Last week it was made public that the personal information of 33 million French citizens could be exposed after two French health insurance operators suffered a data breach early February. Unfortunately, this isn’t an isolated incident. Other recent breaches occurred at i.e. EuroParcs, Air France KLM, JD Sports, T-Mobile, Sony, Cloudflare, …

Cybersecurity goes beyond network, application and endpoint security. Especially in today’s digital age, where data is the lifeblood of organizations, safeguarding sensitive information has become paramount. As organizations amass vast amounts of data, protecting it from unauthorized access, breaches, and misuse has become a complex challenge. In this context, implementing robust lines of defense through techniques such as data masking, data encryption, data  security gateway and data governance policy management is crucial to fortify an organization’s data management strategy.

Data Masking: Concealing the Vulnerabilities


What is Data Masking?

Data masking involves the transformation of sensitive information within a database, making it unintelligible to unauthorized users. The primary objective is to protect sensitive data while maintaining its usability for testing and analytics and overall usage in your processes.


The Defense Mechanism:

Data masking acts as the first line of defense by obscuring sensitive data such as personal identifiers, financial details, or confidential business information. This ensures that even if unauthorized access occurs, the exposed information is rendered useless and non-identifiable. This mechanism can also be useful in the context of specific compliancy driven initiatives such as i.e. GDPR.


Data Encryption: Securing the Data Source


What is Data Encryption?

Data encryption is the process of converting plain text into ciphertext, making it unreadable without the appropriate decryption key. It is a fundamental technique in securing data during transmission and storage.


The Defense Mechanism:

By implementing data encryption, organizations create a robust barrier against unauthorized access to sensitive information. It safeguards data in transit, preventing interception and tampering, and protects stored data from being deciphered by unauthorized entities. This mechanism can also be useful in case your infrastructure is outsources to a third party. Depending on the setup, even internal or external IT personal doesn’t have access to encrypted data.


Data Security Gateway: Active Control for Holistic Protection


What is a Data Security Gateway based on Data Virtualization?

A Data Security Gateway based on Data Virtualization acts as a centralized control point for securing data access, ensuring that only authorized users can retrieve and interact with sensitive information based on their role or profile.


The Defense Mechanism:


By implementing a Data Security Gateway, organizations gain real-time visibility into data access and usage. This proactive approach allows for immediate detection and response to potential threats, providing an additional layer of defense alongside masking, encryption, and governance.


The security layer of the data virtualization platform not only offers extra authentication functionality but also offers row, column and even cell-level security. With this approach you can enforce a security layer that is more strict than the underlying data sources.


Data Access Governance: Establishing Regulatory Compliance


What is Data Access Governance?

Data governance policy management involves defining and enforcing policies that dictate how data is collected, stored, processed, and shared within an organization. It provides a structured framework for managing data assets and allows you to easily create data access policies with a few clicks and preview them before they’re implemented. 


The Defense Mechanism:

Data governance policy management acts as the overarching defense strategy, ensuring that data is handled in accordance with regulatory requirements and internal standards. By establishing clear guidelines and enforcing policies, organizations mitigate risks associated with data breaches and non-compliance. Depending on the technology this can be enabled with a no-code approach to configure and execute a policy in a matter of minutes accross .



Conclusion: Integrating Defense Mechanisms for Holistic Protection

While each technique offers a specific layer of defense, their true strength lies in their integration. Data masking, encryption, data security gateways and governance policy management work synergistically to create a comprehensive and resilient data protection strategy.

By combining these techniques, organizations not only mitigate the risk of data breaches but also ensure compliance with industry regulations and standards. This is crucial in maintaining the trust of customers and stakeholders and avoiding legal repercussions.

By adopting the combination of these techniques, businesses can fortify their data management practices, instill confidence in stakeholders, and navigate the digital data landscape with resilience and security.


Interested in elevating your data security to the necessary standards? Discover how Datalumen can assist you in achieving this goal. 



In the ever-evolving landscape of data-driven decision-making, organizations are increasingly recognizing the critical interplay between Master Data Management (MDM) and Data Governance. These two pillars, seemingly distinct in their functions, converge to form a symbiotic relationship that is instrumental in driving efficiency, ensuring compliance, and fostering a data-driven culture within enterprises.

