Executive Summary
This webinar outlines the critical topics related to Data Management and its impact on business value. Howard Diesel utilises this series to explore the roles and responsibilities of data professionals, including Data Managers, Data Stewards, and Data Owners. Addressing challenges such as Data Quality, Data Warehousing, and commercial feasibility, Howard emphasises the significance of Data Management and its value proposition. The webinar emphasises the significance of leadership, cultural transformation, and return on investment (ROI) in Data Management, underscoring the crucial role of marketing analytics in generating customer value.
Webinar Details
| Title | Designing Data Products to Support Customer Value Proposition – Data Managers |
| URL | https://youtu.be/GYw8BXJtv3I |
| Date | 25 July 2024 |
| Presenter | Howard Diesel |
| Meetup Group | Data Manager |
| Write-up Author | Howard Diesel |
Contents
Data Professionals, Customer Value Proposition, and the Role of Marketing Analytics
Understanding the Responsibilities of Data Managers
The Role of a Data Manager
Understanding the Roles: Team Lead, Manager, and Executive
The Challenges of Data Stewardship in the AI Era
Leadership and Data Management
Understanding the Data Management Hierarchy
The Role and Responsibilities of Data Owners
Understanding the Data Manager
Data Quality and Value Definition in Organisations
The Challenges of Data Warehousing and Technology Implementation
The Commercial Feasibility and Value of Data Initiatives
The Responsibilities of a Data Practice Manager
Evaluating Value in Data Management Practices
Data Management and Value Strategy
The Journey of Data Delivery and Management
Data Management and Value Proposition
Commercial Value Allocation and Immaterial Assets in Data Product Management
Culture Change and Value Delivery in Business
Data Management and ROI in Business
Data Professionals, Customer Value Proposition, and the Role of Marketing Analytics
Howard Diesel opened the webinar and shared that the focus of the webinar would be on understanding customer value and building strong relationships for businesses aiming to succeed in today’s competitive market. He then emphasised the significance of leveraging marketing analytics to effectively deliver data products that meet customer needs. Additionally, by focusing on these key areas, companies can enhance their strategies and foster lasting connections with their clientele.
Understanding the Responsibilities of Data Managers
The role of Data Managers is crucial in overseeing data teams and practices while ensuring alignment with business initiatives. A key focus of the discussion was the establishment of a Data Management office (DMO), which serves to create value through effective data products. Furthermore, integrating business and marketing value propositions into Data Management strategies was identified as essential for enhancing overall impact. In conclusion, prioritising these elements can significantly improve Data Management effectiveness and drive business success.
In exploring the intricacies of Data Management, the discussion highlighted the challenges of defining and delivering value while developing compelling value propositions. Participants actively engaged in a dialogue about how to achieve tangible value and effectively leverage data products, considering factors such as fostering a supportive culture within data practices and navigating various compliance regulations. Ultimately, the exchange of ideas underscored the importance of aligning organisational strategies with Data Management capabilities to enhance overall value.
Figure 1 Designing Data Products
The Role of a Data Manager
Effectively managing data is crucial for fostering trust and credibility within a team. When team members are invested in Data Governance and recognise the value of their work, the positive impacts on collaboration and productivity are significant. Additionally, integrating the customer journey for data professionals with the SFIA (Skills Framework for the Information Age) illustrates the various stages of a data professional’s development, ranging from novice to expert to thought leader. Ultimately, acknowledging the uniqueness of each individual’s journey and encouraging excellence in their roles is essential for a thriving, data-driven environment.
Figure 2 Data Management Value Proposition
Figure 3 Illustration of SFIA levels
Understanding the Roles: Team Lead, Manager, and Executive
The role of Data Managers is pivotal in fostering a successful data-driven environment within organisations. By overseeing data teams and aligning their efforts with business initiatives, Data Managers contribute significantly to the establishment of a Data Management Office (DMO) that creates value through effective data products. Additionally, integrating business and marketing value propositions into Data Management strategies is essential for enhancing overall impact, as it drives tangible results and strengthens customer relationships. Ultimately, by prioritising these elements, Data Managers can significantly improve Data Management effectiveness and facilitate organisational success.
