Pathways to Data Monetization (PowerPoint PPTX Slide Deck)

ANALYTICS PPT DESCRIPTION

This product (Pathways to Data Monetization) is a 27-slide PPT PowerPoint presentation slide deck (PPTX), which you can download immediately upon purchase.

We are living in the Age of Data. Every company operating today is essentially a data company. However, only 1 inf 12 are monetizing data to its full extent.

For organizations to achieve Data Monetization, there are 2 pathways they can take—one with an internal focus and the other with an external focus. This framework explores both of these 2 pathways in detail.

With an internal focus on Data Monetization, we look at Operational and Organizational areas of improvement, which can be broken down in Cost Reduction and Revenue Growth initiatives.

With an external focus on Data Monetization, we look at opportunities with Partners and Customers. We can also unlock 3 new business models.

These presentation dives deeper in to these business models. We also delve into how to actually implement a Data Monetization Strategy through a 5-phase approach in setting up a Data Factory.

The slide deck also includes some slide templates for you to use in your own business presentations.

The PPT also covers the impact of Data and Analytics on various industries and functions. Insights from the McKinsey Global Survey highlight significant changes in core business practices, especially in sales, marketing, and R&D. This data-driven transformation is reshaping competitive landscapes, with traditional competitors launching new products and forming data-related partnerships.

Setting up a Data Factory is a critical step in executing a Data Monetization Strategy. The document outlines a 5-phase approach to implementation, focusing on enabling insights and analytics, adopting a smart operating model, and ensuring governance and compliance. The slide deck includes detailed templates to assist in each phase, making it a comprehensive guide for organizations aiming to leverage their data assets effectively.

Source: Best Practices in Analytics, Data Monetization, Data Factory PowerPoint Slides: Pathways to Data Monetization PowerPoint (PPTX) Presentation Slide Deck, LearnPPT Consulting

ANALYTICS PPT SLIDES

Revolutionizing Competitive Landscapes with Data-Driven Insights

This PPT slide presents insights into how data and analytics have reshaped competition within various industries over the past 3 years. A significant 70% of surveyed executives acknowledge that these technological advancements have led to notable changes. The data is categorized into 5 levels of impact: fundamental, significant, moderate, minimal, and no change. The majority of respondents (36%) reported moderate change, while only 8% recognized a fundamental shift.

The slide further breaks down specific changes in competitive dynamics attributed to data and analytics. A striking 50% of executives noted that new entrants are launching data-centric businesses that challenge traditional players. This indicates a shift in market dynamics, where innovation driven by data is becoming a key differentiator. Traditional competitors are responding by enhancing their core operations through data insights, with 36% of respondents affirming this strategy.

Additionally, 36% of executives highlighted that traditional firms are introducing new products, including analytics services, to stay relevant. Partnerships along the value chain are also emerging, with 21% of respondents indicating that companies are forming data-related collaborations. Other strategies include launching new data-and-analytics-focused businesses (18%) and pooling data into shared utilities (7%).

The overarching message is clear: organizations must adapt to this evolving landscape. The emphasis on data-driven strategies is not just a trend, but a necessity for maintaining relevance and achieving growth. Companies that fail to leverage data effectively risk being outpaced by more agile, data-focused entrants. This slide serves as a crucial reminder for executives to reassess their strategic priorities in light of these developments.

Three Models for External Data Monetization

This PPT slide presents an overview of 3 external business models for data monetization, emphasizing the importance of making data accessible to partners and customers. The overarching aim is to leverage data to create new revenue streams.

The first model, "Data as a Service (DaaS)," is described as the most basic approach. It involves selling data directly to end customers or intermediaries who analyze the data for insights. This model is characterized by its simplicity,, but it also has the lowest revenue potential and minimal value impact on customers.

The second model, "Insight as a Service (IaaS)," represents a more advanced offering. It builds on the foundation of DaaS by applying sophisticated analytics to the data, which generates actionable insights. This model has a moderate revenue potential and provides a more significant value impact compared to DaaS.

The third model, "Analytics-enabled Platform as a Service (APaaS)," is noted as the most complex of the three. It involves utilizing proprietary algorithms to enrich data for customers via a self-service platform. This model not only offers the highest revenue potential, but also delivers the greatest value to customers.

The slide concludes by indicating that the external pathway to data monetization presents more substantial opportunities than internally focused alternatives. This insight suggests that organizations should consider these external models to fully capitalize on their data assets. Understanding these distinctions is crucial for decision-makers looking to enhance their data strategies and drive revenue growth.

Impact of Data Analytics on Business Functions

This PPT slide presents an analysis of how data and analytics have significantly transformed core business functions across various industries over the past 3 years. It utilizes a matrix format to illustrate the degree of change experienced by different sectors, categorized by specific business functions such as sales and marketing, R&D, and supply chain management.

Each industry is represented along the horizontal axis, including sectors like travel logistics, public and social sector, advanced industries, media and telecom, consumer retail, financial services, professional services, healthcare systems, high tech, and basic materials and energy. The vertical axis lists key business functions, allowing for a clear comparison of how each sector has adapted to data-driven practices.