MDM – Orchestrating Data Symphony

At its core, Master Data Management is the discipline of managing an organization’s critical data, including master and reference data, to ensure uniformity, accuracy, and consistency across the entire organization. This involves establishing and maintaining a single, authoritative version of the truth for core data entities such as customers, products, assets, and employees. MDM acts as the custodian of data integrity, fostering reliability in decision-making processes and supporting various business functions.

Data Governance – Setting The Stage For Data Harmony

On the other hand, Data Governance is the framework that defines how organizations manage and control their data assets. It involves establishing policies, procedures, and standards to ensure data quality, security, and compliance with regulatory requirements. Data Governance provides the necessary oversight and control mechanisms to safeguard data assets and align them with organizational objectives.

In Unison

The link between MDM and Governance lies in their shared objective of ensuring data accuracy, consistency, and reliability. MDM provides the tools and processes to create a single, trusted source of master data, while Governance defines the rules and policies that guide the creation, usage, and maintenance of this data. Together, they form a formidable alliance that addresses the challenges of data silos, inconsistency, and lack of accountability.

Effective MDM and Governance synergies offer a range of benefits to organizations. Firstly, they enhance data quality by establishing standardized processes for data creation, validation, and maintenance. This, in turn, leads to improved decision-making, as stakeholders can confidently rely on accurate and consistent data. Moreover, compliance with regulatory requirements becomes more manageable, as Governance ensures that data practices align with legal and industry standards. Next to Data Quality, Data Governance typically also embodies a number of other data management capabilities, such as a Business Glossary to properly manage shared business terms and definitions and the associated data governance workflows and processes. Having this properly in place is also fundamental for MDM.

Furthermore, the collaborative efforts of MDM and Governance foster a data-driven culture within organizations. Employees are empowered with reliable data, breaking down silos and encouraging cross-functional collaboration. This not only enhances operational efficiency but also facilitates innovation and agility in responding to market dynamics.



In conclusion, the link between Master Data Management and Governance is not just a technical integration but a strategic alliance that underpins the success of modern enterprises. By aligning MDM and Governance initiatives, organizations can unlock the full potential of their data assets, driving informed decision-making, ensuring compliance, and fostering a culture that values and leverages the power of data. The journey towards data excellence begins with recognizing and nurturing this symbiotic relationship.


Also want to take your data agenda to the next level and address MDM & Data Governance? Would you like to find out how Datalumen can help?


Data Governance as the Foundation for Data Democratization. The data governance solution market has evolved from an emerging space to a mature one, with core capabilities including data catalogs, policy management, and privacy and compliance modules. The focus now is on elevating data governance to a strategic capability, enabling organizations to move to a federated model for trusted data. The market emphasis has shifted from regulatory compliance to realizing the full potential of data for business insights.


TOP3 Key considerations for data governance solution customers include:

  1. Stable Foundation for AI:
    Providers should offer a stable foundation for trusted data intelligence for AI. This involves implementing robust data lineage, versatile access controls, and comprehensive data quality checks. Privacy compliance and security policies are used to ensure data reliability, integrity, and quality for AI applications.
  2. Support for Stewardship Functions:
    Data stewards require solutions with intuitive workflows and advanced collaboration features. This includes seamless user experiences and modern collaboration tools to ensure accurate ownership and flexible cocreation. Robust stewardship capabilities directly impact efficiency, streamline governance processes, and reduce the time and effort needed for data stewardship tasks.
  3. Empower Data Democratization:
    Organizations should prioritize providers that enhance data literacy and enable self-service data access. Solutions with advanced glossary capabilities and granular personalization options support a wide range of personas in accessing, understanding, transforming, and personalizing data. Self-service capabilities with intuitive embedded controls expedite data access, reducing dependence on IT departments and eliminating the need for business users to possess specialized technical knowledge.


In summary, the current state of the data governance market is marked by a shift towards strategic capabilities, focusing on AI-driven decision-making, efficient stewardship functions, and empowering data democratization for enhanced business value creation.