The SFIA framework highlights the roles of thought leaders and executives in setting strategies, inspiring organisational members, and mobilising teams, with support from team leads and Data Managers. Beyond just Data Management knowledge, SFIA encompasses essential generic attributes such as working independently, collaborating within teams, and effective business communication. This holistic approach incorporates various competencies that are vital for successful organisational functioning. Participants may find their roles vary, with some, like Yogan, actively engaging as thought leaders, while others focus more on Data Management.
The role of Data Managers is vital for the successful implementation of Data Management strategies within organisations. They are responsible for overseeing data teams, establishing Data Management offices (DMOs), and ensuring alignment with business initiatives. By integrating business and marketing value propositions into Data Management practices, Data Managers can significantly enhance organisational effectiveness and drive business success. Ultimately, fostering a culture of trust and collaboration among team members boosts productivity and reinforces the importance of delivering value through data.
Figure 4 Illustration of SFIA levels pt.2
The Challenges of Data Stewardship in the AI Era
Involving thought leaders and specialists in the assessment workshop plan is essential for ensuring effective Data Quality delivery. This collaboration underscores the importance of adopting a strategic framework, such as PDCA or AI frameworks, to guide the implementation process. Moreover, the development of skills in conceptual modelling and data architecture is increasingly vital, as these roles are evolving into data ontology specialists.
As organisations navigate the integration of AI in Data Modelling, it is crucial to create clear paths for all stakeholders, enhancing their understanding and execution in the evolving data landscape. Ultimately, a well-structured approach that prioritises expertise and strategic frameworks will lead to more successful Data Management practices.
The role of a Data Steward involves transitioning from a businessperson or subject matter expert to a data citizen, equipped with the necessary skills for effective Data Management. This includes training on providing crucial inputs for data artefacts, such as defining business glossaries and writing clear business definitions in Data Modelling.
The training encompasses both practical and theoretical elements, encouraging participants to explore innovative techniques and apply academic research to real-world scenarios. Ultimately, the goal is to integrate diverse areas of knowledge to enhance Data Stewardship capabilities.
Leadership and Data Management
Operational leadership plays a critical role in various management levels, including supervisors, team leads, first-line management, and senior management, distinguishing itself from strategic leadership. This type of leadership emphasises self-management and maturity, encouraging professionals to cultivate self-leadership skills rather than relying solely on others for direction.
The importance of these skills became particularly evident during the COVID-19 pandemic, which challenged individuals to demonstrate their self-leadership capabilities in unprecedented circumstances. Furthermore, operational leadership extends into Data Management, where roles such as a data team lead are essential for overseeing projects like data migration and ensuring accountability for team outputs. Ultimately, enhancing operational leadership fosters not only individual growth but also contributes to the overall effectiveness of an organisation.
Operational leadership is essential across various management levels, from supervisors to senior management, as it promotes self-management and maturity among professionals. Unlike strategic leadership, which focuses on long-term vision and tactics, operational leadership encourages individuals to develop self-leadership skills, enabling them to take initiative and navigate challenges independently.
This necessity for self-leadership became particularly pronounced during the COVID-19 pandemic, where many were required to adapt and demonstrate their capabilities in unforeseen situations. Moreover, operational leadership is crucial in Data Management roles, such as a data team lead, who is responsible for overseeing projects like data migration and ensuring team accountability. Ultimately, by enhancing operational leadership, organisations not only support individual growth but also improve their overall effectiveness and resilience.
The distinctions between a Data Practice Manager and a Data Team Lead are crucial for effective Data Management. A Data Practice Manager is responsible for defining methodologies that guide data initiatives, ensuring a strategic approach to Data Governance and usage. In contrast, a Data Team Lead manages multidisciplinary teams tasked with delivering data products, working within frameworks like Scrum and domain-driven development. This differentiation highlights the vital role of thought leadership in shaping how data initiatives are approached, going beyond mere execution to foster innovation and best practices in the field.
Data migration approaches and strategic documentation are essential for successful Data Management, particularly in the context of advancing technologies such as AI. A thought leader, such as Andrew, plays a pivotal role in shaping this strategy by emphasising the development of a robust Data Quality plan, a comprehensive understanding of Data Engineering and migration, and effective knowledge management. Furthermore, the SFIA framework serves as a valuable resource for properly positioning personnel in these essential areas, ultimately enhancing the overall effectiveness of the practice.
