From Data to Dollars: How to Position Data as a Profit Enabler with Julia Bardmesser

Executive Summary

In an era where 70% of digital transformations fail and the average Chief Data Officer tenure lasts just two years; something is fundamentally broken in how organisations approach data management. This webinar, featuring Julia Bardmesser, a data management veteran with experience at Bloomberg, Freddie Mac, Citigroup, Deutsche Bank, and Voya Financial, reveals the missing link: connecting data capabilities directly to business value. Learn how to move beyond technical discussions and anchor data initiatives to revenue growth, operational efficiency, and strategic business objectives.

Webinar Details

Title: From Data to Dollars: How to Position Data as a Profit Enabler with Julia Bardmesser
Date: 2025-11-17
Presenter: Julia Bardmesser
Meetup Group: DAMA SA Big Data
Write-up Author: Howard Diesel

The Crisis in Data Leadership

The data management industry is grappling with a significant challenge, as evidenced by the short tenure of Chief Data Officers (CDOs), who average just 2 years in their roles, according to Randy Bean’s research. This brevity is not primarily due to CDOs securing more lucrative opportunities elsewhere; rather, it stems from organisations’ inability to meet their data leadership expectations. Additionally, 70% of digital transformations fail, and recent statistics reveal similar dismal success rates for generative AI initiatives, despite substantial investments.

This concerning trend highlights a deeper issue within many organisations regarding the implementation and management of data strategies. The lack of effective data leadership can lead to unmet goals and wasted resources, ultimately undermining the potential benefits of data initiatives. To improve outcomes, organisations must reevaluate their approach to data leadership and prioritise fostering an environment where CDOs can thrive and drive meaningful change.

The primary obstacle to successful data initiatives within organisations is not technical inadequacy but rather cultural resistance and ineffective change management. While organisations possess the knowledge to build data warehouses, implement data catalogues, and deploy AI platforms, they often struggle to adapt their business operations and culture to fully support these technologies. This disconnect means that when challenges arise in Data Quality, Master Data Management, or AI implementations, the root cause is typically a lack of alignment between the technology and the organisation’s internal environment.

For data technology to succeed, it is essential that business teams actively embrace new practices and prioritise necessary changes within their operations. Data leadership encompasses more than just deploying tools; it requires a transformation in how people conduct business. Therefore, organisations must foster a culture that is open to change and supportive of new disciplines, as this cultural shift is crucial to the successful integration and utilisation of data technologies.

Figure 1 From Data to Dollars

Figure 2 “About me”

Figure 3 “The stats are in … and they’re not pretty”

Figure 4 “Why?”

Building Bridges Between Business and Data

The link between data and business value is often challenged by the need to realign incentives across various sectors, including business, operations, and technology. A prime example of this struggle can be observed in Master Data Management, where the success of initiatives rests not merely on the sophistication of the chosen platform but on the ongoing commitment of business teams to treat Master Data Management as a continuous discipline rather than a one-time project. Without an active engagement from these front-office business units, achieving high Data Quality at the source—a crucial goal in Data Management—remains elusive.

For organisations to leverage their data effectively, business teams must prioritise Data Quality improvements and genuinely understand the benefits that come with these efforts. When these units assume responsibility for managing Master Data, it fosters a culture of accountability and enhances the overall quality of data across the organisation. Ultimately, the successful integration of data-driven insights into business processes will hinge on this collaborative approach, transforming data into a valuable asset that drives success.

During a pivotal executive committee meeting, the CEO asked, “What is Master Data Management?” This moment highlighted the essential challenge of articulating complex technical concepts in terms that resonate with business objectives. Using Google as an example provided clarity, but the true insight lay in understanding that data professionals must prioritise communication of business value over expecting business leaders to become well-versed in data literacy.

One executive encapsulated this sentiment by stating, “I’m tired of data literacy discussions. When you can talk business to me and finance to the CFO, then we’ll talk data to you.” This underscores the need for data professionals to engage in meaningful dialogue about how data can drive business decisions rather than simply pushing for enhanced data understanding from leadership. Ultimately, bridging the gap between data and business language is crucial for fostering collaboration and achieving organisational goals.

Figure 5 Let’s build a Bridge

The Data Strategy Journey Framework

Every effective strategy, including a data strategy, must tackle three core questions: Why are we doing this? What actions will we take? How will we execute our plan? While data and technology professionals are proficient at delving into the specifics of implementation, they often overlook the fundamental “why,” which is crucial for aligning efforts with organisational goals.

