The Solution to Data Management's GenAI Problem? More GenAI. – BCG
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Artificial Intelligence
By Krishnan Ramachandran, Lucas Quarta, Marc Schuuring, Christian Kirschniak, Benjamin Rehberg, and Antoine Gourévitch
Generative AI has a lot of companies talking—and looking to their chief data officer to put words into action. With its transformative, content-creating algorithms, the technology falls right in a CDO’s wheelhouse: turning data into value. But it also puts existing models for data governance and management in the crosshairs.
That’s because GenAI learns how to create content by training on massive amounts of unstructured data: text, video, audio, even programming code. Few companies have experience classifying or assessing this kind of material. Moreover, data governance is rarely the poster child for efficiency and effectiveness. For many companies, it’s a pain point, the work too manual and tedious—a real headache, especially in industries that are highly regulated or incorporate vast amounts of personally identifiable information. Businesses either throw a lot of people at the effort or not enough.
GenAI, in short, makes a process that was already a challenge even more of one.
Tackling this dilemma should be—if it isn’t already—at the top of every CDO’s to-do list. Across industries, companies are leveraging GenAI to turbocharge customer service and personalization, automate traditionally manual processes, and create value in ever-increasing ways. But without adapting their data strategy, policies, and capabilities, businesses face a Hobson’s choice. They can get bogged down in more manual work to ensure that all the new training data passes muster on quality, integrity, security, and responsible use. Or they can move ahead without that governance and risk the consequences—a risk that can cause top management to pull the plug on GenAI and its potential value.
But here’s the twist—and the silver lining. The same technology that increases the burden on data governance can also alleviate it. In fact, GenAI can do more than deaden the pain of all that manual, tedious work. It can largely eliminate it. GenAI creates and interprets content. That means it can augment or automate many key data management tasks; for example, labeling data with privacy or intellectual property concerns so it’s not used inappropriately. In finally bringing efficiency to data management, GenAI demonstrates one more way in which it’s a breakthrough.
By embedding GenAI in their data governance and management processes, companies can reap the opportunities without the burden. And with algorithms doing the legwork, data professionals can devote more time to value-adding work—creating still more opportunities to grow the business.
Data governance—the rules around capturing, storing, and using data, as well as verifying its quality and integrity—builds trust in data. Data management implements those rules, ensuring that organizations know where data is and where it came from, provide access to the right people for the right uses, and are aware of any issues—such as privacy and regulatory concerns—that might impact how they utilize the data.
While companies take different approaches to data governance and management, one element has long remained constant: structured data. Stored in standardized form within databases, structured data is easily labeled and classified, so companies can readily understand its key characteristics—and how they can and can’t use it. Lineage, traceability of sources, assurances of quality, flags for personally identifiable information or other concerns: it’s all in the record.
Unstructured data—GenAI’s fuel—typically isn’t stored, neatly labeled and classified, within a database. It’s everything from email and Word documents to YouTube videos and dialogue from computer games. Companies may have the data, but they aren’t likely to have much, if any, insight regarding the who, how, do’s, and don’ts of usage.
GenAI models don’t use some unstructured data. They use an enormous amount of it. And the processes for labeling, classifying, and ensuring data quality are largely manual. Companies may not be starting from scratch: they’re likely to have applied data management practices to documents used internally, for instance. But they still face a gargantuan task understanding all this data and ensuring its quality and appropriate use in customer-centered processes and value streams.
They also face risks, particularly around data remediation. A company that applies manual processes to so much unstructured information can quickly fall behind in correcting data errors and inconsistencies. That’s concerning for any business, but it can be an especially big worry for large, regulated firms.