Figure 5 Illustration of SFIA levels pt.3
Figure 6 Data Manager Value Proposition
Understanding the Data Management Hierarchy
The structure of the Data Management team consists of various managerial roles, each responsible for ensuring the effective delivery of data products. A Data Team Lead coordinates data professionals from different areas to work collaboratively on projects, while the Data Practice Manager oversees the availability of skilled resources and technology necessary to maintain high Data Quality and governance standards.
This includes ensuring adequate capacity to handle multiple data initiatives aligned with the business’s strategic needs. The team often operates in Sprint teams across different domains, engaging diverse professionals to develop curated and well-defined data products, emphasising that effective Data Management requires a collective effort rather than a single resource.
The role of a Data Engineer encompasses more than just technical tasks; it involves leading a data team, managing a data practice, and overseeing a data office to ensure a positive ROI. Key responsibilities include defining a clear value proposition aligned with organisational expectations, building a comprehensive business case for data migration—particularly from localised to cloud platforms—and developing a robust strategy that encompasses implementation frameworks, practices, policies, and procedures. This requires significant effort and collaboration to effectively establish the necessary groundwork for successful Data Management.
Figure 7 Data Manager Value Proposition pt.2
The Role and Responsibilities of Data Owners
The distinct roles of a Data Owner and a Data Manager are essential for effective Data Governance and project execution. A Data Owner, serving as a business authority, oversees a specific data domain and acts as a subject matter expert, ensuring the integrity and relevance of key data objects, such as cost centres or general ledger accounts, in a financial context. In contrast, a data practice manager or team leader is responsible for managing the team and executing data-related projects, including delivering use cases that require a diverse range of expertise, such as quality assurance and engineering. Together, these roles ensure that data operations not only adhere to business objectives but also enhance the overall effectiveness of Data Management practices.
Effective leadership in Data Management is crucial for fostering a successful and productive environment within organisations. Operational leadership, which spans various management levels from supervisors to senior management, emphasises the importance of self-management and maturity among professionals.
This type of leadership encourages individuals to cultivate self-leadership skills, enabling them to take initiative and navigate challenges independently, a necessity that became particularly evident during the COVID-19 pandemic. Moreover, roles such as data team leads are essential for overseeing projects, ensuring accountability, and driving team performance, which directly contributes to the overall effectiveness and success of an organisation. Ultimately, enhancing operational leadership not only promotes individual growth but also strengthens the organisation’s capacity to achieve its goals.
This division of roles is essential as organisations transition from project mode to ongoing business operations, ensuring that Data Quality is maintained and that all terms are clearly defined. The Data Owner plays a crucial role in overseeing Data Stewards to facilitate this continuity in Data Governance.
The development of data products involves a collaborative approach where team leads come together to establish business data products, which are then handed over to a Data Owner responsible for their oversight. In a banking context, the distinction between Data Owners and product owners is highlighted; while Data Owners manage the foundational data, product owners are tasked with overseeing the transformation, contextualization, and continuous delivery of insights, ensuring the data products maintain their quality and trustworthiness. This dual ownership structure emphasises the need for adequate resources and frameworks to sustain the ongoing value provided to consumers or customers.
Understanding the Data Manager
A Data Manager, as defined by the Mark Atkins writing business approach, serves as a resource manager overseeing both people and technology relevant to Data Management practices. Their primary function is to ensure value generation from data-related processes and stakeholders, which include data professionals, executives, and product stakeholders.
A key responsibility of a Data Manager is to oversee the data product life cycle, encompassing delivery, maintenance, and the establishment of effective methodologies for these tasks. They must also consider principles, policies, procedures, and technology (P3T) throughout their management efforts, ensuring comprehensive oversight and strategic alignment in Data Management initiatives.
Data initiatives primarily occur at the organisation level, within a data office, or among data teams, all guided by an implementation framework designed to ensure the successful execution of activities needed to build data products. One notable example of such a framework is the PDCA (Plan-Do-Check-Act) cycle, which emphasises continuous improvement.
A key role in this context is that of the data practice manager, who is responsible for leading Data Management practices and demonstrating their value. The effectiveness of a data practice is measured by ensuring that the benefits it provides exceed its costs, reinforcing the need for a solid business case.
Data initiatives are typically implemented at the organisational level, often within a dedicated data office or among specialised data teams, guided by a comprehensive implementation framework.
