Slack data import and review.
Role: IC Lead Product Designer
Cross-Functional Team: Product, Engineering, Data science, Executive team, Sales, Customer Success.
Problem: Legal teams face difficulty efficiently handling Slack's complex data for discovery.
Solution: Logikcull enables rapid, secure, and efficient discovery of Slack data through API ingestion and intuitive review tools.
Result: Slack data is now a strategic asset, not a liability, enabling legal professionals to swiftly and securely find crucial evidence.
Legal teams face difficulty efficiently handling Slack's complex data for discovery.
Slack's Widespread Use:
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Over 30 million daily users.
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Used by small businesses and over 65% of Fortune 100 companies.
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Serves as a workplace communication hub.
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Workplace Instant Messages (IMs) are increasingly vital evidence in lawsuits.
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Extracting and analyzing Slack data for legal discovery is complex.
Technical Challenges :
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Slack data is primarily in JSON format, which is massive and intricate.
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A single message can generate pages of complex code.
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Manual review of this data is nearly impossible.
Security and Efficiency:
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Exporting and storing Slack data locally raises security concerns.
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Directly ingesting data into specialized review platforms is more secure and efficient.
Specialized Solutions needed:
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Legal professionals require platforms designed to process Slack's JSON data seamlessly.
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These platforms should transform data into a user-friendly and easily reviewable format.
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This will streamline the legal discovery process.
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This will ensure critical evidence is effectively collected and analysed.
Pre Logikcull: Slack JSON example
User research validated the need for Logikcull's Slack data solutions.
Start with the WHY
Thanks to Slack, more businesses are moving communication away from email and into chat. But many legal professionals don’t know how to handle Slack data in litigation. Legal teams are used to documents, not chat. But chat is taking over.
To ensure our product roadmap and feature creation align with customer needs, we actively solicit feedback through various channels, including direct customer interactions and communication platforms like Intercom. We gather initial insights from these sources and from our internal teams (Customer Success, Support, and Sales) who directly engage with customers. This valuable information is then consolidated within Productboard, a platform that enables us to effectively prioritize features based on their potential impact (Reach, Impact, Confidence, and Effort) – a framework commonly known as RICE scoring.
As a designer and member of the product team, I have access to a valuable pool of enthusiastic and collaborative users. This includes accounts and users who have expressed interest in handling Slack data within our product, as well as my network of users with whom I have cultivated strong working relationships over the years, including members of our customer advisory board.
To gather in-depth user insights, we utilise a public Google Calendar link. This allows users to schedule 1:1 or group calls with me and other members of the product and design team. These research calls provide a valuable platform for users to share their needs, pain points, and specific requests. Simultaneously, they offer us the opportunity to share early ideas, workflows, and low-fidelity designs.
Early-stage research calls played a crucial role in understanding the true scale and impact of the Slack data challenge. By directly hearing from users and leveraging existing design UX patterns, we were able to move forward quickly and begin exploring potential solutions.
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As a user, I want the ability to integrate with Slack data to avoid the manual "export from Slack and import to Logikcull" steps.
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As a user, I want a direct integration with Slack engagement cloud to perform a more targeted collection.
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As a user, I want to discover as many file types as Slack provides.
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As a user, I want to discover the types of conversations a custodian might have had.
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As a user, I expect synchronisation to be possible throughout the entire organisation.
Rapid, collaborative development delivered a streamlined Slack import.
Understanding what we had and how best to present it.
This project was a great example of successful collaboration. Through rapid iteration and close communication with data science, engineering, and product teams, we quickly identified the most valuable Slack data to prioritise for phase one of this release. We focused on delivering value quickly, establishing a strong foundation for future enhancements.
Working closely with the data science team, I analysed the available data and identified meaningful groupings to facilitate user interaction and understanding.
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To design a user-friendly multi-step wizard for Slack imports, I analyzed user requests and the available data from the Slack API. This involved grouping the data effectively for easy user interaction and selection. The following summarizes the required and available data:
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Logikcull's Slack import simplifies data ingestion with API access and a guided wizard.
Below you will find the final designs for Phase One of the 'data import' and 'search review' stages within this user flow. When redesigning how users import Slack data in Logikcull, we prioritized maintaining existing functionality. Recognising that different organisations have varying levels of access to the Slack API, we ensured the continued availability of a manual data transfer option. Simultaneously, we provided users with the option to 'upgrade' their experience by connecting directly to the Slack discovery API for a more streamlined and efficient import process.
When selecting the Slack Discovery API, users are redirected to their Slack workspace for authorisation. Depending on their organisation's settings, this step may have been pre-configured by an account owner, allowing some users to bypass this step.
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Once connected to the Slack discovery API, users are presented with a multi-step wizard to begin to customise their import. Due to the complexity of the overall import flow, we decided to utilise a stepped wizard to reduce user overload and minimise the complexity of creating an import. The import was divided into four simple steps with each step focussing on one area. Those steps are shown below and are as follows: Workspaces, Participants, Conversation types and upload. Use the arrows below the image to view each step.
Logikcull enables fast Slack data searches with intuitive, chat-specific filters.
Once a user uploads their Slack data, they gain access to Logikcull's highly effective and user-friendly search facets, renowned for their 'powerful simplicity.' Building upon this proven design pattern, we expanded our facet offerings to specifically address the challenges of filtering and visualising Slack data. We introduced chat-specific options, enabling users to quickly and efficiently refine their search results within this new data source.
By selecting desired options within these facets, users can quickly and efficiently narrow down potentially massive datasets containing millions of documents. To ensure future scalability and accommodate the integration of other chat platforms (e.g., Microsoft Teams), we intentionally labelled these facets as 'Chat' rather than 'Slack,' maintaining a consistent and flexible design pattern.
By collaborating closely with our users, we identified the most valuable search facets for their specific needs. Some were more intuitive than others. Our goal was to seamlessly integrate these chat-specific facets into our existing customisable search environment. This allows users to easily add or remove facets based on their preferences, ultimately enabling them to create a tailored search experience for their chat data.
With this user-friendly interface, users can now efficiently search through their Slack data without the cumbersome task of manually sifting through pages of raw JSON files. The interface is intuitively designed and populated with data directly imported from Slack, providing a much more streamlined and efficient search process.
Logikcull offers fast, secure Slack discovery via API and intuitive review tools.
1. Easily collect Slack data with just a few clicks.


2. Get all chat conversations from relevant custodians directly from the source and start reviewing them right away in a format that’s easy to understand.
3. Find “smoking guns” fast. Using relevant keywords is not enough in an ecosystem filled with non-verbal communication like emojis and GIFs. Chat filters help users quickly surface the important conversations, reactions, and attachments buried in the Slack morass — no matter where they hide.
4. Eliminate the risk of saving exported Slack data on their local drive by ingesting it directly into Logikcull where it’ll be protected with the highest security standards.
User-focused Slack updates provided a model for expanded chat data solutions.
Following the successful launch of the Slack API data import and improved search review, we continued to collaborate closely with our users to identify further areas for enhancement. A key area of focus was the in-application display of chat data. Users expressed a strong desire for a more 'native' chat view within the document viewer, which we promptly began developing.
Furthermore, user research revealed a significant need for the ability to send and confirm legal holds directly within Slack. We were proud to be among the first in the industry to implement this innovative feature.
With the exponential growth of chat data across platforms, our work with Slack provided a valuable foundation for scaling our capabilities to other platforms, such as Microsoft Teams. This has opened up significant opportunities while consistently upholding our commitment to providing users with a powerfully simple and helpful experience.