In today’s data-driven world, organizations are constantly collecting vast amounts of data. This data can be used to gain insights, make informed decisions, and drive business growth. However, not all data is created equal. Some data may be useful, while others may be irrelevant or even harmful. Dark data is one such type of data that can cause harm to organizations. In this blog post, we will explore what dark data is and how to prevent it.

What is Dark Data?

Dark data refers to data that is collected by organizations but is not utilized for any meaningful purpose. This data may be unstructured or semi-structured, making it difficult to analyse and derive insights. Dark data can come from a variety of sources, including customer interactions, social media, and employee behavior.

The problem with dark data is that it takes up valuable storage space, increases storage costs, and can potentially expose organizations to data breaches. Moreover, dark data can become a liability for organizations as it can be subject to regulatory compliance requirements, such as GDPR and CCPA.

How to Prevent Dark Data?

Develop a Data Management Strategy:

Data management strategy is a plan that outlines how an organization will collect, store, manage, and use its data. It involves the processes, policies, and tools used to manage data throughout its lifecycle. A data management strategy should align with an organization’s business objectives and support its data-driven initiatives.

Benefits of Data Management Strategy

• Improved Data Quality: Data management strategy can help organizations improve data quality. By having a clear understanding of how data is collected and managed, organizations can ensure that data is accurate, complete, and consistent.
• Better Decision Making: A well-defined data management strategy can help organizations make better decisions. By having access to high-quality data, organizations can derive meaningful insights and make informed decisions.
• Cost Reduction: Data management strategy can help organizations reduce costs associated with data management. By identifying and eliminating unnecessary data, organizations can reduce storage costs and improve overall efficiency.
• Compliance with Regulations: Data management strategy can help organizations comply with data protection and privacy regulations such as GDPR, CCPA, and HIPAA. By implementing proper data governance policies, organizations can ensure that data is managed in a compliant manner.
• Improved Collaboration: Data management strategy can help organizations improve collaboration between departments. By having a central repository for data, departments can easily access and share data, leading to better collaboration and decision-making.

How to Develop a Data Management Strategy?

• Define Business Objectives: A data management strategy should align with an organization’s business objectives. Therefore, organizations should define their objectives and identify the data needed to achieve them.
• Identify Data Sources: Organizations should identify the data sources they have and how they are collected. This will help them understand the quality and accuracy of their data.
• Define Data Governance Policies: Data governance policies define how data is collected, stored, and used. They should include data security, privacy, and compliance with regulations.
• Implement Data Quality Controls: Data quality controls ensure that data is accurate, complete, and consistent. Organizations should implement controls such as data validation, cleansing, and normalization.
• Choose the Right Tools: Choosing the right tools for data management is critical. Organizations should select tools that are scalable, secure, and easy to use.

Use Automation:

Automation can be a powerful tool in identifying and categorizing dark data for organizations. Dark data refers to data that is collected by organizations but is not utilized for any meaningful purpose. This type of data can be difficult to identify and categorize manually, making it hard for organizations to manage effectively. In this post, we will discuss how automation can help organizations identify and categorize dark data.

• Machine Learning Algorithms: Machine learning algorithms can be trained to identify patterns and trends in data. These algorithms can be used to analyse large volumes of data quickly and accurately. Organizations can use machine learning algorithms to analyse their data and identify any patterns that may indicate dark data.
• Data Classification Tools: Data classification tools use predefined rules and algorithms to identify and categorize data. These tools can be used to automatically identify and classify data based on predefined categories. For example, data classification tools can be used to identify sensitive data, which may be at risk of being exposed to external threats.
• Automated Data Profiling: Automated data profiling can be used to identify data quality issues, such as duplicates, inconsistencies, and missing data. These profiling tools can be used to identify and categorize dark data, making it easier for organizations to manage and analyse their data effectively.
• Data Mining Techniques: Data mining techniques can be used to extract useful insights from large volumes of data. These techniques can be used to identify patterns and trends in data, making it easier to identify dark data. Data mining techniques can be automated, making it easier for organizations to analyse their data quickly and accurately.
• Natural Language Processing (NLP): Natural Language Processing (NLP) can be used to analyse unstructured data, such as text and speech. NLP tools can be used to identify and categorize dark data, such as unstructured data from customer interactions or social media.

There are various automation platforms available that can help organizations identify and categorize dark data. These platforms use advanced algorithms and machine learning techniques to analyse large volumes of data quickly and accurately.

1. IBM Watson Discovery: IBM Watson Discovery offers a suite of AI-powered tools for analysing and categorizing unstructured data. Some of the key products within this suite include:

• Watson Discovery: This platform enables organizations to analyse large volumes of unstructured data, such as text, images, and audio, to uncover hidden insights and relationships.
• Watson Knowledge Studio: This platform enables organizations to train Watson Discovery to recognize specific entities and relationships within their data, helping to improve accuracy and relevance.

2. Google Cloud Platform (GCP): Google Cloud Platform offers a range of services for data analysis, including several options for identifying and categorizing dark data. Some of the key products within this platform include:

• Cloud Data Loss Prevention (DLP): This platform enables organizations to automatically scan their data for sensitive information, such as personal data or financial information, and classify it according to predefined policies.
• Cloud Natural Language: This platform enables organizations to analyse large volumes of unstructured data, such as text, to uncover hidden insights and relationships.
• Cloud Vision AI: This platform enables organizations to analyse and categorize images and videos using advanced computer vision algorithms.