Want more info about the latest Data Governance Solutions from vendors like Collibra, Allation, Informatica or others? Let us help you unravel the pros and cons, ensuring a perfect match for your needs. Don’t miss out – kickstart the conversation now!



Data is king. Businesses rely on data to make decisions, understand their customers, and improve their operations. However, in complex organizations, it can be difficult to get insight into relevant data flows.

There are a number of reasons why this is the case. First, complex organizations often have a large number of data sources. This can make it difficult to track the movement of data between different systems and applications. Second, data flows in complex organizations can be complex and dynamic. This means that the path that data takes can change over time, making it difficult to keep track of. Third, data in complex organizations is often siloed. This means that data is stored in different systems and applications, making it difficult to get a holistic view of the data.

As a result of these challenges, it can be difficult to get insight into data flows in complex organizations. This can make it difficult to ensure the quality of data, comply with regulations, and make informed decisions. Despite these challenges, there are a number of things that organizations can do to get insight into data flows. Data lineage is typically a fundamental capability.

What is Data Lineage?

Data lineage is the tracking of data as it flows through an organization’s systems and processes. It provides a detailed record of the data’s origin, transformation, and destination. Data lineage is important for a number of reasons, including:

  • Data quality: Data lineage can help to ensure the quality of data by providing a way to track its history and identify any potential problems. For example, if a data point changes unexpectedly, data lineage can be used to trace back the change and identify the source of the problem.
  • Compliance: Data lineage can help organizations to comply with regulations that require them to track the movement of data. For example, the European Union’s General Data Protection Regulation (GDPR) requires organizations to track the personal data of their customers. Data lineage can help organizations to meet this requirement by providing a record of how personal data is collected, used, and stored.
  • Auditing: Data lineage can be used to audit data flows and identify potential security risks. For example, if an organization is concerned about the possibility of a data breach, data lineage can be used to track the movement of sensitive data and identify any potential vulnerabilities.
  • Business intelligence: Data lineage can help organizations to gain insights into their data by providing a way to track the relationships between different data sets. For example, data lineage can be used to identify which data sets are used to calculate a particular metric. This information can then be used to improve the accuracy of the metric and make better business decisions.


  • Improved data governance: Data lineage can help organizations to improve their data governance practices by providing a way to track the ownership, access, and usage of data. This information can then be used to develop and enforce data policies and procedures.
  • Reduced risk of data loss: Data lineage can help to reduce the risk of data loss by providing a way to track the location of data. This information can then be used to recover data in the event of a loss or corruption.
  • Increased data agility: Data lineage can help organizations to become more data agile by providing a way to track the changes to data. This information can then be used to update data models and applications as needed.

How to Implement Data Lineage

There are a number of ways to implement data lineage. One way is to use a data lineage tool. These tools can help to automate the tracking of data flows and provide a visual representation of data lineage. Another way to implement data lineage is to manually track data flows. This can be done by creating spreadsheets or diagrams that track the movement of data.

The best way to implement data lineage will depend on the specific needs of the organization. However, all organizations should consider implementing data lineage to improve the quality and usability of their data.

A sample data lineage diagram illustrating the high-level data flow between a Tableau Workbook and a Snowflake datawarehouse.


In summary, data lineage is an important tool for organizations that want to ensure the quality, compliance, security, and usability of their data. By tracking the movement of data, data lineage can help organizations to identify and address problems, comply with regulations, and gain insights into their data.


Also want to take your data agenda to the next level? Would you like to find out how Datalumen can help?


In the realm of data, metadata is widely recognized as the “data about data” – the vital information that characterizes its structure, meaning, and utility. Metadata serves as a crucial element in data management, enhancing accessibility and comprehension.

Among various metadata types, active metadata stands out as a dynamic and continuously updated entity that informs decisions and drives actions. It surpasses the notion of a static data description; instead, it thrives as a dynamic resource, revolutionizing our interaction with data.

Why Do You Need Active Metadata?

