organised, methodical, and company-specific modern data management.
As a company grows, large volumes of data keep on accumulating, and in order to maintain a uniform, comprehensive overview of such a volume of data, as well as to generate important information, a well-considered data management approach is vital.
Efficient data management not only makes it possible to filter the right data quickly, but also integrates new data in a meaningful way; as a result, employees can quickly access a vast wealth of knowledge and increase process efficiency.
Data management is the foundation of all data-driven analytics, and AI can provide support for critical decision-making. However, AI can only be used meaningfully if a structured, comprehensive data management system is in place.
With a carefully crafted strategy, data assets can accelerate the attainment of business objectives and also generate business value, derived from enhanced decision-making, optimised and automated operations, and potential data monetisation opportunities.
An effective data strategy aligns analytics, data governance, and information architecture, encompassing tools, processes, and rules for handling business data. It outlines how data will be managed, analysed, and processed, encompassing not only decision support through reliable data but also compliance and security considerations.
Your data strategy is shaped by your business strategy and operational business model, but it should not be a one-way process: it is crucial to identify the areas that require change in order to derive benefits from data assets.
Data strategy also considers process optimisation; data should not only be available for analysis, but should also be obtained automatically and seamlessly. Potential conflicts between legal requirements, customer interests, and processes themselves must be determined.
The BI operating model and analytics architecture form the core of the data strategy: a target model is developed based on the assessment of requirements, best practices, market trends, and organisational maturity, leading to scenarios which are evaluated to outline the path towards achieving that goal.
synvert's partnerships with innovative, market-leading software vendors, in combination with a broad base of customer projects spanning three decades, give us comprehensive know-how about data strategies in the BI and data analytics worlds, with their drivers, best practices, and trends. synvert accompanies you on your journey of change and offers you coordinated tools for strategy development.
We follow a comprehensive and neutral approach to evaluating your information and data architecture, then bring our expertise to develop a suitable target architecture and roadmap for your data strategy. Wherever you want to go and whatever you want to do - cloud, more AI, data engineering, self-service BI with storytelling, building a data lake or a data catalogue – we’ll get you there!
synvert works with you to identify achievable and meaningful use cases, in order to ensure that the adoption of new data and technologies can be effectively realised and result in tangible benefits. Our proprietary tools for developing, coordinating, and documenting analytical use cases provide invaluable support in the strategy definition process.
At synvert, we see data as a valuable asset that should benefit the entire organisation. We understand the critical role of data governance and effective data management practices, including data quality, metadata management, master and reference data, information security, content and knowledge management, and their interaction with the overall data strategy. Our synvert Data Governance Framework provides a structured approach to address all these topics.
We fully understand that data strategy is not just an IT issue, but can affect the whole organisation, and thus requires a broader perspective that encompasses change management, communication, organisational transformation and process optimisation. The target data strategy must encompass these aspects to ensure successful adoption.
Take responsibility for your data – and big data comes with big responsibilities! It is essential to ensure that all your data is managed in accordance with its importance to your business, as well as maintaining the right level of data quality. This can be achieved by establishing and implementing policies and processes that align with the criticality and lifecycle of your data.
Data governance is all about creating the necessary structures and processes to enable effective data management.
Every company has unique data governance requirements, and depending on the scope, the starting point of the project, and your company structure, many aspects of a data governance strategy need to be customised.
We provide strategic planning services that include assessing your organisation's data governance maturity and capabilities, offering best practices for stakeholder management, and aligning your data governance strategy with your business goals.
We devise concepts for the integration of data governance tools to measure activities and achievements, and formulate plans for change management, communication, and employee training, all in alignment with your organisation's specific requirements.
Policies, standards and a variety of exemplary workflows for different application purposes (data governance policies, critical data elements, data quality, master data management, etc.).
Our customers benefit from our proven synvert Data Governance Framework to quickly develop a customised data governance solution for their organisation. When designing and implementing a data governance programme, synvert offers its specially developed data governance procedure model, a template providing a basis for data governance components in areas such as strategy, organisation, communication, and change management.
Ideally, every employee in your organisation should be familiar with their role in the lifecycle of a data element and the associated duties. Through a data governance programme, you can establish organisational structures that assign clear roles and responsibilities at each stage of the data lifecycle, ensuring that the goals of the data governance programme are met.