Understanding the purpose behind any strategy is essential for business leaders, as it provides a clear rationale and direction. By focusing on the reasons and goals before moving into execution plans, leaders can ensure that their teams are motivated and aligned. Ultimately, addressing both the “why” and “how” is vital for successful implementation and long-term impact.

A common pattern emerges when individuals attempt to define their business challenges: they often focus on data issues rather than the underlying business problems. For instance, stating that “Our business challenge is that CRM Data Quality is poor” merely identifies a symptom rather than addressing the core issue. The true business challenge lies in understanding the consequences of poor Data Quality, which can lead to lost customers, missed revenue, and operational inefficiencies.

Recognising the actual business problems is crucial for uncovering meaningful value from data. By shifting the focus from data-related concerns to the specific challenges the business faces, organisations can better identify how data and its capabilities can be leveraged for effective solutions. This shift in perspective transforms data from a technical issue into a vital enabler of business success.

Figure 6 Agenda

Figure 7 Data Strategy Journey

Figure 8 Data Strategy Journey pt.2

Defining Business Value and Data Capabilities

Business value is fundamentally defined by the impact that data and its associated capabilities have on a company’s financial performance. This value stems from the multitude of actions and insights that can be derived from data, which, according to Oxford Languages, comprises facts and assumptions that ground reasoning and calculations. By understanding data in this way, organisations can unlock growth opportunities that directly translate into their bottom line.

However, grasping the concept of data capability requires a more nuanced approach. As highlighted by insights from early AI models like ChatGPT-3, data capability encompasses not only the technological infrastructure but also the processes and skills necessary for effective data utilisation. By fostering these capabilities, organisations position themselves to leverage data more effectively, thereby achieving strategic objectives and enhancing overall business value.

The distinction between data and data capability is crucial for organisations aiming to deliver value effectively. Capability encompasses more than just technology platforms; it integrates processes, technology, and data to create a powerful framework for value delivery. A critical insight is that possessing data alone is insufficient; both relevant data and the capability to utilise it are necessary for meaningful outcomes. Conversely, having advanced data capabilities without access to relevant data can lead to minimal progress, emphasising the importance of aligning data with business objectives.

This understanding sheds light on the challenges faced by institutions, particularly banks, as they transition from a defensive posture focused on regulatory compliance to an offensive posture centred on revenue generation. While the necessary capabilities were often present, the data they relied upon was frequently incongruent with the requirements of revenue-generating initiatives. Consequently, it highlights the need for organisations to invest not only in robust data capabilities but also to ensure that the data they utilise aligns with their strategic goals to drive meaningful growth and performance.

Figure 9 Defining Data and its Business Value

Understanding AI as a Data Capability

Artificial Intelligence (AI) is often surrounded by hype, yet it fundamentally represents a highly advanced data capability. Developing an effective AI strategy mirrors the process of creating a data strategy: organisations must first understand the value of AI, identify the necessary capabilities, and recognise that AI is just one of many tools at their disposal. The application of machine learning and data science has historically adhered to this framework, demonstrating that even before the advent of generative AI, strategic data utilisation was essential.

Generative AI marks a significant advancement in data capabilities, representing an evolution of traditional business intelligence. While it showcases remarkable potential, it remains crucial to approach its deployment with a clear strategic perspective, ensuring that it complements existing capabilities rather than overshadowing them. Ultimately, effective integration of AI into a broader data strategy will drive value and innovation in business processes.

The integration of AI into business strategy is crucial for its successful implementation, as viewing it as a standalone technology contributes to a staggering 95% failure rate. Instead, organisations should consider AI as a valuable tool within their existing Data Management frameworks, recognising its unique mechanisms for delivering value.

For instance, Master Data Management platforms, while technically complex, remain fundamentally focused on data capabilities. By treating AI similarly—as a data capability rather than just a technological innovation—companies can better navigate the complexities it introduces and significantly increase their chances of success.

Moreover, this perspective safeguards organisations from the pitfalls of relying solely on consultancies that promote agentic AI for automating processes without considering the essential connections to data and process standardisation. By understanding AI’s role within the broader context of Data Management and business strategy, organisations can position themselves to join the 5% success club. Ultimately, embracing AI as an integrated component of a cohesive strategy can unlock substantial value and drive meaningful outcomes.