It doesn’t have to be this way. GenAI’s key traits—an affinity for unstructured data and an ability to create content—make it a natural tool for boosting the efficiency and effectiveness of data management. In our experience, there are six main GenAI use cases for data management:
These use cases can have a particularly big impact on data stewards and data custodians. Tasked with ensuring data quality and promoting trust in data, these teams devote much of their time to manual, repetitive activities. With GenAI augmenting their work, data stewards and custodians can focus their attention—and capacity—on more complex, strategic, and value-adding tasks.
We recommend that CDOs take a two-pronged approach. First, prepare the data foundation—the data architecture, data platform capabilities, and data life cycle management (covering everything from sourcing data to preparing algorithms for operational use)—for GenAI business use cases. Second, incorporate GenAI into the company’s data governance and management processes.
While the journey will vary from one business to another, there are three general roadmaps. The one to take depends on a company’s current level of digital maturity, that is, on whether it’s a digital passive (a company whose digital and data foundation is still at a low level of maturity), a digital literate (typically a business that’s in the midst of a digital transformation but has yet to build out its data foundation fully or launch use cases at enterprise-level scale), or a digital performer (a company with an enterprise-wide data and digital platform that is fueling use cases at scale).
Digital Passives. For these companies, the primary focus should be the data foundation, developing the core capabilities and strategies to effect a data-driven digital transformation. This begins with assessing existing data capabilities. How is data supporting business functions? Where else could data create value? Which fundamentals—such as identifying, labeling, and cleansing key data assets—require attention? This analysis enables companies to understand where they stand on leveraging data and where they want to be. It serves as a guiding light for crafting a holistic data strategy: defining the optimal data architecture, refining data governance and management capabilities, and prioritizing use cases.
The great advantage of this approach is that it lets companies focus on what matters most and steadily build their capabilities instead of trying to do everything in one fell swoop, which is often unsuccessful. But even as they develop this data foundation, companies should consider how they might use GenAI to improve internal work. Integrating GenAI into data governance and management makes those processes more efficient, but it also sets the stage for leveraging GenAI more broadly down the road and helps businesses identify the talent they’ll need to develop business use cases.
Digital Literates. Companies in this group have a data foundation robust enough to support their initial forays into AI and advanced analytics. Their task now is to extend their capabilities in support of GenAI use cases. To that end, CDOs should drive proof-of-concept (POC) initiatives to explore—and demonstrate—the value that GenAI can bring to data governance and management. By pursuing POCs, companies can test the technology and sound out use cases without disrupting existing processes and workflows.
We recommend starting with one to three POCs, based on an initial review of feasibility and the potential for value creation. For each initiative, CDOs should develop a blueprint covering technology and talent requirements, as well as risk and compliance implications. Lessons learned from the pilots can help companies refine the business case for each POC, optimize the order in which they scale initiatives (weeding out those POCs that didn’t pay off), better understand—and plan for—talent and capacity requirements, and steer around roadblocks and bottlenecks when scaling.
Digital Performers. With successful AI use cases under their belt, these companies are poised to integrate GenAI into their business. Like digital literates, they should consider how they might improve foundational data capabilities with GenAI, pursue POCs, and leverage insights from pilots to plan for and steer at-scale deployment. But their high level of digital maturity puts this group in a position to move faster and more ambitiously. The key is to create—and unleash—agile squads composed of data scientists, AI developers, and data governance experts.
Digital performers are likely to already excel at agile, so applying its structures and methodologies to GenAI should be relatively seamless. Working together, squad members can assess the feasibility of leveraging unstructured data (such as scanned images or email) to create business value. They can then implement—in an efficient and collaborative way—the GenAI use cases that make the cut.
No matter which path a company takes, a few best practices can accelerate the journey. First, be selective: prioritize efforts by value and impact. Pilots are a great way to demonstrate—or discard—a business case and get the order right. Next, imbue scaling with robust change management and risk management, with a particular focus on data privacy and responsible AI. Organizations that do this ensure that users—and, in turn, the business—get the most out of GenAI-enabled solutions, without opening the door to new risks and concerns.
