The subject of reusing data by organizations that are involved in serial litigation is a popular topic of discussion, and often is seen as ideal, if not often practiced. It is more of a necessity these days because organizations are doubling their data footprint annually, resulting in billions being spent on managing big data each year.
A survey conducted at the 2013 CGOC Summit revealed that organizations are reluctant to eliminate unnecessary data because they:
- Do not know where to start
- Cannot bring all information stakeholders to the table
- Are unable to demonstrate the urgency
- Cannot clearly demonstrate negative cost and risk impacts
- Cannot build a compelling business case
The result of retaining unnecessary data means that significant costs are being unnecessarily expended to identify, preserve, collect, review and produce data that would not be required, but merely because it exists it has to be part of the process. Because of this, the eDiscovery market is one of the “beneficiaries” of organizations dealing with vast amounts of data. It’s time for organizations to get to know their data before crisis strikes, and find ways to reduce their data footprint so they are able to efficiently manage only the necessary and relevant data across the entire enterprise.
One way to help cut costs of eDiscovery is having a reusable data policy – but there are other steps you can take throughout the EDRM. Download this eBook to see what specific costs you can start shaving today →
Data reuse for purposes of eDiscovery efforts can best be described as “the use of data collected and used for litigation that can then be leveraged for additional needs around new litigation efforts”.
There are many ways an organization can benefit from focusing on the capture and synthesis of prior decisions, and work product previously applied to data during work efforts.
Benefits of Data Reuse in eDiscovery
Repeated data acquisition and handling results in unnecessary costs. Leveraging the same data can lower your litigation and discovery costs. If you understand your organization’s data landscape, and implement information governance and records management policies, it will enable you to only manage the necessary data within the organization.
Working with data that is repeatedly collected for litigation and associated with discovery efforts comes with associated risks, when not managed correctly. Apart from the increased costs, data collected for litigation purposes that may have been used in prior efforts can result in different decisions being made.
If you understand your organization’s data landscape, and implement information governance and records management policies, it will enable you to only manage the necessary data within the organization.
Risks associated with inconsistent decisions being made can result in defensibility challenges to the eDiscovery process, sanctions, privilege decisions being overturned, all in addition to the obvious cost impact.
Being able to fully comprehend and understand prior analysis and use of data enables organizations to ensure consistency in data analysis and handling.
Identifying Data to Reuse
Wanting to reuse or repurpose data and understanding the benefits is one thing; but being able to understand what data can or should be identified for reuse, as well as how best to comprehend and distribute the useful knowledge gleaned from the data is another.
Reduce Your Data Footprint:
It is imperative that organizations work through the process to clear out their data stores, effectively leverage technology to manage what is left and develop a centralized location that can store, manage and support multiple data use needs.
In survey by the Compliance, Governance and Oversight Council (CGOC) results indicated that approximately:
- 1% of organizational information is subject to legal hold
- Only 5% is held pursuant to a document classification schema
- 25% relates to a business need
- The remaining 69% has no legal or business value
4 Questions to Help Start Your Data Reuse Program
Here are some questions to get you started making decisions regarding the time and costs around data reuse programs:
1. How much litigation does the organization get involved with on an annual basis?
Understanding your litigation exposure will help you get a better idea of how comprehensive your data reuse efforts need to be. It will help whether you are in a heavily regulated industry, an industry that is litigious by nature, or simply evaluating the litigation history for the organization over a certain period of time
2. Is there a record of “frequent flier” custodians that are key targets for repeated legal holds, preservation, collection?
Conducting an analysis of who has been involved as custodian sources for legal hold, litigation and discovery efforts, will enable you to identify those sources that are key targets.
Key targets can be prioritized for data management, data reuse, and data analysis efforts.
3. What potential impact does litigation have on the organization’s bottom line?
One of the main factors in determining how to approach a data reuse strategy for an organization is to understand the overall impact of litigation.
The impact can be multi-faceted, financial, as well as public perception. The financial impact on the organizations bottom line can be objectively quantified. The impact on how litigation effects an organization’s public perception is more subjective and may or may not directly relate to the financial impact. Litigation may not be financial substantive but may be highly public and as such impact’s the organization directly.
4. How have prior data sets been managed?
Evaluate the prior process for how data has been handled, what where the pros and cons, and what were the results. Where is the data currently residing and how can the data analysis, work product etc., currently associated be harnessed for future requirements.
The benefits of implementing a data reuse policy will result in the key ability to efficiently manage, find and understand your data. Additionally, avoiding unnecessary and costly duplicative handling of data as well as minimizing risks around data analysis and data exposure all lead to effective data management.
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