Figure 10 What about AI?

The Business Value Framework

Companies can achieve growth by adhering to a fundamental formula that encompasses acquiring more customers, selling additional products, and ensuring better profit margins while managing acceptable risks. To attract more customers, businesses must focus on both acquiring new clientele and retaining their existing customer base. Increasing product sales requires strategies such as enhancing the usage of current offerings, cross-selling complementary products, and continually innovating new solutions.

Additionally, pursuing better margins hinges on optimising operational efficiency and leveraging assets effectively. An essential aspect of growth is recognising that risk cannot be completely eliminated; rather, it must be carefully managed. While risk-taking is necessary for expansion, uncontrolled risk can lead to catastrophic failures, as seen in the cases of Enron and FTX. By balancing these elements, companies can create a resilient foundation for sustainable growth.

The adaptability of the framework across various organisational types highlights the distinct objectives and strategies employed by nonprofits and government entities compared to those of traditional businesses. In the case of nonprofits and government, the focus shifts from growth to achieving their mission, prioritising user and participant service over customer relations, and delivering programs and services rather than products. This unique orientation requires maintaining financial margins and managing risk through a data defence approach, which emphasises acceptable risk and effective risk management.

Conversely, businesses pursue a data offence strategy to expand their customer base and product offerings. Monetisation becomes a critical approach when high-revenue product lines face declining margins, prompting companies to leverage data and relationships developed in these segments.

After maximising efficiency and automation, organisations can create new products with higher margins, thereby addressing a significant challenge distinct from Gartner’s broader definition of monetisation. This targeted approach not only reinforces the importance of adaptability across organisational types but also underscores the necessity of strategic data utilisation to achieve financial sustainability.

Figure 11 Framework for the Business Value of Data

Figure 12 Framework for the Business Value of Data pt.2

Customer Lifetime Value – A Strategic Use Case

Customer Lifetime Value (CLV) is a crucial metric that quantifies the total profit a company can generate from a customer throughout the entirety of their relationship. This metric encompasses more than just the initial contract value; it considers the potential for long-term engagement and maximized relationship value. Companies can leverage CLV to inform their customer acquisition strategies, ensuring that customer acquisition costs do not exceed the expected lifetime value.

Additionally, CLV plays a significant role in various aspects of business operations, including customer retention efforts, cross-selling opportunities, and cost of servicing. By understanding CLV, businesses can focus on keeping retention costs below the value generated, identifying opportunities to deepen relationships through cross-selling, and managing customer service costs. Ultimately, a thorough grasp of CLV not only enhances revenue potential but also supports risk management, as high-value customers can pose significant exposure risks.

Calculating Customer Lifetime Value (CLV) is a complex task that demands extensive data capabilities and knowledge across multiple domains. It involves analysing various revenue data such as products purchased, payment amounts, and associated fees, including merchant and late payment fees. Additionally, understanding cost data is crucial, encompassing intrinsic product costs—straightforward for physical goods but often challenging for services, such as banking products—and servicing costs, where frequent client interactions can diminish overall value even when clients hold multiple products.

In order to create a holistic picture of CLV, it is essential to consider client tenure data, which reflects how long customers have been with the company, along with projected tenure based on agreement terms and historical averages. Furthermore, external factors, including industry trends, competitive insights, and data from CRM systems about leadership changes, play a significant role in shaping customer value. While many companies excel at tracking revenue, they often struggle to gain a comprehensive understanding of the associated costs, underscoring a critical area for improving CLV calculations.

Figure 13 Customer Lifetime Value

Figure 14 Business Case Study – Customer Lifetime Value

Figure 15 Customer Lifetime Value – Generic

Figure 16 Operationalising CLV: Capabilities

Data Capabilities Required for CLV

Operationalising Customer Lifetime Value requires a robust framework of data capabilities that work together seamlessly. At the centre of this framework is Data Governance, which identifies and manages information sources while determining authoritative data sources to prevent users from relying on less reliable options.

This essential function ensures that data sources maintain their integrity over time. Complementing this is Master Data Management, which consolidates data from various sources using common identifiers, allowing for efficient integration across systems.