All companies, no matter their data maturity, should appoint an AI ethics officer, accountable for the company’s responsible and regulatory-compliant use of AI. This individual will also onboard and enable any additional ethical-AI experts the organization may require. It’s hard to overemphasize the importance of responsible AI. It not only reduces the potential for harm but also strengthens trust, improves the performance of AI systems, and boosts value creation.
GenAI is an emerging technology and its track record is a brief read. But there’s no time to wait and see how things evolve. GenAI has the potential to transform everything from R&D to customer support. To realize that promise, CDOs need to act now: anticipating—and moving to alleviate—the burden that GenAI puts on data governance and data management.
Fortunately, GenAI is its own best enabler. By using it to automate critical data processes, companies can create a foundation that fuels the technology—and the possibilities it brings. GenAI may create content, but with the right preparation, it can create competitive advantage, too.
Partner & Associate Director, Digital in Financial Institutions
Silicon Valley – Bay Area
Managing Director & Partner
Paris
Managing Director & Senior Partner
Amsterdam
Managing Director & Partner
Stuttgart
Managing Director & Senior Partner
New York
Managing Director & Senior Partner
Paris
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Our aerospace and defense experts help industry players navigate their day-to-day operations and identify ways they can innovate for the future.
Breakthroughs in automotive technologies and emerging business models are changing the world. We help the leading game changers stay ahead of the curve.
Brands around the world must master digital to survive. We have the expertise and capabilities to power consumer-centric innovation grounded in data, analytics, and AI.
We partner with effective organizations and educators to improve student outcomes and learning models—from K-12 through higher education—locally, nationally, and globally.
BCG’s energy consultants work with business leaders, governments, and ecosystems to create energy solutions for a net-zero pathway and beyond – that are practical, equitable, and just.
BCG helps global and regional financial institutions build for the future using digital innovations and an ESG focus to drive fundamental change and deliver on evolving customer demands.
We work with organizations around the globe to transform health care operating models enabling them to deliver medical breakthroughs, innovative cures, and transformative patient experiences.
Our experts provide industrial goods clients with the skills they need to adapt to change and rethink their business models in their rapidly evolving industries.
Customers increasingly expect more from insurers. BCG helps companies rise to the challenge and equips them to lead in the digital future.
The rapid growth of private capital brings unprecedented opportunities to unlock value while making a positive difference in the world. BCG advises leading investors on how to stay ahead.
BCG’s Public Sector practice joins forces with global organizations and governments to transform how they operate so they can better meet increasingly challenging and complex societal problems.
We help the world's leading retailers compete in the digital era by developing and delivering cutting-edge tech strategies and identifying new sources of growth.
BCG collaborates with technology, media, and telecommunications companies to solve problems and pursue new strategies as they continue to shape the future.
We empower clients to boldly reinvent themselves and embrace digital innovation so they can better serve their customers amid today's volatility.
The industry has suffered an unprecedented shock. BCG collaborates with travel and tourism providers as they navigate this turbulence and build resilience for the future.
See how we help our clients expand their capabilities on multiple existing—and emerging—fronts.
Scaling artificial intelligence can create a massive competitive advantage. Learn how our AI-driven initiatives have helped clients extract value.
Excavating purpose is among the rewarding journeys companies can take. BCG BrightHouse helps organizations embrace purpose to achieve higher returns, employee engagement, and customer satisfaction.
Leaders face an uncertain landscape. The impact of each decision feels impossible to predict, which is why they need strategies that are proactive, resilient, and competitive. No matter their starting point, BCG can help.
Even today's leading companies could benefit from an always-on transformation capability—the kind that engenders resilience and leads to long-term value creation. BCG knows what it takes to build this muscle.
The time for global climate action is now. BCG helps clients accelerate their climate and sustainability journey and seize new opportunities to build competitive advantage.
The rules of business and for maintaining competitive advantage are changing. We help companies reimagine strategy and value creation in a fast-paced world.
