Shedding Light On Dark Data: What Is It?
Ever heard of “Dark Data”? It may appear mysterious, but it is not. Instead, the term implies to data that is collected but not analyzed. However, if we use it effectively, it can benefit us in many ways. Well in this article, we’ll talk about what dark data is, the various forms it can take, the risk and compliance, how to find, store and most importantly, how to protect it.
What is Dark Data?
“Dark data” refers to untapped digital information that organizations collect, process and store but don’t use. It is referred as “digital shadows” hidden in an organization’s database. Gartner Inc. defines dark data as the “information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing).”
Besides collecting the data for analysis, businesses collect a whole lot of other data known as” Big Data”, close to 90% of the available data is dark data. For example, when you log into your online banking account, shop online or order food online, your activity generates dark data, like customer clickstream data, abandoned shopping carts, customer reviews, search histories, and more. However, only a part of this data is held for compliance reasons and the rest is disregarded, underutilized, or simply ignored, probably leading to potential risk. Let’s investigate how.
The Risk and Implications
Dark data comes with its own set of challenges and retaining this unused data can be risky and expensive to store. Unmanaged dark data can result in legal issues and a data breach involving dark data which can harm an organization’s reputation.
So, it’s important to focus on using it effectively rather than just finding or storing it. When analyzing valuable data, it’s important to know that correlation does not imply causation. In other words, making decisions solely based on data, without clear cause-and-effect relationships will lead to wasting resources and strategies. With the rapid growth of big data, the amount of hidden dark data is also increasing. Let’s take a closer look at the various types of dark data.
The Various Forms of Dark Data
Unstructured, Redundant, Obsolete and Silent Data are the various forms of dark data. This data is often unstructured and can include everything from email archives and server logs to customer feedback forms, downloaded attachments, call records and social media interactions.
Dark data has the potential to generate new revenue, lower waste, cut cost. Well then how can we discover the hidden data? Here are some key points:
Ways to Discover Dark Data
- Data Inventory – Compile your data sources within the organization.
- Data Classification – Categorize and prioritize data if valuable.
- Data Profiling – Employ data profiling tools to identify anomalies, patterns & hidden insights.
- Data Sampling – Use this for extensive dataset analysis.
- Data Exploration – To evaluate unstructured data, use natural language processing (NLP) or text mining.
- Data Auditing – Conduct assessments of Identity and Access Management (IAM) configurations to know how much data is inaccessible due to security measures.
- Data Usage Monitoring – Track data utilization across various departments, highlighting resources that are not fully utilized.
- Stakeholder Interviews – Interact with stakeholders about existing and future data usage.
- Automated Data Discovery – Incorporate data finding automated applications.
- Data Privacy Compliance – Ensure compliance with data privacy regulations.
- Documentation – Maintain a detailed record of data discovery efforts.
- Data Governance – Manage the newly discovered data according to importance labeled & protected.
- Regular Review – Identify & regularly review hidden dark data.
Advantages of Dark Data
Below is a funnel chart illustrating the key benefits of Dark Data.
Dark data can provide valuable insights to businesses. Analyzing it can help with compliance, cost savings, and data security. It can also improve customer experiences and increase revenue. Overall, mining this data can enhance decision-making, efficiency, and competitiveness.
Protecting Dark Data
Businesses use dark data AI initiatives to map their data resources and immediately take the right measures to safeguard stored data. AI Software can quickly scan millions of pages and identify sensitive information. It selectively edits or masks specific information within a document or dataset to remove sensitive details. Machine learning can identify suspicious activity, and deep learning models can avert threats. AI-guided encryption and access controls aid in securing delicate information. By detecting threats proactively, AI fortifies data security.
Transformation of Dark Data
AI plays a crucial role in safeguarding dark data. However, it’s essential to remember that AI’s effectiveness is bound by its training data and engineered capabilities.
TurtleBay Advisory Services is proud to announce its strategic alliance with RyAILITI knowledge engineering AI platform that tackles this limitation with innovative strategies. The practical approach allows to create models that blend different elements and sources, enabling more extensive data exploration than traditional systems.
These strategies are vital for their cognitive methodology to design information structures. By using qualitative expertise graphs and tapping the reservoir of dark data within the in-memory model library, bias analysis is made possible.
Overall, we aim to address challenges related to data discovery, utilization, bias analysis, and network management, offering solutions for more effective data-driven decision-making. To gain deeper insights into the success stories of the companies benefiting from our solutions, or if you require further information or assistance, we invite you to get in touch with turtlebayadvisoryservices.com. Your inquiries are welcomed, and we are here to assist you.
In conclusion, dark data represents both a challenge and an opportunity for organizations. As data continues to grow in volume and complexity, organizations must recognize the importance of shedding light on their dark data and implementing strategies to recognize its potential. In doing so, they can explore hidden resources that will propel them into a brighter and more data-driven future.
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