5 Data Management Challenges with Solutions

This report provides an overview of the challenges that arise in data management and the solutions that can help overcome these challenges.

5 Data Management Challenges with Solutions
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As digitization and digitalization bring forth paradigm shifts to how businesses operate, proper data management ensures the accessibility and availability of accurate data that can be analyzed for generating valuable insights. These insights are critical for companies to understand the market behavior and customer trends that further help drive better decision-making.


Challenges of Data Management 


With exponential growth in enterprise data, traditional methods of handling huge data volume quickly become inefficient. Organizations face a challenge in aggregating, maintaining, and generating value from their vast volumes of data. Among the most prominent hurdles that arise is the creation of multiple data repositories or data silos, leading to issues in the identification and management of data. The lack of skilled resources and poor understanding of data handling from disparate sources also acts as a bottleneck towards taking better data management initiatives. Without proper data management, businesses may fail to ensure the security and privacy of data, which can lead to financial and potential legal consequences.


1. Growing Data Silos Amongst Multifaceted Distributed Systems


Data silos occur when data is stored in separate and disconnected systems, departments, or business units, making it difficult to access, share, and integrate. As organizations collect more data from various sources, data silos can grow, creating challenges for data integration, analytics, and decision-making.

Data integration complexities arise when attempting to combine and synchronize data from multiple sources, especially when dealing with disparate data formats, inconsistent data quality, and varying data structures. Integration complexities can make it challenging to establish a single source of truth and derive accurate insights from the data.

To overcome these challenges, organizations must adopt a comprehensive data integration strategy that includes data governance, quality management, and integration tools and technologies.


2. Inherent Complexity of Data Architecture


Modern data strategies can only be implemented by evolving the existing data architectures. The inherent complexity of such architectures necessitates an overhaul of the associated underlying technologies, which can be an expensive and time-intensive undertaking. Furthermore, if legacy systems are not updated to meet modern-day business needs, data silos can become challenging to integrate, causing insufficient or inaccurate information to be shared across business units. Data architectures, thus, need to be flexible and adaptable to modern demands to ensure seamless and continuous data analysis, which further fuels innovation. 

Most businesses still practice the waterfall approach to data sharing. However, a flexible data architecture must ensure seamless inter-department collaboration over data. This can be achieved by adopting DataOps principles within a data management strategy, as DataOps focuses on automating the seamless flow of data within an organization. In addition to flexibility, DataOps introduces the much-needed agility to rapidly scale data pipelines in alignment with the organization’s growth.


3. Lack of Data Governance and Compliance


Organizations must implement proper data governance practices to enable data integrity and usability with easy accessibility. This ensures that data is trustworthy, flows seamlessly, and is protected from misuse. However, while implementing data governance, organizations must circumvent issues such as lack of skilled data governance leadership and resources, poor quality of data, and absence of control over enterprise data.

Data governance and compliance go hand in hand. Lapses in data governance practices and policies can lead to regulatory compliance infringements. Regulatory bodies can impose fines and penalties depending on the nature and severity of the infringement and the local governing laws and regulations. Such violations can attract a penalty of up to €20 million or 4% of the annual gross revenue of the previous financial year (whichever is greater). In addition, businesses can be subject to one or multiple lawsuits in instances of data misuse or breaches by the affected parties. In severe cases, the operational workflow can be disrupted for extended periods when increased regulatory scrutiny is imposed.

To address data governance and compliance challenges, organizations need to establish clear policies and procedures, assign responsibilities, train employees, and implement appropriate technical and organizational controls. They also need to regularly monitor and audit their data practices to ensure ongoing compliance and continuous improvement.


4. Risk of Data Security and Privacy


Large data volume presents a critical challenge for businesses to keep sensitive data and personal identifiable information (PII) private. Furthermore, as cyberattacks and data breaches become more prevalent, the costs of investing and updating old security tools and infrastructure rise. 

According to Check Point Research (CPR), cybersecurity attacks increased by 28% by the third quarter of 2022 compared to 2021, indicating an increased potential in vulnerability to data breaches and misuse. With a rapid rise in business infrastructures adopting a digital model, strong security measures and practices must be deployed to safeguard data privacy and ensure its integrity. Under the umbrella of data management strategy, physical and digital access to data must be strictly enforced via authentication. In the advent of a system failure, data corruption, or unforeseen disasters, proper recovery measures must also be in place to recover data. 

Businesses must invest in data collection and categorization tools to segregate data that falls under regulatory obligations. Organizations must create stringent policies and procedures for managing data, including data retention, data quality, and data access. The solution should incorporate categorizing data based on its sensitivity and applying appropriate security controls based on the data's classification. Continuous risk assessments must be undertaken as necessitated to identify potential risks to data security and privacy and implement measures to mitigate those risks.


5. The Sophisticated Nature of the Data Ecosystem


Data ecosystems, which refer to the interconnected network of data sources, tools, technologies, and stakeholders, face several challenges. For example, ensuring data interoperability between different systems and platforms can be challenging. It requires establishing common data standards, protocols, and interfaces. Moreover, extracting meaningful insights from the data is a significant challenge in data ecosystems. It requires the use of advanced analytics tools, techniques, and skills to derive valuable insights from complex and large datasets.

Addressing these challenges requires a collaborative effort from all stakeholders involved in the data ecosystem. Organizations must adopt a holistic approach to data management, focusing on data quality, integration, privacy, security, governance, scalability, and analysis. This requires a combination of technological solutions, such as data integration platforms, data analytics tools, and security software, as well as organizational change, including process improvements, training, and communication.

Integrating natural language processing (NLP) and machine learning (ML) models can enrich data ecosystems. NLP, combined with process automation, can be used to convert raw and unstructured data to structured data. On the other hand, ML models can aid in evolving the operational logic within a data ecosystem and reduce the burden of human intervention to a minimum.




Data is aptly called digital gold, and organizations' ability to swiftly and efficiently handle large amounts of data for analysis and value generation is critical to business agility and scalability. An effective data management strategy ensures improved efficiency and productivity, better and calculated business decisions, reduced costs, and enhanced data security. The net result is improved customer experience. 

The importance of understanding and making efforts toward data management will continue to grow. Connecting data at all levels will remain paramount as data management evolves to cater to more complex business needs. This will further drive innovation in the data management space by adopting artificial intelligence at the forefront. 
Dietmar Rietsch is CEO of Pimcore. A serial entrepreneur with a strong sense for innovation, technology and digital transformation. He is a passionate entrepreneur who has been designing and realizing exciting digital projects for more than 20 years.