To effectively apply the PDCA framework to Reference and Master data, as well as Data Governance, a carefully structured approach is essential rather than simply following a checklist. Central to this effort is the role of the data practice manager, who leads Data Management initiatives and demonstrates their value to the organisation. Ultimately, the effectiveness of a data practice is assessed by ensuring that its benefits significantly outweigh its costs, highlighting the necessity for a robust business case.
Figure 8 Data Manager
Figure 9 Data Practice Manager
Data Quality and Value Definition in Organisations
Ensuring Data Quality is essential for organisations, as suboptimal quality directly translates to increased costs. To address Data Quality issues, it’s crucial to conduct a quality impact assessment to evaluate the costs involved and devise a strategy for improvements while determining the new quality levels to be achieved. Although managing Data Quality is relatively straightforward, other practices, such as quantifying the value derived from Metadata, present more complex challenges that require careful definition and consideration.
Establishing a clear value definition for initiatives such as developing a controlled vocabulary or a business glossary is essential for organisational success. Without a well-articulated value proposition, organisations often find it difficult to justify their contributions, especially during challenging times when results are scrutinised, as seen in Data Warehouse projects that frequently encounter delays.
Engaging stakeholders in recognising and understanding the value added by data initiatives fosters ongoing support and strengthens efforts to achieve overarching business objectives. Ultimately, a defined value framework not only enhances project credibility but also ensures alignment with the organisation’s strategic goals.
The Challenges of Data Warehousing and Technology Implementation
An organisation that traditionally embraced dimensional modelling using Kimball methodology decided to implement Data Pault techniques due to challenges with their existing approach. Despite the successful integration of data vault consultants, questions arose from the Kimball supporters regarding the tangible benefits of the new system.
Engaging business stakeholders is crucial for enhancing Data Warehousing maturity. A consultant underscored that understanding the specific pain points of these stakeholders can significantly shape the project’s success and deliver the desired value. This focus on collaboration highlights a potential oversight that often occurs during the initial implementation process, where stakeholder input may be insufficiently considered. Ultimately, prioritising stakeholder engagement can lead to more effective Data Warehousing solutions.
To evaluate the delivery of value in a project, it is essential to establish clear metrics that allow for assessing outcomes after completion. This process enables teams to determine whether they met their goals and understand any shortcomings if they did not. A notable example involves a debate between advocates of different Data Modelling approaches—Data Vault and Kimball—where the former highlighted benefits through extensive justifications and cost analyses, while the latter remained sceptical about the added value of the work involved. This situation raises the question of whether individuals have encountered similar challenges in justifying the value of technology within their organisations.
Establishing effective reporting practices within an organisation presents significant challenges, especially when faced with external scrutiny that can disrupt team stability. One individual recounts their experience of implementing AI solutions to streamline operations, yet they encountered difficulties in quantitatively demonstrating the value of these initiatives. Although their efforts led to noticeable improvements and increased user engagement, articulating these results in measurable terms proved to be a complex task. This highlights the intricate nature of communicating the impact of technological advancements in a business context, emphasising the need for clearer frameworks in reporting outcomes.
Reducing expenses associated with paper, printing, and delivery costs from sending thousands of invoices each month brings substantial financial benefits to the organisation. These savings are easily quantifiable; however, it is more difficult to measure the less tangible advantages like enhanced workflow efficiency and significant time savings. Furthermore, the recent layoffs within the data team, which resulted in the number of members dropping from 15 to just 3, highlight the emotional toll and challenges associated with such transitions. To effectively illustrate the value of these changes, it is crucial to link improvements to specific, measurable business processes, as these areas hold the potential for greater organisational success.
Master Data Management (MDM) and Metadata Management are often misunderstood concepts, but their value becomes clear when linked to business process improvements. When organisations enhance these processes and demonstrate the benefits, they gain support for implementing these initiatives. A key tool in articulating the value derived from data initiatives is a value taxonomy, which categorises benefits such as cost reduction, risk mitigation, and revenue generation.
An example of this framework is the Enterprise Value Map (EVM) from Deloitte, which helps define the nuances of value associated with data products, including potential pricing strategies. A well-defined value taxonomy is essential for effectively communicating the benefits of data initiatives to stakeholders.