In our data-centric world, organizations generate an unprecedented volume of data. While this data holds immense value, managing it poses challenges. Active metadata emerges as an instrumental asset, empowering businesses to:

  1. Unearth deeper data insights: Active metadata provides businesses with comprehensive comprehension of their data, enabling the identification of patterns, trends, and relationships.
  2. Enhance data quality: Active metadata plays a pivotal role in elevating data quality. By pinpointing and rectifying errors, as well as ensuring data consistency and currency, businesses can significantly improve their data assets.
  3. Foster data accessibility: Active metadata promotes seamless access to data by providing users with comprehensive information and simplifying data search and utilization.
  4. Automate data-related tasks: Leveraging active metadata, businesses can automate data management tasks, freeing up valuable time for users to focus on critical endeavors. This, in turn, enhances accuracy and efficiency.

How Does Active Metadata Work?

Active metadata finds its home in a metadata repository, which can exist as a physical or virtual database hosted either on-premises or in the cloud. The repository remains interconnected with data sources, continuously monitoring data alterations. This invaluable information is then employed to update the active metadata.

The applications of active metadata are diverse. Here are a few examples:

  1. Enforcing data quality standards: Active metadata empowers the enforcement of data quality rules, ensuring utmost accuracy and consistency.
  2. Generating comprehensive reports: Active metadata serves as the foundation for generating detailed reports on data usage, allowing businesses to track patterns, trends, and usage metrics.
  3. Empowering analytical capabilities: Active metadata fuels cutting-edge analytics tools, enabling businesses to extract profound insights from their data and make well-informed decisions.


Active metadata emerges as a potent resource, empowering businesses to manage their data effectively. By gaining deeper insights, enhancing data quality, and fostering accessibility, active metadata equips enterprises with the ability to unlock the true potential of their data and make informed decisions.


Want to take full advantage of your metadata? Also want to take your data agenda to the next level? Would you like to find out how Datalumen can help?


In the age of data-driven decision making, organizations face the challenge of determining when data quality is sufficient for their needs. Striking the right balance between investing resources in improving data quality and achieving an acceptable level of accuracy and reliability is crucial. In this article, we give you some step-by-step handles to help organizations assess and establish the appropriate data quality level.

Step 1: Define Data Quality Requirements

The first step in determining the right data quality level is to define clear and specific requirements. Take the time to understand your organization’s goals, objectives, and the decisions that will be based on the data. Identify the key dimensions of data quality that matter most to your organization, such as accuracy, completeness, consistency, timeliness, and relevancy. Defining these requirements will serve as a guide for assessing data quality.

Step 2: Evaluate Data Use Cases

Next, evaluate the different use cases and scenarios where data will be utilized. Each use case may have varying requirements and tolerance levels for data quality. Analyze the potential impact of data errors or inaccuracies on the decisions made in each specific use case. This evaluation will help prioritize the allocation of resources and efforts towards improving data quality where it matters the most.

Step 3: Assess Data Collection and Processing Methods

Evaluate the data collection and processing methods employed by your organization. Examine the data sources, collection processes, and data transformation steps. Identify potential bottlenecks, vulnerabilities, and areas where errors or inaccuracies could be introduced. Streamline the data collection process and implement quality checks at each step to ensure the integrity and reliability of the data.

Step 4: Implement Data Quality Controls

To ensure data quality is at an acceptable level, implement data quality controls throughout the data lifecycle. This includes setting up validation rules, data cleansing routines, and data profiling techniques. Establish automated checks to identify and rectify data anomalies, outliers, and inconsistencies. Leverage technology and tools to automate these processes and minimize human errors.

Step 5: Measure Data Quality

Establish data quality metrics that align with your defined requirements. These metrics may include error rates, completeness percentages, timeliness measures, or any other specific indicators relevant to your organization. Implement mechanisms to measure and monitor data quality regularly. Leverage statistical analysis, data profiling, and data visualization techniques to gain insights into the overall quality level and identify areas for improvement.

Step 6: Set Tolerance Levels

Define tolerance levels for data quality based on the specific use cases and requirements of your organization. Determine the acceptable margin of error for each use case. Consider factors such as the criticality of the decision being made, the potential impact of data errors, and the costs associated with improving data quality. Establishing tolerance levels will help determine when data quality is good enough to support the decision-making process effectively.