The success of your data governance programme can be directly measured by the quality of your data and the results of your processes. As such, it is essential to support data governance with dedicated tools, such as data catalogues, data quality monitoring applications, workflow tools, and specialised governance platforms, to ensure that your programme is successful in achieving its objectives.
The implementation of new technologies and processes can often pose challenges for companies and their employees, so to guarantee a seamless transition it is crucial to implement effective change management measures throughout the project and beyond. synvert can support you in implementing your data governance programme through workshops, training, communication plans, and other resources, helping you navigate the changes and so ensure a successful adoption of the new technologies and processes.
The key drivers behind modern decentralised architecture concepts are domain-driven design (DDD) and microservice architectures. DDD promotes a decentralised architecture that consists of domains (often IT systems) with clearly defined business application areas (bounded contexts) and visualised dependencies and interactions (context maps) that are readily comprehensible. A uniform, ubiquitous language is used to ensure clear communication. Microservice architectures, which involve methods of decomposition, decoupling, and isolation of individual services, are redefining and revolutionising software development.
Agility and decentralisation are disruptively impacting the architecture of analytics systems. A modern analytics system is expected to possess characteristics such as flexibility, elasticity, automation and self-service, decoupling of individual applications, and real-time capabilities; cloud and container-based architectures offer the optimal frameworks for achieving this.
Traditional data warehouses with data marts based on Inmon or Kimball principles for structured data provide optimal support for clearly defined business use cases using top-down approaches. Data lakes, on the other hand, store partially structured and unstructured data, which are used by data scientists for exploration and development of bottom-up use cases.
Data Mesh applies the concept of decentralised domains with their own data ownership and architecture. Data is generated as a product of a domain through microservices and made available to other domains through self-service data infrastructure. A cross-domain governance organisation provides global decisions and defines the ubiquitous language and domain boundaries.
Data Fabric is a sophisticated data architecture that seamlessly interconnects multiple domains using a variety of technologies and services. Data is exchanged via intelligent metadata-driven pipelines, allowing users to effortlessly access and consume data at will via self-service. AI and ML support data governance, data quality, and data preparation, providing advanced capabilities for effective data management.
synvert supports the development and modernisation of data architectures using diverse technologies and in a wide range of environments, including on-premise, cloud, hybrid, and multi-cloud configurations.
When developing a data architecture, it is important to define clear use cases. synvert supports you in identifying the necessary data from internal and external domains, by choosing the right technologies, and with goal-oriented roadmap planning in project management.
Depending on how the new architecture integrates with your existing systems, a decision must be made on whether to design it on premise, in cloud, or in a hybrid environment. By conducting proof of concepts (PoCs) and pilots, and offering support for tool selection, system integration, and logical architecture development, synvert ensures that your new systems will seamlessly align with your existing ones.
The implementation of data governance supports you in maintaining the standards of your data architecture. A clear distribution of roles and responsibilities ensures business processes running in a standardized, workflow-supported manner and your employees maintain an overview through business glossaries and data catalogs, thus improving their data literacy.
synvert employs generic industry and use case models to provide agile data modelling concepts. Ready-made data marts and data product structures for a wide range of use cases, including data vault and anchor modelling, ensure that your new data architecture adheres to best practices and industry standards.
As you develop your data architecture, data quality and preparation should be prioritised. Defining data quality KPIs, and implementing their measurement and visualisation, are vital in maintaining high levels of data quality. synvert can help you with the design and implementation of automated data pipelines, as well as various integration patterns (CDC, synchronous, asynchronous, bulk, ETL/ELT, streaming, etc.) to ensure that these standards are met.
Raw data without the appropriate processing and analysis is not very useful. In order to gain meaningful information from raw data, it must be processed and analysed accordingly, and data modelling provides the necessary foundation for this purpose.
Today, there are various approaches to data modelling due to the diversity of goals, so it is important to determine the best way to optimise data performance. Should the data pool be flexible and expandable? Is data loading speed a priority, or should queries be executed as quickly as possible? These are all factors that need to be considered for successful data modelling.
Well-structured, well-prepared data is the foundation of a data-driven business. Only with the right foundation can meaningful insights and new perspectives be gained from your company data.