The Commercial Feasibility and Value of Data Initiatives
When implementing a data initiative, it is essential to conduct a commercial feasibility study to ensure value delivery. Data Practice Managers should be equipped to recognise and articulate the commercial viability of their initiatives when required. Maintaining clear records of these studies is crucial, as stakeholders may question the value created over time. Failing to provide concrete evidence of value can lead to significant consequences, such as project cancellations, retrenchments, or the need to restart projects, ultimately undermining the efforts invested. Understanding the purpose behind the work is vital for success.
Effective Data Governance relies on the distinct roles of Data Owners and Data Managers to enhance organisational performance. Data Owners serve as business authorities who oversee specific data domains, ensuring the integrity and relevance of critical data objects, while Data Managers focus on resource management, guiding teams through data-related projects.
This collaboration is vital, especially as organisations transition from project mode to ongoing operations, ensuring Data Quality is maintained and clearly defined throughout the process. Ultimately, the clear division of these roles not only fosters accountability and continuous improvement but also strengthens the organisation’s overall capacity to achieve its goals.
Data initiatives within organisations play a crucial role in developing effective data products and achieving business objectives. These initiatives are structured by implementation frameworks, such as the PDCA (Plan-Do-Check-Act) cycle, which emphasises continuous improvement and the importance of proactive management. A key figure in this context is the data practice manager, whose leadership and ability to demonstrate the value of Data Management practices are essential for success.
By ensuring that the benefits of data initiatives outweigh their costs, organisations can reinforce the need for solid business cases that justify their efforts. Ultimately, fostering stakeholder engagement and establishing a clear value definition for data projects not only enhances their credibility but also aligns them with the organisation’s strategic goals, paving the way for effective Data Management and sustained growth.
In innovative sectors like music streaming, agility and clear value propositions are essential for engaging with business leaders. Data champions must balance the need for control with the necessity of granting teams the freedom to explore and innovate. This involves creating an environment where well-curated “freshwater” data coexists with a more experimental “salty water” space, allowing for investigation and adaptation. Effective management in these contexts requires careful consideration of when to enforce controls to avoid stifling creativity and to enable teams to discover new methods and solutions.
The Responsibilities of a Data Practice Manager
The value proposition for a Data Practice Manager encompasses several key responsibilities essential for effective Data Management. These include leading the Data Management practice, establishing implementation frameworks, and providing qualified resources to data teams. A crucial aspect is ensuring consistent performance during data migrations, delivering projects on time, within budget, and with high quality to maintain stakeholder satisfaction. Additionally, the practice manager must focus on building credibility for the practice, ensuring positive recognition for Data Quality, business intelligence, and AI efforts. This reputation extends beyond internal stakeholders to include external partners, reflecting the overall success and reliability of the Data Management practice.
Figure 10 Data Practise Manager Value Proposition
Evaluating Value in Data Management Practices
The jobs to be done are essential for defining product value, which can be categorised into functional, emotional, and social aspects. It’s important to identify the pains users experience and how to alleviate them while also recognising the gains achieved through successful task execution.
For instance, in Data Management practices, successful implementation leads to effective process execution, regardless of the complexity involved. To ensure consistent success, it’s vital to utilise a DMMA roadmap for measurement and evaluation, thereby establishing a clear value proposition. Ultimately, breaking down each responsibility from a knowledge perspective helps clarify what needs to be accomplished.
The emotional challenges people face often stem from a lack of trust in their ability to address pain and social issues. If these challenges remain unaddressed, individuals may seek support elsewhere, highlighting the need for effective communication and support within a business context.
This principle can be applied to data practices, where it is crucial for Data Practice Managers to determine and demonstrate ROI. By measuring their effectiveness, managers can identify strengths and areas for improvement, ensuring that both functional, emotional, and social aspects of practice value contribute to their overall reputation within the organisation.
The effectiveness of our practice is under evaluation, focusing on whether we can achieve consistent success without the fear of frequent failures. Key considerations include the potential for comebacks and the possibility of dissatisfaction with our deliverables. To assess our performance, we will employ various measurement methods to ensure we provide value while addressing any concerns that may arise from our stakeholders.
Figure 11 Data Practise Manager Value Proposition pt.2
Figure 12 Data Practise ROI
Data Management and Value Strategy
The data team lead emphasises the importance of delivering value to the business through a structured approach that involves conducting both commercial and technical feasibility studies for every use case. By ensuring clarity on the expected value before execution, the team can gauge the actual outcomes against promised results.
