IBM StoredIQ Suite vs. Relativity

IBM StoredIQ is great if you have a lot of unstructured data and you need to scan that all, especially for GDPR reasons.

Incentivized

Relativity is well suited for all the eDiscovery workflows. It's excellent for document review, coding, active learning, content and structured analytics, searches, persistent highlights, imaging, and burning redactions into productions. The error rates with processing in Relativity are a little compared to other eDiscovery processing tools in the market. It is solely for document review purposes legal scenarios or otherwise, but it is not a complete solution for building customized hosting nor neither it is a replacement for conventional websites.

Incentivized

• Search for any data.
• Processes any type of files.
• OCR works great.

Incentivized

• Searching for documents and native files with various terms and approaches.
• Tagging documents and native files for further review.
• Creating and downloading collections of documents and native files for depositions.

Incentivized

• Adding workstations to scan is too complicated.
• Server hardware requirements are quite big.
• Not so user-friendly interface.

Incentivized

• Very powerful tool, but does require a high level of expertise and head count to administer the product.
• If hosting yourself, requires investment in servers and ideally is housed in a data center
• Providers need to pay kCura a monthly user license fee for every user who has access to the tool. Providers can purchase blocks of users, but with a large amount of users on a case, it's cost that some external clients are not thrilled about

Incentivized

Great support from IBM whenever we have requested it

Incentivized

IBM StoredIQ has better features and pricing in comparison to Varonis

Incentivized

It has the ability to provide all discovery features under one roof. It is adept for working in the data discovery domain with efficiency. Globally renowned software with features that are not available in many tools. Automation is a plus and Active learning in relativity is the absolute example of its class.

Incentivized

• As the solution was not cheap, we tend to decrease the amount of unused data.
• Hopefully by using this tool we will avoid any GDPR fines.

Incentivized

• Saves time spent searching for documents and native files.
• Easy to download materials when needed.
• Can share collections of materials across team members in other offices.

Incentivized