As AI becomes ever more common, governance becomes a greater issue. Billions of people are effectively using AI every day, from AI-powered Google Search to AI-driven social media feeds.
At the same time, we’re all giving away massive amounts of personal data to train these models. Thus, establishing robust governance for data and AI is an imperative. However, traditional governance policies often struggle to effectively oversee rapidly evolving technologies like generative AI.
To build trust and extract full business value, enterprises need to rethink governance approaches for the AI-first era. This article explores leading practices to govern massive datasets and powerful models that are driving next-generation analytics capabilities.
Unified Solutions for Data and AI Governance
As organizations increasingly rely on data and AI, the need for robust and unified governance becomes critically important. Companies like Databricks have partnered with Cleanlab to bring data improvements modern data architectures that consolidate governance across different types of data stores and AI systems.
These improvements allows data engineers, scientists, and analysts to focus their efforts on deriving value from data rather than just managing it, which provides seamless collaboration across the organization so that any team can easily discover, understand, and leverage existing data for their projects. This eliminates redundant copies of data that drain storage and compute resources.
Overall, purpose-built unified data governance solutions allow organizations to thrive in the AI era by providing the foundation for responsible scaling. With consolidate governance, teams can develop AI confidently knowing that policies for monitoring data and models are consistently applied across the organization. This is the type of unified approach needed to balance rapid innovation with governance.
Elements of a Successful Governance Framework
Given the wide range of potential use cases for data and AI across most organizations, a flexible and comprehensive framework is essential for effective governance. The framework needs to accommodate simpler business intelligence needs like sales reporting as well as complex machine learning models that inform decision making across the company.
Ideally the governance protocols are tailored to the type of architecture the data and AI resides on. For example, the data lakehouse has emerged as the new standard data architecture because it combines the best elements of data lakes and data warehouses. This hybrid approach delivers agility, performance, and low cost at scale.
As a result, governance frameworks designed specifically for the lakehouse environment account for its distributed nature and enable unified data and AI governance across cloud-based object stores and traditional warehouses.
At the same time, the governance framework must consolidate standards, policies, and procedures for both data management and AI development lifecycles rather than taking a siloed approach. Cross-functional collaboration is table stakes in the AI era, so bringing data, analytics, and engineering teams together under shared governance helps break down knowledge silos. The frameworks should outline general principles for transparency, privacy, and ethics while allowing customization to address domain-specific issues as needed.
Striking the right balance between high-level guardrails and tactical implementation procedures is critical for successful adoption across the organization. Standards around model risk assessments, ongoing monitoring, and model retirement should facilitate innovation rather than hinder it through excessive controls. Integrating human review checkpoints at critical milestones provides the benefits of governance without sacrificing speed.
The Value of Effective Data Governance
High-quality data and governance serves as the foundation for core business capabilities that drive competitive advantage in the modern enterprise. Effective data governance in particular ensures high data quality and availability such that employees can extract maximal value. It also enables data democratization across the company so any team can build on existing data rather than starting from scratch. This amplifies productivity and innovation.
On the other hand, ineffective data governance often stems from a lack of executive recognition of data as a critical business asset on par with financial capital, products, factories, equipment, etc. With inadequate leadership prioritization and strategic planning around data, organizations suffer from inconsistent policies, fragmented ownership, and general low-quality data.
These deficiencies then lead to significant wasted resources as employees spend excessive time locating datasets, cleaning data, and debating access policies. Data scientists can devote upwards of 80% of their time just finding, preparing, and managing data. And poor data practices expose companies to security breaches, ethical violations, and regulatory non-compliance - all substantial business risks.
So in summary, mature data governance ensures availability of high-quality, timely data that ultimately enables employees to create value through enhanced productivity and innovation. Leadership recognition and investment into data management accordingly pays significant dividends across the enterprise. On the other hand, deficient governance results in wasted resources and opportunity costs that can cripple an organization’s competitive positioning.
Organizational Design and Critical Practices for Governance
To enable effective governance, organizations should consider implementing specialized data management roles similar to traditional corporate functions like finance, sales, and marketing. Centralized data offices help coordinate policies, standards, metrics, and tools across business units and functions. These offices go by names like Chief Data Office or Global Data Services.