This method enables the integration of various data practices, such as Data Quality, sharing, reference and master data, architecture, and advanced analytics. Additionally, it highlights the necessity of change management, as successful product development relies on user adoption and consumption of the systems, rather than mere implementation.
Maximising the value derived from Data Management in a business requires a comprehensive understanding of both data ROI and data office ROI. Data ROI refers to the tangible benefits gained from utilising data products, while data office ROI highlights the capabilities and technical proficiency of the data team responsible for implementing these products.
By focusing on both aspects, organisations can ensure that successful execution, coupled with the essential skills of the data office, significantly enhances overall business value. Moreover, factors such as service level agreements (SLAs) and project initiatives play a crucial role in driving these positive outcomes. Ultimately, a balanced approach towards data and data office ROI can lead to greater efficiency and effectiveness in leveraging data for business success.
An effective SLA for ongoing Data Management focuses on the quality of data at the data practice level, ensuring that the data team delivers a tangible ROI for data initiatives. It is essential to measure progress in data maturity—specifically the value of advancing from maturity level 2 to levels 3 and 4—since improvements in data optimisation can lead to increased efficiencies and notable cost reductions.
Figure 13 Data-Value Driven Management Playbook
Figure 14 Data-Value Driven Management Playbook pt.2
The Journey of Data Delivery and Management
Evaluating value in Data Management practices is essential for ensuring that organisations can effectively meet user needs and achieve operational success. By categorising product value into functional, emotional, and social aspects, it becomes evident that addressing user pains and recognising the gains from successful task execution are critical. Implementing a structured approach, such as a DMMA roadmap, allows organisations to measure effectiveness and thereby establish a clear value proposition.
This focus on both emotional challenges and the necessity of demonstrating ROI encourages data practice managers to identify strengths and areas for improvement. Ultimately, a comprehensive understanding of value in Data Management not only enhances organisational reputation but also ensures that data practices consistently deliver meaningful outcomes.
The primary objective of the team lead is to successfully deliver data products that provide appropriate value. This involves implementing a data talent strategy, which serves as an internal resource justification for building and maintaining a skilled team. When reviewing the presentation deck, it’s essential to assess whether the performance metrics are being measured effectively. Additionally, the data office’s ROI, which encompasses a portfolio of all the sponsored practices and the teams involved, plays a crucial role in evaluating overall performance and effectiveness.
Figure 15 Data-Value Driven Management Playbook pt.3
Figure 16 Data Team ROI
Data Management and Value Proposition
The call to action for Data Managers emphasises the importance of resource management to enhance the efficiency and effectiveness of both technology platforms and data professionals. Data Managers are tasked with providing operational leadership for data initiatives and practices, offering guidance through coaching and mentoring to tackle complex data challenges. Examples of such challenges include implementing data solutions and performing customer lifetime value calculations and analytics.
When seeking to provide valuable advice within an organisation, it’s essential to identify the right person, whether it’s a thought leader or a manager, and ensure that the value delivery aligns with the organisation’s objectives. It’s crucial to fully understand your value proposition before engaging in operational tasks, as this can lead to being overwhelmed with responsibilities beyond your role, particularly in Data Management.
Many BI developers face challenges with Data Quality issues rooted in the applications rather than their direct responsibilities, highlighting the need for a trusted data product. For those looking to develop a data talent strategy and transform their office into a value-driven environment, assistance is available. Feedback and inquiries regarding the Data Management value proposition are encouraged.
Practitioners are increasingly concerned with demonstrating the value of their efforts in the face of growing budgets and personnel. As stakeholders demand clarity on the returns generated by their investments, organisations must not only showcase their strengths but also identify areas for improvement.
One effective strategy is to integrate the value proposition into the risk register, leveraging a familiar framework to emphasise governance and highlight vulnerabilities. This proactive approach not only reinforces the organisation’s commitment to accountability but also effectively addresses stakeholder inquiries, ultimately shaping the direction of the organisation.
Figure 17 Data Manager Actions
Figure 18 Data Manager Value Proposition – Feedback
Commercial Value Allocation and Immaterial Assets in Data Product Management
To enhance the effectiveness of data-driven initiatives, it’s crucial to assign a commercial value to the work from the outset. By doing so, you establish clear expectations and a basis for evaluating the delivered results against those expectations. This proactive approach can prevent potential disputes that may arise later regarding the project’s value.
