What is Dark Data?
Dark Data is the collection of unprocessed information generated by fleets, freight systems, and supply chains. The data is collected by companies during regular business interactions, but the potential usually remains untapped. Estimates say that less than 1% of data is ever analyzed or used, meaning the other 99% goes unanalyzed and unprocessed.
Most companies store large amounts of data. According to a global research survey by Splunk, which included 1,300 businesses and IT decision-makers, 60% reported half of their organization’s data to be dark, with ⅓ of respondents reporting the amount to be 75% of data or more in the dark.
Dark Data is usually retained for compliance purposes only, which can lead to risks and costs associated with storage and management. To both avoid risks and ensure compliance, it is essential to understand and manage dark data effectively.
In the modern age, businesses and organizations can access more data than ever, and many are taking advantage of the opportunities to use data for KPIs (key performance indicators) and decision-making. However, many companies have left vast amounts of data untouched, underutilized, and undiscovered.
One of the reasons Dark data accumulates is that organizations believe it is valuable to store large quantities of data in case it becomes valuable, especially since storage can be inexpensive. However, much of the stored data is never used because storage reservoirs do not document metadata labels appropriately.
Types of Dark Data
Dark data can be structured, unstructured, or semi-structured data.
Structured Data– The following examples are dark data from structured data resources:
- – Server log files
- – Internet of Things (IoT) sensor data
- – Customer relationship management (CRM) databases
- – Enterprise resources planning (ERP) systems
Other forms of data, usually structured, include bank statements, medical records, and encrypted customer data.
Unstructured Data– Unstructured data includes information that cannot be organized in spreadsheets or databases. A formal data analysis would require conversion, codification, tiering, and structuring to be organized. Some examples of unstructured data include email correspondences, chat logs, text documents, social media posts, and PDFs.
Semi-structured Data– These types include HTML code, invoices, graphs, tables, and XML documents.
Why Data Goes Dark
Lack of Awareness: Businesses often are unaware of the data’s existence and, at other times, simply do not understand its value or relevance.
Silos: Data silos occur when different organizational departments collect and store data independently. This leads to data fragmentation and isolation, making the data inaccessible or invisible to other teams.
Lack of governance: Data will likely become lost, disorganized, and unusable without robust data governance.
Legacy Systems: As organizations upgrade their software and hardware, prior systems become outdated and irrelevant. If the data stored in legacy systems goes dark, it cannot integrate with modern tools.
Incomplete data integration: Data gaps and inconsistencies can arise when data integration is incomplete or ineffective. This may leave datasets inaccessible or improperly linked.
Costs
Data Storage: Physical or Digital storage infrastructure is required for data storage. Infrastructure includes servers, data centers, cloud storage solutions, and backup systems. The more data you have, the more data storage capacity is needed.
Liability: Several global privacy laws regarding data have been introduced over the last several years.
Opportunity: Before getting rid of data, it is crucial to analyze which data is usable and which is not. Analyzing data before removing unusable data can help companies avoid missed opportunities.
Inefficiency: When employees have to spend time combing through and managing large volumes of data, this slows down data retrieval and analysis.
Risks: There are many risks associated with dark data storage, which include insufficient cybersecurity, data breaches, compliance issues, and data loss, which can bring about reputational damage and financial consequences.
Dark Data Implications
Many companies have yet to discover the power of dark data. Utilizing the data can improve decision-making adaptations to market changes and allow companies to gain insights into strategic choices. Dark Data is beneficial for the transportation and logistics industry. The data can be used to optimize routes, improve fleet management, and enhance supply chain visibility. Analyzing data from GPS trackers, telematics, and historical data is very useful for boosting delivery performance, routes, and fuel efficiency.
Dark Data and Cybercrime
It is essential for businesses to proactively monitor the dark web to determine if their suppliers or customers are compromised. The dark web hosts underground forums and marketplaces to trade hacking tools, compromised credentials, and stolen data. Understanding and monitoring supply chain and third-party risks in the digital age is crucial. Legal and relational contracts are essential to suppressing partner opportunism within the digital supply chain.
Dark Data Risks
Dark Data can pose significant risks to organizations that impact their security and operations.
Data Breaches & Security Risks: Data breaches can be caused by improperly disposing of IT assets and dark data retention. This can result in lawsuits, penalties, and reputational damage.