Data Mesh is an approach for data modelling that applies the idea of decentralised domains with their own data ownership and architecture; data is created as a product of one domain and offered to others.
The key to a successful solution is to use the right modelling method or technique depending on the analytical goals. Whether it's multidimensional modelling, star and snowflake modelling, classic 3NF modelling, data vault modelling, anchor modelling, or simple key-value tables, it's important to know where and how to use them effectively.
synvert supports you in choosing the right modelling method for your specific needs; with more than 30 years’ experience in many different customer projects, we have implemented the entire modelling spectrum. Our experts will assist you with the extraction, analysis and evaluation of your data, transforming it into meaningful, profitable data.
synvert approaches modelling tasks from the perspective of the entire analytical platform; while evaluation tools can provide optimisation options, relying solely on one software can be risky. At synvert we understand the importance of the right context to leverage new data, goals, users, and tools.
Using the experience gained from numerous projects, synvert accompanies you on your projects from business requirements analysis to conceptual design, data modelling, and data analytics. The goal determines the path, and we know the tools you need to get there.
synvert has developed standardised process models for individual modelling methods, allowing for the safe transfer of pilot experiences to a wider audience. Combined with organisational approaches such as data stewardship or Data Mesh, data can be transformed into knowledge and advantages.
To understand and trust data, visibility and control over metadata, the data about the data, is crucial. synvert helps you to identify the right tools and processes to derive maximum value from your data, using metadata to provide actionable insights, helping you to understand the function and potential of your data, and empowering you to fully exploit its benefits.
With a data dictionary, you know where your data is all the time, improving security and agility.
Your employees work together and share their knowledge to reduce data silos, improving internal communication and collaboration.
The proper use of metadata assists you in analysing and classifying your data assets. With the aid of artificial intelligence and machine learning, links are established and data is refined with additional metadata automatically.
synvert incorporates metadata management as an integral component of its comprehensive data management strategy, which encompasses data governance, data strategy, data quality, and master data management. Our approach includes creating the requisite structures and processes, utilising the appropriate tools and ensuring compliance with legal requirements such as GDPR (General Data Protection Regulation) and other relevant regulations.
When designing and implementing a data governance strategy, synvert follows its own specially developed data governance procedure model which encompasses governance components in areas such as strategy, organisation, communication and change management, acting as a starting point and a template easily adapted to your specific needs.
The synvert Visual Metadata Layer (sVML) is a tool for visualising metadata maps, aimed at simplifying navigation in a data catalogues and extending it with additional features, such as aggregation. It is compatible with catalogues from various vendors and can calculate aggregations and statistics, graphically represent relationships, and help identify connections between records.
With a wealth of projects under our belt, synvert is a leading consulting company in the fields of metadata management and data cataloguing. Its combination of conceptual expertise and technical proficiency ensures project success.
The volume of stored data has significantly increased over the past decades, making data quality management essential to maintain data integrity and accuracy. This enables organisations to work with a data-driven approach and to extract the maximum value from their data assets. It's not just about collecting data, but also about actively ensuring its quality. In a data warehouse, data from different sources is often merged, which can lead to errors, so the sources themselves need to be checked and prepared.
High-quality data means accuracy, completeness, consistency, timeliness, and validity. When these characteristics are met, organisations can obtain meaningful and valuable information to drive their business success.
Data correctness should always be verified and ensured; merging inconsistent data sources can introduce errors that can be time-consuming and laborious to identify and rectify later. Effective data quality management can save significant time and effort in the long run.
To maintain high data quality, organisations should use commercial tools and automated rules for data storage.
With 30 years of experience in data warehousing, synvert has mastered the entire data quality process. In addition to data quality, metadata and master data management, data catalogues are also part of our comprehensive strategy.
Our experts have the knowledge and experience to set up an efficient data quality management in your organisation, including the development of data quality (DQ) rules, criteria, measurements, scorecards, and dashboards, tailored to your specific needs.
We are well-versed in handling different data quality use cases, ranging from address cleansing of customer data to ensuring quality in IoT data. We understand the unique data quality requirements of various industries, including bond data in retail and sensitive data in healthcare, and can provide industry-specific solutions.
We have a great deal of experience with a variety of commercial tool vendors, such as Informatica, IBM, Oracle, and Talend, as well as open-source products and custom-built data quality solutions. We can leverage this expertise to recommend the best tools and technologies for your data quality management needs.