At the data strategy level, defining use cases and conducting a commercial study helps in measuring feasibility and aligning goals. Without this framework, it becomes challenging to demonstrate the value of past initiatives, often resulting in difficulty convincing stakeholders of their impact. Additionally, involving product owners in driving requirements and value back into the business is essential for ensuring alignment and successful outcomes.
Establishing a solid relationship with marketing and utilising their product development techniques can enhance the value delivered by your team. Each capability within business architecture should have a corresponding value proposition, even in areas such as Data Management and Data Quality.
It is essential to measure performance against these propositions. Additionally, aligning with financial teams to translate data value into economic terms can significantly shift discussions, emphasising the importance of quantifying value in monetary terms rather than just qualitative assessments. This approach can lead to more persuasive arguments and a deeper understanding of value creation.
Evaluating value in Data Management practices is crucial for organisations to effectively meet user needs and drive operational success. By categorising product value into functional, emotional, and social aspects, businesses can identify user pain points and the gains achieved through successful task execution.
Implementing a structured approach, such as a DMMA roadmap, facilitates effective measurement and establishes a clear value proposition. Additionally, addressing emotional challenges and demonstrating ROI empowers data practice managers to discern strengths and areas for improvement, ultimately enhancing the organisation’s reputation. In conclusion, a comprehensive understanding of value in Data Management not only ensures meaningful outcomes but also fosters a culture of continuous improvement and success.
Culture Change and Value Delivery in Business
Fostering a culture change within organisations is essential for successfully integrating data and technology implementations. This shift requires managers to be patient and ensure that foundational elements are established before making decisions. Rushing into actions, such as purchasing applications without thorough input from stakeholders, can result in significant operational challenges and undermine the effectiveness of the technology. Ultimately, a deliberate and inclusive approach will lead to more sustainable and effective outcomes.
Successful execution of data-driven strategies necessitates robust support and engagement from the organisation; without this, there may be a need to defend their value after the fact. This underscores the importance of a comprehensive educational initiative that aligns business practices with data utilisation, ensuring that teams are not only investing in products but also understand and apply them effectively in the data realm. Ultimately, achieving overall effectiveness hinges on creating a seamless connection between investment efforts and a deep comprehension of data strategies across the organisation.
Data Management and ROI in Business
The scepticism surrounding the validity of business cases presented by IT departments is a significant concern for financial executives, as highlighted by a CFO from a Saudi Arabian bank. During a recent discussion, she expressed frustration with the lack of credible data backing the claimed returns on investment (ROI) for new technology initiatives. Her experience has shown that very few projects have delivered on their promises, emphasising a broader issue of questionable business cases. This situation indicates an urgent need for improved accountability and accuracy in justifying investments, which is essential for fostering trust and securing necessary funding for future initiatives.
Effective measurement and management of products within organisations is essential for ensuring profitability and avoiding resource waste. For instance, one company incurs annual costs of $3 million on underutilised software, highlighting the financial implications of poor product oversight. This challenge is especially pronounced in large telecommunications companies, where product failures can arise from various factors, including product design, personnel, or implementation strategies.
Assessing the value of Data Management practices is essential for organisations that want to fulfil user requirements and attain operational success. This involves categorising product value into functional, emotional, and social aspects, which helps identify user pains and recognise gains from successful task execution. By implementing a structured approach, such as a DMMA roadmap, organisations can measure their effectiveness and establish a clear value proposition. Furthermore, addressing emotional challenges and demonstrating ROI encourages data practice managers to identify their strengths and areas for improvement, ultimately enhancing the organisation’s reputation. In conclusion, a comprehensive understanding of value in Data Management not only ensures meaningful outcomes but also fosters a skilled and motivated data team dedicated to delivering impactful results.
To effectively showcase data literacy, professionals should first develop business and financial literacy, which helps build credibility with stakeholders. By proactively engaging with business teams and demonstrating a comprehensive understanding of their processes, data professionals can identify improvement opportunities and propose solutions, rather than merely reacting to requests. This approach aligns with the principle of seeking first to understand before being understood. To foster further discussion on the responsibilities of Data Management and team leads in demonstrating value, contact details are provided for follow-up conversations.
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