Additionally, Data Quality and Data Integration are vital to this process. Data Quality guarantees that the information available for decision-making is trustworthy and accurate, thereby enhancing the reliability of insights derived from the data. Meanwhile, effective Data Integration—though primarily driven by technology—is crucial for moving and combining data efficiently across different platforms. Together, these capabilities provide a solid foundation for effectively leveraging Customer Lifetime Value in decision-making.

Effective customer relationship management relies on integrating mathematical analysis with predictive capabilities, enabling businesses to understand their current state while anticipating future trends. For instance, last-mile delivery plays a crucial role in ensuring that customer lifetime value (CLV) data is accessible at every customer touchpoint, such as points of sale, call centres, and chat interfaces. This accessibility empowers representatives with knowledge of who they are engaging with, enabling more tailored customer service.

A pertinent example can be seen in the telecommunications industry, where a long-standing customer with a robust history may not appear at risk of churn, while high-value customers who indicate potential disengagement should be flagged for immediate action. In this context, generative AI stands out, enhancing the last-mile delivery of information by embedding intelligent insights within operational workflows. By doing so, it ensures that critical information is utilised effectively, ultimately improving customer retention and satisfaction.

Figure 17 Operationalising CLV: Capabilities pt.2

Figure 18 Operationalising CLV: Capabilities + Data

Figure 19 Telecommunications: Client Retention

Figure 20 Operationalising CLV: Capabilities + Data pt.2

Figure 21 Extending the Capabilities

Extending Capabilities – Clients at Risk

To effectively identify high-value clients at risk of churn, organisations must build on Customer Lifetime Value (CLV) foundations by enhancing their data and analytics capabilities. This involves not only refining CLV calculations but also incorporating competitive intelligence, such as insights into comparable services and competitors’ current offers.

Additionally, consumer sentiment analysis across platforms—such as TikTok, Yelp, and Facebook—can provide valuable context, particularly as GenAI excels at aggregating this data. Furthermore, interaction analysis, which examines customer inquiries or frustrations during technical support calls, can reveal deeper insights into customer behaviour and satisfaction.

By integrating these varied data sources, companies can pinpoint which top clients are most vulnerable to churn. The comprehensive approach ensures that organisations are not merely reacting to customer behaviour but proactively understanding the factors that drive client loyalty. Ultimately, leveraging this enhanced analytics framework allows businesses to develop targeted strategies to retain their most valuable customers, thereby reducing churn risk and fostering long-term relationships.

The transition from Customer Lifetime Value (CLV) to client-at-risk management can be achieved relatively easily, whereas establishing CLV itself requires robust Data Management capabilities. This journey highlights that, once CLV is in place, other applications become significantly more accessible. Interestingly, the shift from leveraging CLV for offensive strategies to using financial reporting for defensive purposes is simpler than the inverse process, as financial reporting primarily relies on summarised product data, whereas CLV demands a detailed understanding of granular product data.

Ultimately, the transition from granular to summarised data is straightforward, making it essential for organisations to develop strategic roadmaps that emphasise scalable data capabilities across use cases. By prioritising incremental data builds that deliver value at each stage, companies can avoid cumbersome, large-scale implementations and instead foster an environment that supports continuous improvement and adaptability in their data-driven decision-making.

Figure 22 Monday Morning Playbook

Figure 23 QR Code to get in touch

The Metadata Management Challenge

Metadata Management plays a crucial role in data initiatives, although its value can be difficult to quantify directly. Unlike other data capabilities that can be easily linked to specific business outcomes, the benefits of Metadata Management often seem indirect. This challenge makes it essential to explore the areas where Metadata offers clear advantages, particularly in contexts such as data monetisation.

One of the most significant connections between Metadata Management and business value is evident in data monetisation use cases. When organisations leverage data from one sector to develop products for another, understanding data rights and usage becomes paramount. Here, Metadata Management shines by providing clarity on the legal implications of data usage, ensuring compliance and enabling organisations to harness their data assets effectively. Ultimately, strong Metadata Management practices not only support legal compliance but also enhance the potential for successful data monetisation strategies.

Generative AI highlights the critical role of Metadata Management in enhancing data rights management and semantic modelling outcomes. Despite unanimous agreement among businesses on the necessity of clear data definitions, many fail to utilise them effectively once they are created. This challenge arises largely because those responsible for creating Metadata often perceive the task as low priority amid their daily responsibilities.

