Editorial
“Good governance depends on the ability to take responsibility … it has to be pro-people and pro-active. Good governance is putting people at the center.” — Narendra Modi
The Gist
- Governance drives AI success. Effective governance structures empower AI to deliver sustained innovation and success through strategic alignment and participation.
- Metadata enhances AI. Strong metadata management underpins successful AI deployment, enhancing content relevancy and consumer engagement.
- Ethical AI is essential. Emphasizing ethical AI practices ensures technology is used responsibly, aligning with corporate governance to avoid misuse.
Artificial Intelligence (AI) as an energetic force for change in our modern business content systems such as a DAM, PIM, CMS and ecommerce will accelerate the conversation between business and consumer. All the integration and interconnectivity between business applications strengthens the argument for strong and authoritative metadata, and for effective workflow management.
Businesses creating and disseminating brand and marketing messages and products will engage with the consumer community who will respond with shopping behavior, internet searches, assets and data such as reviews, comments, images, check-ins and other online actions. Data serves content as a connection between people, process and technology. Let's take a detailed look at AI data governance.
What’s your data-driven AI customer centric strategy? We want the data and the machines managing it to learn and do more, but we must provide them with good, quality data for them to do that. Good data = smart data = good learning = happy customers. But if the data delivered does not match the user expectations, then the efficiencies of a personalized and meaningful consumer experience are lost.
Data is the foundation for all that organizations do in business and how they interact with their customers. Data is proliferating, and that growth is only going to continue exponentially. As it multiplies, organizations need refreshed, enterprise-level approaches to systematically create, distribute and manage data for your brand and your customers. Data is the foundation.
AI Data Governance Is No Longer an Option
Governance is the process that holds your organization’s data operations together as you seek to become truly data-driven, realize the full value of your data and content, and avoid costly missteps. To be effective, AI data governance must be considered as a holistic corporate objective establishing policies, procedures and training for the management of data across the organization and at all levels.
Without AI data governance, opportunities to leverage enterprise data and ultimately your content to respond to new opportunities may be lost. By developing a project charter, working committee and timelines, governance becomes an ongoing practice to deliver ROI, innovation and sustained success. While technology is important, culture will prevail, for governance is more than just “change management.” Governance demands a cultural presence and footprint. The best way to plan for change is to apply an effective layer of AI data governance to your program.
Related Article: A Guide to Data Governance: 5 Elements in Top Models
Governance Key to Sustained Innovation Success
Good governance delivers innovation and sustained success by building collaborative opportunities and participation from all levels of the organization. The more success you have in getting executives involved in the big decisions, keeping them talking about AI and making this a regular, operational discussion (not just for project approval or yearly budget reviews), the greater the benefits your organization will have. Participation from all levels of the organization is key. Engaging the leadership by involving them in the big decisions, holding regular reviews and keeping them talking about DAM, will yield the greatest the benefits from DAM.
However, we have reached a time in our history when we must implement governance to move our content into the future. Governance is the structure around how organizations manage content creation, use and distribution.
In his autobiography, "Permanent Record," Edward Snowden argues that “Technology doesn’t have a Hippocratic oath. So many decisions that have been made by technologists in academia, industry, the military, and government since at least the Industrial Revolution have been made based on ‘can we,’ not ‘should we.”
Another example of how a form of governance is needed is reflected in the advice of moving away from the brash work ethic of “move fast and break things,” from millennial technobrat and Cambridge Analytica whistleblower Christopher Wylie, who argues for a “building code for the internet” and a “code of ethics” — in essence, regulations to prevent the technological atrocities of the past.
Governance is about the ability to enable strategic alignment, to facilitate change and maintain structure. The best way to plan for change is to apply an effective layer of governance to your program — governance as innovation.
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Related Article: Customer Data Analytics and AI: The Smart Path
Governance Is All People, Process, Technology — and Content
Ultimately, governance is the structure enabling content stewardship, beginning with metadata and workflow strategy, policy development, and more and technology solutions to serve the creation, use and distribution of content. Content does not emerge fully formed into the world. It is the product of people working with technology in the execution of a process.
Proper governance of information and content must include a detailed review and analysis of all factors involved in their manifestation and life cycles, including organization, workflow, rights and preservation. The governance structure establishes the strategic, operational and technical decision-making process required to ensure the collective team excels in its mission.
AI data governance provides strategic leadership, establishes priorities and policies and is accountable and transparent to the organization. In addition, the governance standards should include a core metadata standard, proscribed workflows, and lastly, governance practices that will be carried out on an ongoing basis.
Last, start your AI data governance council sooner rather than later ensuring that it is a cross-organizational team to develop, maintain and govern the program change. Together with this expansion comes increases in regulation on how organizations must manage and protect the privacy of their, and even their customers’ information. Data is intimately associated with business transactions and there in turn those associated actions by people; it demands all our attention.
The struggle in managing content within the digital world is as complex as the digital workflows underpinning the efforts. This provides the link allowing processes and technology to be optimized, and hopefully where learning and intelligence may begin.
Are You Prepared for AI Data Governance
But are your prepared? How will you govern and evaluate your AI?
- How will you prepare for change management?
- How will you foster a culture of curiosity and innovation?
- How will your leadership drive AI initiatives with an emphasis on the importance of establishing clear policies and guidelines for AI use within the organization to ensure ethical and effective deployment?
- How will you evaluate your ROI (return on investment) for your AI?
The future of customer experience is about innovation for AI. It will have to adapt to the customer beyond a one size fits all approach in both content and context; adaption is the key and the realization that both robots and humans have a role to play. If data integrity is critical to AI and machine learning, then trust and certainty that the data is accurate, usable and responsive is also critical. Make the data meaningful, manage it well and adapt to change through continuous learning and improvement. We can do that and need to do that for our customers.
Related Article: 9 Principles to Improve Your Customer Data Management
Final Thoughts on AI Data Governance
Good, trusted, authentic data is critical to AI; trust and certainty that the data is accurate and usable is critical for success. And be mindful of the people, processes and technologies that may influence data and learning within business. Data will only continue to grow. There has never been a more important time to make data a priority and to have a road map for delivering value from it.
AI provides great opportunities for communication, engagement and risk management. Data sharing and collaboration will play an important part in growth, as business rules and policies will govern the ability to collect and analyze internal and external data.
More importantly, business rules will govern an organization’s ability to generate knowledge — and ultimately value. To deliver on its promise, data must be delivered consistently, with standard definitions and organizations must have the ability to reconcile data models from different systems.
Technology succeeds when it is leveraged to transform data into information and then information into insight that can generate action and meaning. Collective actions build mutual trust among community members, establishing knowledge-sharing opportunities, lowering transaction costs, resolving conflicts and creating greater coherence. Trust sets expectations for positive future interactions and encourages participation with technology.
Communicating the meaning and purpose of why a technology tool is being used will build trust with its audience and impact positive experiences. Trust in technology and the data flowing through its pipes will lead to greater participation that will increase information’s value and utility.
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