Step 7: Continuous Improvement

Data quality is an ongoing process that requires continuous monitoring and improvement. Regularly review the established metrics and tolerance levels. Evaluate feedback from data consumers and stakeholders to identify areas for enhancement. Invest in training and education programs to improve data literacy within the organization. By fostering a culture of continuous improvement, you can ensure that data quality is consistently enhanced over time.


Determining the right data quality level is a balancing act for organizations seeking to optimize resources while maintaining reliable insights. By following a structured methodology, including defining data quality requirements, evaluating use cases, assessing data collection and processing methods, implementing data quality controls, measuring data quality, setting tolerance levels, and embracing continuous improvement, organizations can strike the right balance. Achieving the right data quality level will provide confidence in the decision-making process, leading to better business outcomes and a competitive advantage in the data-driven landscape.



Data needs to be effectively managed, protected, and leveraged to derive valuable insights. And that is where data governance frameworks come into play. Data governance frameworks provide a structured approach to establishing policies, processes, and guidelines for data management, ensuring data integrity, privacy, and compliance. In this article, we will delve into the importance of data governance frameworks and explore key components that contribute to their success.

Why Data Governance Frameworks Matter

Data governance frameworks can serve as the backbone for organizations to establish a holistic and consistent approach to managing their data assets. Here are a few reasons why data governance frameworks matter:

  1. Data Quality and Integrity: Data governance frameworks emphasize the importance of data quality, ensuring that data is accurate, consistent, and reliable. By implementing data governance practices, organizations can improve the overall integrity of their data assets, reducing errors and enhancing decision-making capabilities.
  2. Compliance and Risk Management: With an increasing number of data protection regulations, such as GDPR and CCPA, organizations face stringent requirements for data privacy and security. Data governance frameworks help establish policies and procedures to ensure compliance, mitigate risks, and protect sensitive information from unauthorized access or breaches.
  3. Stakeholder Collaboration: Data governance frameworks foster collaboration among stakeholders across the organization. By defining roles, responsibilities, and processes, these frameworks enable effective communication, coordination, and decision-making regarding data management practices.

    Key Components of Data Governance Frameworks

    While data governance frameworks may vary depending on organizational needs, several key components contribute to their effectiveness:

    1. Data Governance Policies: Policies act as the foundation of data governance frameworks, defining the principles and guidelines for data management. These policies address data ownership, data classification, data sharing, privacy, and security measures. They provide a clear direction for employees on how to handle data and ensure compliance with regulatory requirements.
    2. Data Stewardship: Data stewardship focuses on the responsibility of individuals or teams within the organization to manage and govern data effectively. It involves assigning data stewardship roles and responsibilities, establishing data quality standards, and monitoring data integrity throughout its lifecycle. Data stewards act as custodians of data, ensuring its accuracy, availability, and appropriate use.
    3. Data Classification and Categorization: Data governance frameworks include mechanisms for classifying and categorizing data based on its sensitivity, criticality, and usage. This enables organizations to apply appropriate security controls, access restrictions, and retention policies. Data classification also aids in identifying and prioritizing data governance requirements based on the value and risk associated with different data types.
    4. Data Lifecycle Management: Effective data governance frameworks encompass the entire data lifecycle, from data creation to archiving or disposal. They define processes for data collection, storage, integration, analysis, and archival, ensuring data is managed consistently and securely throughout its journey. This includes data quality assessment, metadata management, data retention policies, and data archiving strategies.
    5. Data Privacy and Security: Data governance frameworks address privacy and security concerns by establishing policies and practices to safeguard sensitive information. This includes implementing access controls, encryption techniques, data anonymization, and monitoring mechanisms to detect and respond to data breaches or unauthorized access attempts.



    Data governance frameworks are indispensable for organizations seeking to manage data effectively, mitigate risks, and comply with data protection regulations. By establishing clear policies, roles, and processes, organizations can ensure data quality, integrity, and privacy while enabling collaboration and decision-making across the enterprise. It is crucial for organizations to tailor their data governance frameworks to their specific needs and continually evolve them to adapt to changing data landscapes and emerging challenges. With a robust data governance framework in place, organizations can build a solid foundation for leveraging the power of data.


    Also want to take your data agenda to the next level? Would you like to find out how Datalumen can help?


