Mining Dark Data: A.I. Driven Conversation Enrichment and Analysis

Understanding Dark Data

Dark data is a vast universe of uncollected, unstructured, and untapped data, which resides in the messy corners of the digital world. From server logs and unused survey data to deep vats of old emails and meeting recordings. Dark data refers to the information assets organizations collect, process, and store during regular business activities but fail to use for other purposes, such as analytics, business relationships, or direct monetizing strategies.

Despite its omnipresence, most organizations overlook dark data due to the complexity involved in processing it. However, understanding and leveraging dark data can unlock powerful insights that propel businesses forward. This blog post delves into the intricate world of dark data, exploring its significance, the strategies for its utilization, and the tools necessary to transform this latent information into valuable business outcomes.

Dark data is typically accumulated as a byproduct of other processes and isn't used in decision-making processes. Gartner suggests that up to 80% of organizational data is dark, which could be anything from unused information collected in customer interactions to data from old projects that are completed and forgotten.

The first step toward leveraging dark data is understanding its sources and types. Recognizing the types of dark data your organization collects can clarify potential uses and the tools needed for analysis.

1. Chatbot Logs: Interaction data from intent-based systems and LLMs.
2. Voice Interactions: Audio and transcripts from voice assistants.
3. Messaging Transcripts: Chat logs from applications like Slack or other IM apps.
4. Survey Responses: Open-ended survey responses are a commonly ignored from of unstructured data.
5. Emails: Stored communications between customers and support or internally.
6. Support Tickets: Records of customer support interactions.
7. Meeting Transcripts: Zoom and other AI tools have made meeting summary’s and transcripts prolific.
8. CRM Notes: Updates and notes in CRM systems related to customer interactions.
9. Web Events: User actions tracked on web applications like clicks and form submissions.
10. Metadata: Timestamps, source, alerts, associated parties, and other additional tags are important context that should be associated with your unstructured data.

The primary value of dark data lies in the insights that can be gleaned from it. These insights can lead to significant improvements in several areas. Let’s walk through a scenario with someone accessing some of this dark data could be valuable.

The Reality of Dark Data

Maria, a customer service manager at a mid-sized e-commerce company, is under constant pressure about her team’s slow response times and negative customer satisfaction scores. She had tried various managerial strategies to improve efficiency, but none had moved the needle. Burdened with volumes of chat logs, email correspondences, and call transcripts, the challenge wasn't just the amount of data but the complexity of turning this unstructured information into new steps and actions for her team.

Traditional data analysis tools at Maria's disposal are primarily designed for structured data. SQL, Tableau & Power BI fail to provide insights into open ended text, capable only of numerical based insights with the ability to leverage annotations if they exist. Key word search & approximations of industry metrics did not tell her why a her customer behaved in a certain way. Maria’s team faltered when faced with the diverse and unstructured nature of conversational data her team collected daily.

Maria Mines her Dark Data

As a result of her need to access the dark data that her team generated, they worked together for countless hours manually sifting through data. Routinely tagging customer support interactions in excel and building monitoring dashboards in their industry standard BI tool did not unlock the reasons that drove the customers to respond in the way their tools reactively demonstrated.

To effectively utilize dark data, organizations need to implement specific strategies that involve both technological tools and operational adjustments:

• Implementing Purpose Built A.I.: Software tools & packages leveraging AI and ML advancements can automate the analysis of large datasets such as tagging large swaths of conversations with LLMs making it feasible to extract insights from dark data.
• Blending Data Sources: Often, dark data remains unused because it is scattered across different departments. Integrating these silos into one analytics tool to allow for centralized data analysis can significantly increase the usability of hidden data.
• Data accessibility: Rapid advancements in AI combined with new low and no-code tools has opened the door to allow everyone in an organization to access powerful data storytelling and visualizations.

Shedding Light on the Dark

Lets go back and see how Maria tackles her dark data issue the second time around.

Realizing the need for a more robust tech-enabled approach, Maria explored advanced analytics solutions capable of handling unstructured data types, specific to her support optimization initiative. She implemented a system that could enrich and tag her unstructured data using LLMs in order to automate her dark data processing. This new setup categorized inquiries and identified deeper customer reasoning trends based on Marias areas of focus that highlighted critical areas for improvement.

Armed with new knowledge about specific product queries resulting in major bottlenecks, Maria initiated targeted training programs and set up a real-time dashboard that showed what was going on AND was capable of explaining why. This allowed for dynamic resource allocation, ensuring that her team  was deployed more to focus on improving the product experiences that led to bottlenecks for customers.

The journey into the dark data mine taught Maria and her team valuable lessons about the power of unstructured data. Rather than spending time manually reviewing a small sample of data points, Maria tapped into dark data with A.I. powered analytics to understand the full context of all the customer interactions and improve her teams performance as a result. This story highlights the importance of having the right tools in place to convert massive amounts of latent information into high-ROI insights.

Begin Mining your Dark Data with Dashbot

At Dashbot, we’ve been on a mission to help companies unlock these insights hidden within their unstructured dark data. By leveraging the latest AI advancements to enrich raw unstructured data and processing that enriched text data into unique purpose build visualizations and dashboards. Dashbot allows you to layer in dimensions of metadata that are relevant for your specific workflow to unlock new perspectives on your customer interactions. Plug in your unstructured data and begin to mine your dark data!