Kangaroo Court: Data Governance

Association of Certified E-Discovery Specialists (ACEDS)
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Association of Certified E-Discovery Specialists (ACEDS)

Never has a robust information governance solution been more important than today. The explosion in new and varied form of data has long required a more sophisticated approach to understanding and managing data. If 2020 taught us anything, it is the need for enterprise-wide digital readiness. We need look no further than the rapid shift of entire organizations to cloud based collaboration platforms like Slack and Microsoft Teams. Organizations may have been successful in making the transition to utilizing these tools, but there was minimal preparedness for analyzing chat data in the event of litigation.

Without a sophisticated information governance framework, organizations will continue to find themselves stilted when attempting digital transformation initiatives and new forms of analytics. Good data governance ensures that organizations can pursue domain centric initiatives for capturing the hidden value within a digital ecosystem.

Delaying these initiatives creates problems that go beyond missing out on future analytical wins. There are existing pressures that are screaming for a better way of managing these exhaustive data loads. For example, data processing and cleanup can consume more than half of an analytics team’s time. This includes that of highly paid data scientists. There is also a scalability issue which often frustrates employees and creates wider organizational issues. For data governance to be effective, organizations must think of their data as an asset class to be treated with care.

In McKinsey’s 2019 Global Data Transformation Survey, respondents reported that an average of 30 percent of their time was spent on non-value-added tasks because of poor data quality and availability. The survey found that leading firms eliminated millions of dollars in cost from the data ecosystems and enabled digital and analytics use cases worth millions or even billions of dollars. The results revealed that data governance was one of the top three differences between firms that capture this value and firms that do not. In addition, it was identified that firms which underinvested in governance exposed themselves to severe regulatory risks.

A successful data governance initiative requires buy-in from business leadership. This means a clear understanding of the inherent value data provides as an information asset class. However, acknowledgement is only one of many steps required to reliably extract value from data. The establishment of internal stakeholders in the form of a data management office and supporting groups need to define which data initiatives should take priority and the appropriate milestones required to ensure each initiative succeeds.

When people are excited and committed to the vision of data enablement, they are more likely to help ensure that data is high quality and safe. This means instituting appropriate change management initiatives to grow supporters, develop internal champions, and work to covert skeptics. The requirement of mobilizing people and instituting change can be one of the most difficult parts to any enterprise-wide initiative, so a clear process of education is essential to ensure milestones can be met for each initiative as it occurs.

There is an additional pressing need for organizations to prepare their data for the application of AI tools at scale. Companies seeing the highest bottom-line impact from AI exhibit overall organization strength and engage in a clear set of core best practices. This includes better overall performance, better overall leadership, and a resource commitment to AI. AI high performers have a road map clearly prioritizing AI initiatives linked to business value across the organization. They have a clearly defined AI vision and strategy, beginning with the identification of high-quality data as part of their information governance framework.

AI high performers commonly implement an active program to develop and manage an extensive range of AI ecosystem partnerships. LegalTech firms cannot achieve this herculean task on their own. Legal service providers and law firms must do their part to teach their corporate clients and provide solutions that seek to enhance their AI initiatives.

To create the foundations of an effective data governance structure, three core components must be established.

  1. The creation of a central office for managing and understanding data, typically led by a member of the c-suite tasks with overseeing the process. This should include a target data strategy that defines the overall direction and standards that need to be achieved.
  2. Within each department, governance roles need to be organized by data domain where the day-to-day work is done.
  3. A committee which brings together the central office and domain leaders to ensure that priorities and the strategy are achieved with appropriate funding.

Corporate legal operations and litigation teams manage enormous amounts of data, often without enough hands to complete the extensive list of tasks at hand. There is an opportunity for law firms and service providers to grow their relationship with their corporate clients by taking the lead on these initiatives.

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