Engaging business users in the development of data catalogues becomes increasingly difficult without regulatory impetus, making it essential to demonstrate the practical relevance of these catalogues to their daily tasks. Additionally, effective Metadata Management must extend beyond traditional databases to encompass information assets produced by business units, such as Excel spreadsheets. Therefore, organisations must cultivate foundational capabilities in Metadata Management before successfully implementing comprehensive and effective cataloguing strategies.

The Monday Morning Playbook

Implementing data-driven value creation begins with aligning initiatives to the company’s core vision and strategic objectives. It is crucial for organisations, particularly in the insurance sector, to prioritise their fundamental mission—serving customers—over becoming data-driven alone.

 When data is used to deny claims for efficiency’s sake, it contradicts this essential purpose. Ensuring that data initiatives support the company’s vision fosters a holistic approach that prioritises customer service.

Furthermore, understanding current business strategies and growth levers is essential for effective data implementation. Engaging in conversations focused on how various business units achieve their annual goals allows for a more comprehensive understanding of their needs.

Rather than fixating on data problems, this approach highlights how data can enhance overall business performance and contribute to long-term success. Ultimately, aligning data initiatives with business strategies not only strengthens the organisation’s objectives but also enhances value creation.

To effectively enhance business capabilities, it is crucial to define and prioritise initiatives based on their value to the organisation. This involves identifying the most critical objectives and the necessary capabilities to achieve them.

By systematically extending these capabilities in small increments, businesses can ensure their efforts align with existing initiatives rather than creating isolated projects with separate return-on-investment (ROI) justifications. For instance, if the goal is to retain top customers, organisations should develop capabilities that directly support this aim, utilising established metrics to guide their efforts.

Additionally, maintaining a continuous and adaptive process is essential, as business landscapes, leadership, and priorities are subject to change. Working in Minimum Viable Product (MVP) increments while adhering to value-based roadmaps allows companies to regularly validate their progress and make adjustments as needed. This ongoing adaptation not only enhances the effectiveness of business initiatives but also fosters a culture of resilience and responsiveness within the organisation.

Prioritisation is a multifaceted process that requires careful consideration of various factors. It involves gathering input from diverse stakeholders, creating comprehensive information maps to analyse drivers, and identifying commonalities to inform decision-making.

By utilising a simple value-versus-effort matrix, teams can strategically position minimum viable products (MVPs) where the highest value intersects with the lowest resource investment. This structured approach provides a clearer understanding of the feasible paths forward before introducing political considerations.

Once the non-political realities are understood, organisations can adjust their strategies to account for internal dynamics while remaining focused on optimal outcomes. Delivering significant value to less influential stakeholders can lay the foundation for engaging more influential stakeholders in later phases of the project. This balanced approach not only enhances stakeholder satisfaction but also builds enduring relationships that can facilitate future collaboration and success.

Practical Guidance for Data Stewards

Establishing a strong foundation for data stewardship is crucial for organisations starting from scratch. To navigate the complexity of this process, data stewards should begin by identifying their business unit’s goals for the year and pinpointing areas of struggle. By aligning business objectives with the data and capabilities required, stewards can focus their efforts on the most impactful initiatives. This approach provides a structured pathway for developing effective Data Management practices.

Once the goals and data needs are established, it is essential to designate authoritative data sources, whether they are internal, external, or from a CRM system. This step lays the foundation for a robust Data Governance framework. It is equally important to define expected quality levels based on the objectives at hand, ensuring that the collected data is both relevant and reliable. By taking these deliberate steps, data stewards can lay a solid foundation that enables effective decision-making and supports the organisation’s broader goals.

To achieve effective data stewardship, organisations should avoid starting with extensive infrastructure like data warehouses or comprehensive policy councils. These initiatives often fail unless the organisation is exceptionally fortunate. Instead, they should link stewardship roles to specific value delivery, ensuring that deliverables are organised to enhance capabilities progressively. A mere list of use cases does not constitute a strategy; rather, these use cases must systematically connect and build upon one another.

Focusing on growth levers and crucial metrics, such as Customer Lifetime Value (CLV), allows organisations to identify critical data elements. It is more effective to source the most trusted data for these elements rather than pursue an exhaustive Master Data Management approach across all client data.

This targeted strategy helps prevent individuals from spending their careers striving for data perfection while still delivering measurable value incrementally. Ultimately, building data capabilities should prioritise important business drivers rather than attempting to cover every possible data domain comprehensively.