    Business glossaries and data catalogs play vital roles within data management. They are essential components in virtually any data architecture, but their purposes and interconnections are not always clear to everyone and as such worth exploring.

    Exploring the relationships

    A business glossary and a data catalog are closely related components within the field of data management. They both serve the purpose of organizing and documenting information about data assets within an organization, but they focus on different aspects.

    Business Glossaries – Establishing the common language

    A business glossary is a centralized repository or collection of terms and definitions that are specific to the organization’s business domain. It provides a common understanding and consistent definition of business terms used across different departments and stakeholders. The business glossary helps ensure clear communication and alignment between business users, data professionals, and technical teams by establishing a shared vocabulary.

    Data Catalogs – Unveiling the data landscape

    On the other hand, a data catalog is a comprehensive inventory of the available data assets within an organization. It provides detailed information about the structure, content, and characteristics of each dataset or data source. A data catalog captures metadata about the data, including data lineage, data sources, data quality, and other relevant information. It serves as a valuable reference for data consumers, data analysts, and data scientists to discover, understand, and effectively utilize the available data assets.

    Complementary forces

    The link between a business glossary and a data catalog lies in their complementary roles in facilitating data understanding and governance. While the business glossary focuses on defining business terms and ensuring consistent business vocabulary, the data catalog provides technical information about the underlying data assets. The business glossary helps users interpret and understand the data catalog by providing clear definitions of the business terms used in the metadata descriptions. In turn, the data catalog helps enrich the business glossary by associating technical metadata with the corresponding business terms, enhancing the overall understanding of the data assets and their context within the organization.

    By integrating a business glossary with a data catalog, organizations can bridge the gap between business and technical perspectives, fostering better collaboration, data governance, and data-driven decision-making.


    Also want to take your data agenda to the next level? Would you like to find out how Datalumen can help?


    Data quality management is a critical component in the successful realization of your data strategy, and there are several hot topics that are currently gaining traction in this area. Here are some of the latest trends in data quality & data observability:

    • DQaaS or DQSaaS
    • Hybrid Usage & Island Hopping.
    • Machine Learning has hit the DQ space.
    • From patchwork to a fundamental capability.
    • From a technology to a business driven framework.

    TREND #1. DQaaS or DQSaaS

    Data quality as a service (DQaaS or DQSaaS) is an emerging trend that involves outsourcing a subset of data quality management functionality to third-party cloud application providers. DQaaS providers offer tools and services to monitor and improve data quality, reducing the workload on in-house data teams. In general, SaaS is provided in a cloud-based or hosted model.

    We see two types of DQaaS:
    • One type basically providing a complete set of data quality functionalities (equivalent to traditional on-premise offerings) that run on cloud platforms. Clients typically order them on demand from cloud enabled vendors and often use them on a subscription basis (1 year or longer).
    • The other type of DQaaS is basically based on on-demand online services to i.e. validate and verify addresses or other relevant data assets. These micro data quality services are typically used on a pay per usage / service-call basis.

    TREND #2. Hybrid Usage & Island Hopping

    Until recently, Data Quality was primarily applied in an operational/transactional context. DQ in an analytics context was also already implemented in quiet some organization, but what is relative new is the enhanced DQ usage in a number of other data related initiatives such as MDM, DG, Data Engineering and AI/ML. What we also see as part of that move, is a shift from an island DQ usage tailored towards one specific initiative to an organization-wide DQ usage. The organization-wide DQ approach provides a number of benefits ranging from more consistent data quality up to enhanced collaboration, more efficient data processing and reuse.

    TREND #3. Machine Learning has hit the DQ space

    We don’t need to explain you that AI/ML is hot and all over the place. The AI/ML examples that you read about might not always seem to be relevant to you. However, DQ is one of those areas where AI/ML can really deliver substantial added value. Machine learning algorithms can be used to automatically identify data quality issues and correct them. For example, machine learning models can be trained to detect duplicates, correct spelling errors, and identify missing data. Next to more automated error resolution a lot of DQ applications are expanding DQ to provide insights by discovering relationships, patterns and trends.

    We see both custombuild applications (Python and other open source libraries) and Data Quality platforms coming with embedded AI/ML functionality to bring DQ automation to the next level.

