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Are We Focused on the Right Technology to Streamline eDiscovery?

Everyone is talking about generative AI these days. Its impact on society overall is potentially transformative. Its impact on eDiscovery is also potentially transformative, especially as it relates to streamlining review. However, concerns about hallucinations with generative AI models have caused eDiscovery professionals to be cautious about how to approach rapidly evolving technologies like generative AI and large language models (LLMs).

It’s understandable that legal professionals have been so focused on streamlining review costs when review has historically been as much as 70 to 80% of the total costs of an eDiscovery project. However, streamlining review doesn’t streamline other costs leading up to review – like collection and hosting.

To streamline any life cycle – including the electronic discovery life cycle illustrated in the EDRM model – the best place to start is at the beginning. And the beginning is where the data lives.

The Organizational Benefits of Indexing in Place

To streamline from where the data lives, you must understand the data you have, which requires the data to be indexed in place. Indexing the vast variety of your organization’s data in place might seem like a goal that’s out of reach, but the technology exists today to index data across your organization and access it from a single platform.

Indexing data in place offers several overall benefits to organizations, such as:

  • Reduced Siloes of Data: Data within many organizations is often siloed and difficult to access, making finding the data you need efficiently a significant challenge. Indexing your organization’s data in place reduces the siloing of data and ensures that even remote data stores within your organization can be searched quickly and effectively. It also facilitates the ability to create and maintain organizational data maps.
  • ROT Reduction and Dark Data Identification: Indexing data in place facilitates identification of Redundant, Obsolete and Trivial (ROT) data that can be defensibly deleted. It also helps organizations identify “dark” data and determine the value of that data so that it can be addressed within the organization. As much as 85% of data within an organization can be ROT data (33%) or dark data (52%).
  • Identification of Personal or Other Sensitive Data: Indexing data in place also facilitates identification of personally identifiable information (PII) or other sensitive data. This streamlines the process for responding to Data Subject Access Requests (DSARs) and other data privacy requests from individuals. Identifying personal and sensitive data within the organization also enables organizations to be more targeted in their data protection strategy, which minimizes the potential impact of a data breach.
  • Efficiency and Cost-Effectiveness: The ability to reduce ROT data and identify dark, personal, and sensitive data enables the organization to be more efficient in its data-oriented tasks, which makes data discovery a more cost-effective process overall. It also expands the potential for reuse of important data in innovative ways to provide insights for decision making within the organization.


The estimated volume of global data created is expected to reach 181 zettabytes (which is 181 trillion gigabytes!) by next year and data is doubling in organizations every two years. Information Governance (IG) policies can’t keep up with the enormous growth of data without leveraging technology. Index in place technology is the fuel to your organization’s IG engine.

How Indexing in Place Helps Streamline eDiscovery

The benefits of indexing in place discussed above also lead to benefits that help streamline the entire eDiscovery life cycle – not just review. Here are some of those benefits:

  • Proactive eDiscovery: Information Governance is part of the eDiscovery life cycle, so all the benefits discussed above – including reduction of ROT and dark data and identification of personal and sensitive data – provides a “jump start” to any eDiscovery project.
  • Earlier ECA: Traditionally, to conduct Early Case Assessment (ECA) to assess the merits of a case and determine its viability, you had to collect and ingest the data into a review platform before you could even begin to assess the data and make key case decisions. With the data indexed in place, you can begin ECA immediately.
  • Responsive and Timely Legal Holds: A greater understanding of your data up front helps your organization implement legal holds more quickly and effectively, reducing the potential of data spoliation that could lead to sanctions in litigation.
  • Reduced Collection, Hosting and Review Costs: The ability to search the data where it lives enables your organization to cull clearly non-responsive data at the beginning of the eDiscovery life cycle, enabling you to perform targeted collections instead of collecting the entire corpus of relevant custodians. This reduces the volume of data being collected and hosted, which reduces both costs and reduces review costs as there is less data to be reviewed.
  • Reduced Data Redundancy: eDiscovery has traditionally created lots of redundant data, as collection creates a copy of the data being collected. Collecting and hosting less data reduces data redundancy, which not only reduces costs, but also reduces cyber risks associated with multiple copies of large volumes of data.


The growth of data in organizations has turned routine investigations and litigation cases into large investigations and cases – and the investigations and cases formerly considered large to become XL, or even XXL! Indexing in place streamlines the management of data in eDiscovery throughout the life cycle of the project, enabling even the largest investigations and cases to be managed efficiently.

Are We Focused on the Right Technology?

Generative AI is not only generating content that can be useful to individuals and organizations everywhere, but it’s also generating a lot of buzz everywhere as well. Many eDiscovery companies have touted generative AI technology and large language models as the “next big thing” to reduce eDiscovery costs, especially in review. The potential exists for generative AI to make a significant impact, but there are still questions to be answered about the accuracy and reliability of genAI models, as well as the costs for training and using them.

On the other hand, index in place technology has a proven track record within organizations in facilitating information governance programs and streamlining data discovery within organizations. It may not be generating as much buzz, but index in place technology can offer proven benefits to organizations throughout the eDiscovery life cycle and overall.

For more regarding Trustpoint.One’s Information Governance capabilities, click here.