BCG’s cost advantage approach resets costs within a framework that is customized, precise, and thorough.
Having rich customer insights allows companies to solve pressing business problems and capture growth opportunities. BCG’s Center for Customer Insight arms companies with the tools they need to unlock this advantage.
BCG identifies and delivers high-impact programs to build companies of the future that will outperform the competition in the future.
Our work enables clients to build teams that fully reflect the diversity in the world and the communities they serve—and empowers them to advance their businesses and our society.
Innovation is extremely difficult—but also necessary. We collaborate closely with organizations on holistic innovation journeys to help them secure long-term competitive advantage.
We stand side by side with our clients, offering expertise and strategy as they look to expand the boundaries of their businesses in an uncertain and sometimes tumultuous world.
We ask three guiding questions: when, where, and how will you manufacture in an era of emerging technologies and economic uncertainty? We're committed to helping our clients arrive at answers that are right for them.
Customer centricity is key and made possible through analytics, agile processes, and a test-and-learn culture. BCG partners with marketing and sales organizations to make it happen.
BCG provides the strategic decision-making capabilities and proactive portfolio management companies need to create value from M&A, transactions, and PMI.
We help clients unlock value from every aspect of their operations, maximizing efficiency and effectiveness in procurement, service operations, supply chains, and beyond.
Organization is at the core of nearly everything we do at BCG. We believe that when clients have the right organization design, the possibilities are endless.
Any company's most important asset is its people. Our experts in leadership, culture, talent, reskilling, and HR help businesses respond to current and future challenges.
We bring expertise and data-driven strategies to help clients unleash the power of effective pricing—and unlock its potential to increase the bottom line.
BCG's Risk and Compliance consulting supports their client's growth ambition with strategic, transformational, and technical functional offerings in Finance.
By focusing on tangible ways to generate positive social impact (related to climate change, global education, racial equity, and more) we help organizations tackle some of the most pressing issues facing our world today.
Zero-based budgeting is a powerful strategy that yields significant benefits. Our approach fosters a culture of cost consciousness, growth, and innovation.
BCG X disrupts the present and creates the future by building new products, services, and businesses in partnership with the world’s largest organizations.
Explore a cross-section of the latest insights and perspectives from BCG experts.
At the BCG Henderson Institute (BHI), we believe that leadership starts in the mind and that ideas precede action. We bring the ideas and inspiration that will help forward-looking leaders shape their next game.
Subscribe to our newsletters and e-alerts to stay connected with the latest insights, data, ideas, and perspectives from BCG.
Access BCG’s alumni network for former and current BCGers.
As a top consulting firm, BCG helps clients with total transformation—driving complex change, enabling organizations to grow, and driving bottom-line impact.
Meet BCG’s executive, functional, and regional leadership teams.
Find BCG offices around the world.
Visit our media center for resources, press releases, trending topics, contacts, and more.
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Artificial Intelligence
By Krishnan Ramachandran, Lucas Quarta, Marc Schuuring, Christian Kirschniak, Benjamin Rehberg, and Antoine Gourévitch
Generative AI has a lot of companies talking—and looking to their chief data officer to put words into action. With its transformative, content-creating algorithms, the technology falls right in a CDO’s wheelhouse: turning data into value. But it also puts existing models for data governance and management in the crosshairs.
That’s because GenAI learns how to create content by training on massive amounts of unstructured data: text, video, audio, even programming code. Few companies have experience classifying or assessing this kind of material. Moreover, data governance is rarely the poster child for efficiency and effectiveness. For many companies, it’s a pain point, the work too manual and tedious—a real headache, especially in industries that are highly regulated or incorporate vast amounts of personally identifiable information. Businesses either throw a lot of people at the effort or not enough.
GenAI, in short, makes a process that was already a challenge even more of one.