3. Microsoft Azure: Microsoft Azure provides a range of services for data analysis, including several options for identifying and categorizing dark data. Some of the key products within this platform include:

• Azure Cognitive Services: This platform offers a suite of AI-powered tools for analysing and categorizing unstructured data, including text, images, and videos.
• Azure Information Protection: This platform enables organizations to classify and protect sensitive data across their entire IT ecosystem, including on-premises and cloud-based systems.

4. Amazon Web Services (AWS): Amazon Web Services provides a range of services for data analysis, including several options for identifying and categorizing dark data. Some of the key products within this platform include:

• Amazon Macie: This platform uses machine learning algorithms to automatically discover, classify, and protect sensitive data stored within an organization’s AWS environment.
• Amazon Rekognition: This platform enables organizations to analyse and categorize images and videos using advanced computer vision algorithms.
• Amazon Comprehend: This platform enables organizations to analyse large volumes of unstructured data, such as text, to uncover hidden insights and relationships.

5. Informatica: Informatica offers a suite of products for data management and analysis, including several options for identifying and categorizing dark data. Some of the key products within this suite include:

• Informatica Enterprise Data Catalog: This platform enables organizations to automatically discover, classify, and govern data assets across their entire IT ecosystem, including on-premises and cloud-based systems.
• Informatica Axon Data Governance: This platform enables organizations to establish and enforce data governance policies and standards, ensuring compliance with data protection regulations.

Regularly Audit Data:

Regular auditing of data is essential to prevent dark data. Auditing can help organizations identify data that is no longer useful or relevant. Data that is not needed should be deleted or archived. There are various platforms available for auditing data in organizations. These platforms help organizations to identify any discrepancies in data, monitor data quality, and ensure compliance with data protection regulations. In this blog post, we will discuss some of the popular platforms available for auditing data in organizations.

• ACL Analytics: ACL Analytics is a data analysis platform that helps organizations to analyse and audit large volumes of data. It provides a range of tools for data visualization, data mining, and data quality monitoring. ACL Analytics is widely used in industries such as finance, healthcare, and manufacturing.
• Datawatch Monarch: Datawatch Monarch is a self-service data preparation and analytics platform that helps organizations to extract, transform, and load data. It provides a range of tools for data cleaning, data transformation, and data analysis. Datawatch Monarch is widely used in industries such as healthcare, financial services, and government.
• Alteryx: Alteryx is a data preparation and analytics platform that helps organizations to analyse and audit data. It provides a range of tools for data blending, data analysis, and data visualization. Alteryx is widely used in industries such as finance, healthcare, and retail.
• Trifacta: Trifacta is a data preparation platform that helps organizations to clean, transform, and monitor data. It provides a range of tools for data wrangling, data profiling, and data quality monitoring. Trifacta is widely used in industries such as finance, healthcare, and telecommunications.
• Talend: Talend is a data integration and data quality platform that helps organizations to extract, transform, and load data. It provides a range of tools for data integration, data quality monitoring, and data governance. Talend is widely used in industries such as finance, healthcare, and retail.

Train Employees:

1. Employees play a crucial role in preventing dark data. Organizations should train employees on the importance of data management and how to identify data that is no longer needed.

Implement Data Governance Policies:

Data governance policies help organizations ensure that data is managed properly. Data governance policies should define how data is collected, stored, and used. Policies should also outline how data is shared, who has access to it, and how it is protected. There are many platforms available to implement data governance policies. Here are some examples:

1. Collibra: Collibra is a data governance platform that offers a range of tools for managing and governing data assets. The platform includes features for data cataloging, data lineage, data quality, and data privacy management, among others.
2. Informatica Axon Data Governance: Informatica Axon Data Governance is a data governance platform that provides a range of features for managing and governing data assets. The platform includes tools for data cataloging, data lineage, data quality, and data stewardship, among others.
3. IBM InfoSphere Information Governance Catalog: IBM InfoSphere Information Governance Catalog is a data governance platform that provides a range of tools for managing and governing data assets. The platform includes features for data cataloging, data lineage, data quality, and data policy management, among others.
4. Talend Data Fabric: Talend Data Fabric is a data integration and management platform that includes features for data governance. The platform provides tools for data cataloging, data lineage, and data quality, among others.
5. Alation: Alation is a data governance platform that offers a range of tools for managing and governing data assets. The platform includes features for data cataloging, data lineage, data quality, and data stewardship, among others.
6. SAP Master Data Governance: SAP Master Data Governance is a data governance platform that provides a range of features for managing and governing master data. The platform includes tools for data modeling, data quality management, and data stewardship, among others.
7. Informatica MDM: Informatica MDM is a master data management platform that provides tools for managing and governing master data. The platform includes features for data modelling, data quality management, and data stewardship, among others.

In conclusion, dark data can cause significant harm to organizations. Therefore, it is essential to develop a data management strategy, use automation, regularly audit data, train employees, and implement data governance policies to prevent dark data. By taking these steps, organizations can ensure that their data is managed effectively and efficiently, and they can derive meaningful insights to drive business growth.