    TREND #4. From patchwork to a fundamental & companywide capability

    As Data Governance (DG) maturity in organizations grows, we also see that they address Data Quality as a fundamental integrated data capability. Instead of the so called one-off usage for one specific case (data migration, CRM, etc), a lot of companies make DQ a structural component that they can reuse continuously for all their data related initiatives. The benefit of this wider embedded approach is that it eases organizations to demonstrate DQ to be a profit vs a cost component.

    TREND #5. From a technology to a business driven framework

    As we mentioned in the previous trend and as maturity increases, we are also observing a shift in the approach from an it-driven towards a business-driven perspective. The main reason for this is that organizations require data quality to be seamlessly integrated into their business processes for optimal results.


    In conclusion, data quality management is a critical component of data management, and there are many exciting trends and technologies emerging in this area. From data profiling to machine learning, organizations have many tools and techniques available to improve data quality and drive business growth.


    As the world becomes increasingly digitised, organisations are generating more data than ever before. But did you know that up to 80% of that data remains untapped? This structured/unstructured, unprocessed data is known as dark data, and it has the potential to be a goldmine of insights for organisations.

    What is Dark Data?

    Dark data refers to data that organisations generate but don’t use. This data is typically unstructured and stored in various formats, such as emails, documents, images, videos, and social media posts. It’s often ignored because it is difficult to process and analyse, requiring advanced analytics tools and techniques to extract meaningful insights. However, with the right approach, dark data can be a valuable source of information that can help organisations make better decisions and gain a competitive advantage.

    Where is Dark Data Found?

    Dark data can be found in many areas of an organisation, including customer feedback, product reviews, employee emails, and social media mentions. By analysing this data, organisations can uncover hidden patterns and insights that can help them improve their products and services, enhance customer experiences, optimise operations, and reduce costs.

    Dark Data in Action

    Dark data can be used in virtually any industry to improve business outcomes. For example, an FMCG company can analyse customer reviews on social media and product forums to identify common complaints or issues. This data can be used to improve product design, customer service, and marketing strategies. Similarly, a healthcare organisation can analyse patient data to identify potential health risks, improve treatment plans, and optimise resource allocation.

    Analysing dark data requires a different approach than traditional structured data analysis. Machine learning and artificial intelligence can be used to process large amounts of unstructured data and extract meaningful insights. This technology can be used to categorise data, identify patterns and anomalies, and extract sentiment from text and other data.

    How to Leverage Dark Data?

    To leverage dark data effectively, organisations need to establish a data strategy that includes data governance, data quality, and data privacy. They need to ensure that the data they collect is accurate, complete, and secure, and that they comply with regulatory requirements. Additionally, organisations need to invest in the right tools and technologies to extract insights from dark data.

    There definitely some challenges with dark data is to extract valuable insights from it, but there are strategies you can use to put it in action:

    1. Identify your goals: Before you begin to analyze your dark data, it is essential to identify your business goals. Understanding what you want to achieve will help you determine what data to focus on and what insights you need to extract.
    2. Collaborate across teams: Dark data is often spread across different departments within an organization. Collaboration across teams can help you identify opportunities for using this data and uncover insights that might have been missed.
    3. Make your data consumable: Once you have identified the data you need, the next step is to make it available. This doesn’t mean that you need move your data in a single location as there are plenty efficient architectures to prevent data duplication. Making your data available in an agile and flexible way, will enable you to process and analyze it more efficiently.
    4. Use data analytics & AI/ML: The right tools can help you mine valuable insights from your dark data. There are various tools available, from simple data visualization software to sophisticated machine learning algorithms.
    5. Implement data governance: Finally, it is essential to have proper data governance in place to ensure that your dark data is used appropriately. This includes establishing data quality standards, data retention policies, and data security protocols.


    Dark data represents a vast untapped resource for organisations seeking to gain a competitive advantage. By analysing this data, organisations can uncover valuable insights that can help them make better decisions and improve their operations. With the right strategies and technology in place, organisations can demystify dark data and unlock its full potential. It’s time to start exploring the dark data lurking within your organisation and turn it into a competitive advantage.


    Want to shed some light on your dark data? Want to take your strategic Data Governance initiatives to the next level?