Financial & Operational Costs: Continuously storing dark data can lead to excessive storage and management costs. These consequences can affect a company’s financial resources and efficiency.
Compliance and Regulatory Risks: Ineffectively governing dark data can lead to regulatory non-compliance, resulting in legal penalties and liabilities.
Missed Opportunities: A vast amount of dark data is not utilized actively for business or decision-making purposes. This leads to missed opportunities in operations, insights, and innovation.
Reputational Damage: A data breach affects customer trust and brand perception. When sensitive information or private records are breached, it can lead to identity theft and other serious consequences.
Energy & Resource Wasting: Data centers waste a lot of energy when storing dark data, which leads to increased environmental and operational costs.
Liabilities: It can be challenging for companies to minimize risks while ensuring compliance and privacy standards. However, failure to address these challenges can lead to legal and operational problems. Managing risks is critical to managing data assets effectively and protecting their reputation and operations.
Dark Data Insights
Utilizing Dark Data while ensuring compliance can provide valuable insights into customer behavior, trends, and operational efficiencies.
Sales & Marketing: Data analytics can help organizations better understand consumer needs and behaviors, allowing them to predict behaviors and cater to customer needs.
Products & Services Customization: Products can be customized according to customer data, reflecting their needs and preferences. It has been shown that organizations that leverage dark data achieve significant improvements in outcomes.
Operational Efficiency: When dark data is analyzed, it can highlight system bottlenecks, optimize resource allocation patterns, and also enable predictive maintenance. These benefits can lead to increased operational efficiency and cost savings.
Compliance & Risk Management: Utilizing the power of dark data can allow companies to detect and correct process errors, improve quality assurance practices, and even help identify security vulnerabilities. These factors can help mitigate risk and ensure regulatory compliance.
Customer Support & Experience: Leveraging dark data allows companies to improve customer support the customer experience, as well as drive business growth. The data provides insights into customer needs, preferences, and pain points. These factors allow for more accurate customer personalization and increase brand loyalty and satisfaction.
Ethical Usage
Transparency & Informed Consent: Obtain informed consent from the individuals your company is collecting data from, clearly communicating and disclosing what data is collected. Clear communication and disclosure will help to build trust and respect for your company.
Data Security & Privacy: Ensure that your business adheres to data regulations and implements the appropriate safeguards to prevent breaches, misuse, and unauthorized access while ensuring privacy and data confidentiality.
Anonymization and De-identification: To protect individual privacy, it is crucial to anonymize and de-identify personal information and reduce the risk of re-identification.
Fairness and Bias Mitigation: Companies must be aware of and mitigate biases in the data. Make decisions and take action steps that are fair, unbiased, and based on the data.
Data Governance and Accountability: To ensure compliance with relevant regulations and standards, it is essential to define roles and responsibilities, ensure compliance, and regularly audit data practices.
Responsible Data Use: When using an individual’s data, make sure to use it in a way that benefits individuals, organizations, and society. This must be done while also ensuring privacy and avoiding harm.
Continuous Evaluation and Improvement: To use data ethically, conduct ongoing evaluations, and work to improve data practices. Regular assessment of processes, ethical concerns, and regulation adaptations should be ongoing.
Taking the steps above can help to ensure individual privacy rights and trust in data-driven practices.
Leveraging Dark Data
Break down Silos: When a team creates data that another team does not know about, this can create a silo within an organization. Once silos are broken down, this makes data available to other team members.
Improve data management: To effectively manage data, it is essential to understand what data exists. This can be done by classifying all data within the organization. Teams can then organize data in accordance with the company’s needs.
Set data governance policies: Set policies that cover review processes for incoming data and offer clear guidelines for data retention, archival, and destruction. Be strict about what data should be destroyed and when. Enforcing data governance and review practices will help minimize the amount of dark data that will not be used.
Use ML and AI tools to parse data: Machine learning and Artificial Intelligence can analyze and categorize the data. ML automation helps with data privacy and compliance regulations in part by redacting sensitive data.
The Data Age
In the ever-competitive markets of today’s world, data is said to be king when appropriately utilized. Dark data can revolutionize the supply chain industry through valuable insights, improved decision-making, and risk mitigation. Despite all the potential for good, it is still critical to be careful when storing and analyzing data.
With all the potential for good, there is also significant potential for bad outcomes in cybercrime. Understanding dark data and being proactive is critical to business success in the ever-evolving supply chain management landscape.