Tackling this dilemma should be—if it isn’t already—at the top of every CDO’s to-do list. Across industries, companies are leveraging GenAI to turbocharge customer service and personalization, automate traditionally manual processes, and create value in ever-increasing ways. But without adapting their data strategy, policies, and capabilities, businesses face a Hobson’s choice. They can get bogged down in more manual work to ensure that all the new training data passes muster on quality, integrity, security, and responsible use. Or they can move ahead without that governance and risk the consequences—a risk that can cause top management to pull the plug on GenAI and its potential value.
But here’s the twist—and the silver lining. The same technology that increases the burden on data governance can also alleviate it. In fact, GenAI can do more than deaden the pain of all that manual, tedious work. It can largely eliminate it. GenAI creates and interprets content. That means it can augment or automate many key data management tasks; for example, labeling data with privacy or intellectual property concerns so it’s not used inappropriately. In finally bringing efficiency to data management, GenAI demonstrates one more way in which it’s a breakthrough.
By embedding GenAI in their data governance and management processes, companies can reap the opportunities without the burden. And with algorithms doing the legwork, data professionals can devote more time to value-adding work—creating still more opportunities to grow the business.
Data governance—the rules around capturing, storing, and using data, as well as verifying its quality and integrity—builds trust in data. Data management implements those rules, ensuring that organizations know where data is and where it came from, provide access to the right people for the right uses, and are aware of any issues—such as privacy and regulatory concerns—that might impact how they utilize the data.
While companies take different approaches to data governance and management, one element has long remained constant: structured data. Stored in standardized form within databases, structured data is easily labeled and classified, so companies can readily understand its key characteristics—and how they can and can’t use it. Lineage, traceability of sources, assurances of quality, flags for personally identifiable information or other concerns: it’s all in the record.
Unstructured data—GenAI’s fuel—typically isn’t stored, neatly labeled and classified, within a database. It’s everything from email and Word documents to YouTube videos and dialogue from computer games. Companies may have the data, but they aren’t likely to have much, if any, insight regarding the who, how, do’s, and don’ts of usage.
GenAI models don’t use some unstructured data. They use an enormous amount of it. And the processes for labeling, classifying, and ensuring data quality are largely manual. Companies may not be starting from scratch: they’re likely to have applied data management practices to documents used internally, for instance. But they still face a gargantuan task understanding all this data and ensuring its quality and appropriate use in customer-centered processes and value streams.
They also face risks, particularly around data remediation. A company that applies manual processes to so much unstructured information can quickly fall behind in correcting data errors and inconsistencies. That’s concerning for any business, but it can be an especially big worry for large, regulated firms.
It doesn’t have to be this way. GenAI’s key traits—an affinity for unstructured data and an ability to create content—make it a natural tool for boosting the efficiency and effectiveness of data management. In our experience, there are six main GenAI use cases for data management:
These use cases can have a particularly big impact on data stewards and data custodians. Tasked with ensuring data quality and promoting trust in data, these teams devote much of their time to manual, repetitive activities. With GenAI augmenting their work, data stewards and custodians can focus their attention—and capacity—on more complex, strategic, and value-adding tasks.
We recommend that CDOs take a two-pronged approach. First, prepare the data foundation—the data architecture, data platform capabilities, and data life cycle management (covering everything from sourcing data to preparing algorithms for operational use)—for GenAI business use cases. Second, incorporate GenAI into the company’s data governance and management processes.
While the journey will vary from one business to another, there are three general roadmaps. The one to take depends on a company’s current level of digital maturity, that is, on whether it’s a digital passive (a company whose digital and data foundation is still at a low level of maturity), a digital literate (typically a business that’s in the midst of a digital transformation but has yet to build out its data foundation fully or launch use cases at enterprise-level scale), or a digital performer (a company with an enterprise-wide data and digital platform that is fueling use cases at scale).