Master data refers to the fundamental information that represents the core data of a company, including essential details about various aspects of the business, such as partners, finances, products, and locations. Master data serves as the foundation for all corporate processes and functions; it is distinct from transaction and inventory data, which are related but different. Inventory data pertains to operational quantity and value structures, such as an account balance, which can be changed by the transaction-oriented movement of data, such as inflows or outflows in an account. Transaction data relies on assignment to master data for meaningful interpretations.
The implementation of a Master Data Management (MDM) system is one of the most intricate tasks in IT: it involves covering all business processes, from the group level down to individual companies.
Standardising master data elements and establishing effective master data governance and data stewardship practices can significantly simplify the handling of metadata.
The use of complex technologies (real-time, mobile, cloud, etc.) can be a challenge in implementing an MDM system.
We support you right from the start on this challenging journey, leveraging our vast experience across various industries to assist with all data domains; our field-tested utilities provide valuable tools to swiftly and efficiently build your MDM system and establish the necessary organisational structure.
At synvert, we provide support in developing a comprehensive MDM strategy that spans your entire organisation. We work closely with you to define use cases, plan roadmaps, and manage your MDM projects to ensure success.
We design your MDM platform so that it integrates seamlessly with your existing systems, and test its functionality with PoCs and pilots to ensure you can achieve your goals quickly and efficiently.
Drawing on our extensive experience in building data governance systems, we implement your MDM meticulously, defining roles and responsibilities, establishing robust standards and best practices, and designing efficient workflows for acceptance and review processes. Our approach guarantees long-term MDM success.
In workshops, we work with you to identify relevant data domains, and to develop master and reference data models, based on our comprehensive generic industry and data domain models.
We conduct an in-depth analysis of your data landscape to assess the quality of your master data, and based on this analysis, implement effective processes for data cleansing, enrichment, golden record creation, and so on. To ensure the sustained success of your MDM programme, we establish then measure and evaluate data quality KPIs.
For companies operating within the EU, stringent data protection and data security requirements apply, and non-compliance can result in severe penalties. Cloud projects are also subject to laws such as the General Data Protection Regulation (GDPR) and court rulings like Schrems II, which can significantly impact or prematurely terminate such initiatives. Data security requires that data is encrypted, whether in transit or at rest, ensuring confidentiality and integrity. At synvert, we assist you in creating a secure environment for your business data by providing tailored solutions in line with industry best practices, such as OWASP, NIST, and MITRE, for various aspects of data security. We can also help you align with standardisation authorities like NIST and ISO to enhance data security and protection. In addition to prevention, we also provide support in processing and analysing data breaches, as well as identifying and implementing measures to mitigate or eliminate such threats.
Businesses need to have robust data loss protection measures in place, independent of various factors, including location.
It is vitally important to set up access restrictions to sensitive data, as well as to classify business data correctly.
Implementing stringent data encryption standards for both data storage and transmission is critical in reducing the risk of data theft. Data masking and encryption are essential tools in analysing sensitive data while maintaining security; furthermore, providing dashboards to monitor the effectiveness of deployed security measures is crucial for continuous security monitoring.
To begin securing your business data, sensitive data must first be identified then classified according to security levels. synvert supports you in efficiently categorising your data and achieving your security goals.
Once your business data has been categorised, we assist you in conceptualising and implementing appropriate Access Control Lists (ACLs) to ensure maximum control over your data.
A powerful cryptographic Key Management System (KMS) is essential for centrally managing security-related IT processes that are secured with keys. We can guide you through choosing then integrating a KMS into your environment.
Logging and monitoring your system is essential to gain insights into attack surfaces and risk assessment for your environment, enabling you to proactively prevent attacks and safeguard against data theft.
synvert has developed various procedures and algorithms to comply with the requirements of data protection regulations such as DSGVO, HIPAA, GLBA, PCI DSS, CoC and others, without compromising performance.
In the event of a data leak, it is vital to gather a comprehensive overview of the incident, identifying what data was stolen, who the attacker was, and which systems and components were affected. We are ready to help you to find the answers to these questions.
In the event of a data leak, immediate security measures must be taken. synvert can support you in developing further operational measures, as well as in implementing the security updates and patches provided for the affected components.