Within business units, organizations should aim to develop data stewards and domain data experts for different subjects like customer, product, risk, and finance. These domain data leaders shape standards and practices within their decentralized data domains. Staff data scientists, analysts, and engineers then leverage this domain expertise and feedback to build data and analytics products tailored to business needs.
Finally, a data governance council with executive membership provides strategic approvals over data plans and investments. This council ratifies and sponsors enterprise-level data policies, standards, and metrics based on feedback from data domain stewards. They also adjudicate conflicts of interest across business units. Together, these three facets of people, process, and technology coordinate effective governance and value creation from data assets.
Securing Top Management’s Attention
Driving real change around data and analytics requires getting executive leadership attention focused on data as a business priority on par with more traditional assets. This means framing data governance and management as vital to enterprise strategy rather than just an IT concern.
The process starts with assessing the current data culture and political landscape across the organization to identify gaps and opportunities. Then data leaders can craft targeted messaging and data products that resonate with executives based on their interests and motivations. Rather than leading with technical concepts, effective framing addresses business risks, opportunities, and competitive standing.
Financial impact quantification also always garners executive attention. This educational campaign both enrolls individual executive sponsors as well as institutionalizes data governance through official charters, revised policies, and funding allocations. Sustained senior leadership support enables the launch and success of enterprise-wide data governance initiatives.
Applying the Right Level of Governance
Effective governance stems from balancing structure and controls with flexibility and autonomy as appropriate for the organization. Excessive policies and limitations slow innovation while no oversight at all increases business risk. The right governance also elevates data quality and consistency but avoids a single mandated standard that prohibits optimization and specialization for different business needs. For example, customer data needed for personalization systems requires more precision than aggregated outputs used for market sizing.
These competing concerns get balanced based on assessments of organizational data maturity and risk tolerance. Centralized guardrails around security, privacy, and ethics establish enterprise norms. Decentralized data domain policies then codify standards, procedures, and quality metrics tailored for their specific needs upon which data engineers and scientists design compliant and useful data products.
Governance frameworks additionally get tailored to company size and culture. Large bureaucratic institutions may require more oversight processes to align autonomous teams whereas startups incorporate informal collaboration and iterative development. Undergoing maturity and culture assessments allows organizations to implement the appropriate level of governance to maximize business value.
Iterative and Focused Implementation
The most successful governance initiatives start small in scope to demonstrate quick wins and value. This proof then justifies additional investment to expand governance coverage across more data domains and use cases.
For example, focusing first on customer data integrity makes an immediate impact on marketing campaign performance and customer satisfaction. Starting with hot spots that currently impede business performance concentrates resources on the most pressing needs before addressing less critical domains. It also allows data teams to hone governance processes on simpler challenges before graduating to company-wide implementation.
Instituting data governance spans technology, process, and organizational change management. By adopting agile principles, data leaders can roll out capabilities iteratively to match business needs rather than getting bogged down trying to launch comprehensive systems. Iterative delivery also allows incorporating user feedback to ensure governance protocols remain helpful rather than hinderance.
Maintaining a project backlog allows fluid prioritization based on shifting business needs while incrementally driving maturity through successive iterations. Quick wins sustain momentum while tactical governance gets embedded across all facets of data management.
Generating Excitement for Data
Effective data governance requires getting employees outside the data function engaged, excited, and bought into its value. But skepticism remains high as many staff lack exposure to modern analytics and data science techniques. They require education on governance goals and how controls will improve access and data quality rather than restrict it. Hands-on participation also increases advocacy as people apply new data and analytics proficiencies back on their own jobs.
Data teams should actively identify innovators across the business who can serve as pilot users and test cases for data governance capabilities as they roll out. These power users provide feedback to enhance the protocols and platforms. And their vociferous advocacy helps market the tools and convert skeptics in their domains. Creative messaging campaigns with incentives and celebrations further engrain recognition of data contributions as part of the cultural fabric.
Ultimately about 30-40% of employees become active supporters who enthusiastically participate in governance programs for their own benefit while about 60% passively comply but need incentives. The lingering holdouts require either more focused education or transition off projects dependent on governed data. Investing resources into thoughtful change management and engagement sustains excitement for governance efforts over the long haul. The process establishes data quality and security as a responsibility shared across teams rather than just the data function.