Digital Passives. For these companies, the primary focus should be the data foundation, developing the core capabilities and strategies to effect a data-driven digital transformation. This begins with assessing existing data capabilities. How is data supporting business functions? Where else could data create value? Which fundamentals—such as identifying, labeling, and cleansing key data assets—require attention? This analysis enables companies to understand where they stand on leveraging data and where they want to be. It serves as a guiding light for crafting a holistic data strategy: defining the optimal data architecture, refining data governance and management capabilities, and prioritizing use cases.
The great advantage of this approach is that it lets companies focus on what matters most and steadily build their capabilities instead of trying to do everything in one fell swoop, which is often unsuccessful. But even as they develop this data foundation, companies should consider how they might use GenAI to improve internal work. Integrating GenAI into data governance and management makes those processes more efficient, but it also sets the stage for leveraging GenAI more broadly down the road and helps businesses identify the talent they’ll need to develop business use cases.
Digital Literates. Companies in this group have a data foundation robust enough to support their initial forays into AI and advanced analytics. Their task now is to extend their capabilities in support of GenAI use cases. To that end, CDOs should drive proof-of-concept (POC) initiatives to explore—and demonstrate—the value that GenAI can bring to data governance and management. By pursuing POCs, companies can test the technology and sound out use cases without disrupting existing processes and workflows.
We recommend starting with one to three POCs, based on an initial review of feasibility and the potential for value creation. For each initiative, CDOs should develop a blueprint covering technology and talent requirements, as well as risk and compliance implications. Lessons learned from the pilots can help companies refine the business case for each POC, optimize the order in which they scale initiatives (weeding out those POCs that didn’t pay off), better understand—and plan for—talent and capacity requirements, and steer around roadblocks and bottlenecks when scaling.
Digital Performers. With successful AI use cases under their belt, these companies are poised to integrate GenAI into their business. Like digital literates, they should consider how they might improve foundational data capabilities with GenAI, pursue POCs, and leverage insights from pilots to plan for and steer at-scale deployment. But their high level of digital maturity puts this group in a position to move faster and more ambitiously. The key is to create—and unleash—agile squads composed of data scientists, AI developers, and data governance experts.
Digital performers are likely to already excel at agile, so applying its structures and methodologies to GenAI should be relatively seamless. Working together, squad members can assess the feasibility of leveraging unstructured data (such as scanned images or email) to create business value. They can then implement—in an efficient and collaborative way—the GenAI use cases that make the cut.
No matter which path a company takes, a few best practices can accelerate the journey. First, be selective: prioritize efforts by value and impact. Pilots are a great way to demonstrate—or discard—a business case and get the order right. Next, imbue scaling with robust change management and risk management, with a particular focus on data privacy and responsible AI. Organizations that do this ensure that users—and, in turn, the business—get the most out of GenAI-enabled solutions, without opening the door to new risks and concerns.
All companies, no matter their data maturity, should appoint an AI ethics officer, accountable for the company’s responsible and regulatory-compliant use of AI. This individual will also onboard and enable any additional ethical-AI experts the organization may require. It’s hard to overemphasize the importance of responsible AI. It not only reduces the potential for harm but also strengthens trust, improves the performance of AI systems, and boosts value creation.
GenAI is an emerging technology and its track record is a brief read. But there’s no time to wait and see how things evolve. GenAI has the potential to transform everything from R&D to customer support. To realize that promise, CDOs need to act now: anticipating—and moving to alleviate—the burden that GenAI puts on data governance and data management.
Fortunately, GenAI is its own best enabler. By using it to automate critical data processes, companies can create a foundation that fuels the technology—and the possibilities it brings. GenAI may create content, but with the right preparation, it can create competitive advantage, too.
Partner & Associate Director, Digital in Financial Institutions
Silicon Valley – Bay Area
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Paris
Managing Director & Senior Partner
Amsterdam
Managing Director & Partner
Stuttgart
Managing Director & Senior Partner
New York
Managing Director & Senior Partner
Paris
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