This framework is developed by a team of former McKinsey and Big 4 consultants. The presentation follows the headline-body-bumper slide format used by global consulting firms.
Explore a comprehensive Data Valuation framework by ex-McKinsey and Big 4 consultants. Learn actionable steps to embed valuation practices in your organization. Data Valuation is a 27-slide PPT PowerPoint presentation slide deck (PPTX) available for immediate download upon purchase.
Driven by the pervasive adoption of Big Data and Analytics, as well as the trending focus on fostering Learning Cultures, many organizations now are eager to determine how much their data assets are worth. Data Valuation, moreover, plays a key role in evaluating M&A deals, bankruptcy filings, and sale of data assets.
This presentation provides a detailed overview of a methodical approach to implement and embed Data Valuation practices into the Organizational Culture.
This approach encompasses 3 practical steps that organizations in any industry can take to implement Data Valuation practices.
1. Define and Disseminate Data Valuation Policies Across the Board.
2. Develop Internal Data Valuation Capabilities.
3. Select and Establish Appropriate Data Valuation Processes.
The presentation also alludes to the Data Valuation Framework and its 3 sources of value.
The slide deck also includes some slide templates for you to use in your own business presentations.
This presentation delves into the intricacies of Data Valuation Frameworks, breaking down the process into actionable steps. It highlights the importance of categorizing data into critical, important, and other groups, making it easier to assess its value. The framework identifies three primary sources of value, providing a comprehensive approach to understanding and leveraging data assets.
The PPT also covers Data Monetization, emphasizing how effective use of data can significantly impact business revenue. It outlines three essential building blocks for successful Data Monetization: Organization & Talent, Strategy, and Leadership & Culture. The deck includes pathways to monetization, both internal and external, ensuring a holistic view of how to maximize data value.
The Data Valuation Framework highlights the lack of effective data valuation practices in organizations. It categorizes data into 3 sources of value: the "Value of Data as a Strategic Asset," which positions data as a core component of strategic initiatives; the "Value of Data in Usage," focusing on optimizing data for operational efficiency; and the "Expected Future Value of Data," emphasizing the importance of assessing potential future worth for investment decisions. Additionally, it notes challenges in classifying data, particularly the context-specific "other" category, and advocates for formalizing data valuation practices within a structured framework to navigate these complexities.
This PPT slide outlines 3 critical components for effective data monetization: Organization & Talent, Strategy, and Leadership & Culture. Organization & Talent emphasizes a hybrid model combining decentralized and centralized structures, enabling analytics leaders to leverage a center of excellence and retain technical talent. The Strategy component highlights the need for a comprehensive, long-term analytics strategy to adapt to shifts in business practices, essential for leveraging data in decision-making. Leadership & Culture stresses senior management's commitment to data initiatives, with active involvement being the top contributor to achieving organizational goals. Fostering a data-driven culture is crucial for the success of analytics programs.
Data as a strategic asset can generate critical insights through effective monetization. Monetizing data involves assessing the value of customer data, essential for leveraging this resource. Two primary monetization strategies are direct and indirect: direct monetization entails selling or trading customer data for immediate financial benefits, while indirect monetization focuses on creating new products or services utilizing customer data without selling it. Blending public and copyrighted data can enhance decision-making and create external sales opportunities. Data monetization represents a significant market opportunity, with the financial sector projected to yield around $300 billion in revenue, underscoring the need for effective data valuation practices.
Data in Usage is a key component of the Data Valuation Framework, emphasizing that data's value is linked to its frequency of utilization across applications like Customer Relationship Management (CRM) systems. The frequency of data use is affected by application workload, transaction rates, and access frequency, indicating that increased consumption enhances data value. Unlike conventional tangible assets that depreciate, data can appreciate in value with higher usage, positioning it as an asset yielding increasing returns on investment. Cost considerations reveal that expenses related to data collection, storage, and maintenance are minimal, making marginal costs of usage negligible. Timing of data usage, particularly in contexts like seasonal retail, can further enhance its value. Additionally, perceptions of data value may differ between creators and users, which is crucial for optimizing data utilization and maximizing value generation.
This PPT slide outlines a structured approach to implementing Data Valuation in organizations, emphasizing its integration into organizational culture as a recurring process. The process consists of 3 steps:
1. "Define and Disseminate Data Valuation Policies Across the Board," which establishes clear policies communicated throughout the organization to ensure stakeholder understanding of data importance.
2. "Develop Internal Data Valuation Capabilities," focusing on building expertise and resources for effective data evaluation, essential for leveraging data assets and making informed decisions.
3. "Select and Establish Appropriate Data Valuation Processes," which involves identifying and implementing tailored processes to operationalize the defined policies and capabilities.
Organizations must first understand the business and technology events that trigger the need for Data Valuation, aligning efforts with broader goals and technological advancements.
This PPT slide outlines a critical step in the data valuation process: developing internal capabilities within organizations. Many enterprises rely on external consultants to assess the value of their data assets, highlighting a gap in internal expertise. Organizations aspire to monetize their data through selling or licensing, but often lack the skills to execute these transactions effectively. The rapid evolution of disruptive technologies necessitates cultivating in-house expertise to evaluate data internally. Investing in internal competencies enhances agility and responsiveness, enabling organizations to capitalize on data opportunities rather than depending on external parties. Prioritizing internal data valuation capabilities positions organizations for future success in the evolving landscape of data monetization.
Source: Best Practices in Big Data, Analytics, Data Monetization PowerPoint Slides: Data Valuation PowerPoint (PPTX) Presentation Slide Deck, LearnPPT Consulting
This framework is developed by a team of former McKinsey and Big 4 consultants. The presentation follows the headline-body-bumper slide format used by global consulting firms.
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