The Importance of AI Governance
As artificial intelligence capabilities have grown more powerful thanks to advances in data, cloud compute, and algorithms, excitement around AI’s potential has skyrocketed. However, risks stemming from ungoverned AI also multiply in parallel.
Both internal policies and external regulations require organizations deploy AI responsibly, with accountability and oversight to address biases, safety issues, ethical concerns. Companies who fail to implement principled AI governance face substantial brand, legal, and financial repercussions from problematic models.
Internally, uneven AI governance leads to models that conflict with corporate values around fairness, interpretability, and privacy. External parties including customers, regulators, and media then punish brands that allow biased or exploitative algorithms. For example, Apple Card faced allegations that its credit model discriminated by gender while TikTok received criticism for manipulating addictive content to vulnerable demographics.
In both cases, the lack of established governance practices to audit for and address model risks earlier resulted in deployment of flawed algorithms. These types of AI debacles batter brand reputations and employee morale which then negatively impact sales, recruiting, and retention. Cleaning up problematic models requires resource investment as well into audits, re-training, monitoring upgrades and so delays capabilities getting to market.
Externally, violations of evolving regulations around AI transparency and accountability like the European Union’s Artificial Intelligence Act result in hefty financial penalties. Worldwide, there are dozens of unique regulatory approaches to AI governance.
Image by UNICRI https://commons.wikimedia.org/w/index.php?curid=139489438
Overall, taking an “Ask Forgiveness Not Permission” stance by skipping governance reviews multiplies legal, ethical, and financial risks.
Balancing Control and Autonomy in AI Governance
Scaling AI responsibly requires finding the right balance between too much governance that limits innovation velocity versus too little control that permits unacceptable risks. Organizations understandably want to encourage experimentation that drives breakthroughs but not at the expense of safety, fairness, and compliance.
Unfortunately, top-down policies mandating approvals for developing, testing, and monitoring algorithms often hamper productivity for data scientists. Excessive red tape disincentives innovation especially around emerging techniques like generative AI that defy existing governance paradigms.
Leading platforms facilitate building, deploying, and monitoring machine learning models embed guardrails to automate governance best practices at each project phase. For example, during model development, predefined data quality checks automatically flag potential issues for review rather than denying outright access to sensitive datasets.
These automated safeguards allow data teams to maintain momentum while still adhering to data compliance standards around subjects, geographies, and use cases. Data scientists then attest to addressing flagged issues which gets logged for auditing rather than requiring pre-approval paperwork.
For model deployment, platforms auto-generate model cards that document performance metrics, fairness assessments, concept drift triggers, and dependencies. Attaching these metadata cards directly to the algorithms themselves ensures governance moves with the model post-development. Finally, embedded monitoring continuously scans production data and pipelines to detect signals like skew that indicate when models start decaying. Automated alerts notify owners to intervention rather than just relying on manual oversight.
Across the project lifecycle, purpose-built AI platforms harden adherence to governance best practices directly into the technical architecture data teams use rather than imposing external controls that sacrifice speed and agility. This empowers autonomy under the hood of responsible scaling to drive innovation safely. Rather than compliance coming at the expense of innovation or vice versa, integrated governance enables both simultaneously.
Takeaways
As AI and data-driven technologies increasingly permeate every aspect of modern enterprise, the necessity for robust and unified governance frameworks becomes paramount. By aligning data governance with AI development and deployment, organizations can ensure that innovation progresses responsibly, balancing agility and compliance. With the integration of unified data governance solutions and specialized roles, companies can foster a culture that values data as a critical asset, driving innovation while maintaining high standards of privacy, ethics, and transparency. This approach not only mitigates risks associated with data and AI but also amplifies the potential for these technologies to deliver transformative business value.
Ensuring proper Data Governance can require lots of work. Solutions like Cleanlab can help by automatically understanding each data point in a massive dataset to streamline governance efforts. For instance, Cleanlab Studio can auto-detect text/images that are low-quality or unsafe content (including PII). If your team labels a few examples of sensitive data that requires particular treatment, Cleanlab Studio can auto-label the rest of your massive dataset for you instantly. Cleanlab Studio can also auto-detect errors your team made in such governance-labels, to ensure your organization remains compliant.
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