The circles in the matrix indicate the extent of change, with varying shades representing significant, moderate, or minimal change. Notably, over 50% of respondents indicated that sales and marketing functions have undergone the most substantial transformation. R&D also showed notable changes, with more than 30% of respondents reporting shifts in this area.

The insights suggest that industries such as high tech and basic materials and energy are at the forefront of adopting data analytics, reflecting a broader trend of digital transformation. This data-driven approach is reshaping how businesses operate, emphasizing the need for organizations to adapt to remain competitive. Understanding these shifts can guide strategic decisions and investments in analytics capabilities.

Strategic Pathways for Effective Data Monetization

This PPT slide outlines 2 distinct pathways for organizations aiming to achieve data monetization, emphasizing both internal and external focuses. The first pathway is characterized by an internal focus, which revolves around enhancing operational and organizational efficiencies. This involves leveraging data to improve various aspects of the organization, including operations, productivity, and offerings. Additionally, it highlights the importance of engaging in tailored dialogues with customers by utilizing customer data, thereby fostering a more personalized approach to service delivery.

The second pathway shifts the focus externally, targeting partnerships and customer relationships. This approach centers on making organizational data accessible to external partners and customers. The objective here is to leverage this data to create new revenue streams, suggesting a more outward-looking strategy that capitalizes on collaboration and market opportunities.

The slide also notes that these pathways are not mutually exclusive, indicating that many organizations might find value in pursuing both strategies simultaneously. This dual approach can enhance operational efficiencies while also exploring new revenue opportunities through external collaborations.

Overall, the slide serves as a strategic framework for organizations contemplating their data monetization strategies. It encourages a balanced consideration of both internal improvements and external partnerships, highlighting the potential for comprehensive growth through effective data utilization. This insight can guide executives in making informed decisions about their data strategies and aligning them with broader corporate objectives.

Foundational Steps for Data Platform Implementation

This PPT slide outlines the foundational steps for establishing a Data Platform, which is crucial for a Data Monetization Business Model. It emphasizes 2 primary components: creating a data platform and enabling insights and analytics.

The first section highlights the need for a robust IT infrastructure, which includes an Enterprise Data Strategy and a dedicated Data Platform. The objective is to develop a "single source of truth" for data, which necessitates effective data management practices such as cleansing, standardization, and harmonization. The slide notes that the platform should be user-friendly, facilitating higher-level data interactions through intuitive interfaces. It also mentions the importance of accommodating both public and private cloud options to ensure scalability and multi-party data sharing.

The second section focuses on the architecture of the platform, which must support several core features. These include interactive, self-service analytics, user-friendly interfaces, and effective data visualization. The emphasis on data visualization suggests that presenting data in an accessible manner is seen as both an art and a science, crucial for deriving actionable insights.

Overall, the slide serves as a strategic guide for organizations looking to leverage data effectively. It presents a clear framework for building a data platform that not only supports data management, but also enhances analytical capabilities. This approach is designed to empower users at various levels within the organization, ultimately driving better decision-making and value creation from data assets.

Evaluating Options for Data Platform Implementation

This PPT slide outlines 4 distinct options for establishing a Data Platform: Build, Buy, Lease, and Partner. Each option is presented with its strengths and limitations, offering a clear comparison to aid decision-making.

The "Build" option emphasizes customization, highlighting that in-house solutions can be tailored to meet specific organizational needs. It notes the importance of cybersecurity and control, which can be critical for sensitive data handling. However, the slide points out that this approach typically demands significant investment in technology infrastructure and talent, making it the most expensive choice.

The "Buy" option is characterized as a cost-effective alternative to building from scratch. It allows organizations to leverage off-the-shelf solutions that can be enhanced with in-house development. While this method can meet strategic needs, it also requires a comprehensive understanding of the software's functionalities and limitations, which can be a considerable commitment.

"Lease" is presented as a flexible option, with a shorter commitment period and a variety of tech solutions available. This approach allows organizations to avoid heavy upfront investments and vendor management complexities. However, it warns that leased solutions may not fully align with specific requirements and can lead to operational challenges due to vendor proliferation.

Lastly, the "Partner" option offers a balance between commitment and customization. It allows for tailored solutions without the heavy investment of building or leasing. Yet, it requires effective vendor management and an understanding of market intelligence capabilities.

Overall, the slide encourages a careful evaluation of each option, emphasizing the need for a strategic approach to budgeting and decision-making regarding data platforms.

Strategic Framework for Data Factory Implementation

This PPT slide outlines a strategic approach to maximizing data monetization through the establishment of a "Data Factory." This concept is presented as a systematic method for organizations to automate various processes related to data management, including collection, aggregation, enrichment, transformation, and analysis. The ultimate goal is to derive actionable insights from the data, which can drive business value.

Three guiding principles are emphasized for effectively transforming a business model via a Data Factory. The first principle focuses on identifying both internal and external opportunities for data monetization. This suggests a comprehensive view of potential avenues for leveraging data, whether from within the organization or through partnerships and market analysis.

The second principle stresses the importance of appraising data. Organizations should actively seek hidden opportunities to enrich their data, which can enhance insights and increase overall value. This indicates a proactive approach to data management, where the quality and depth of data are prioritized.

The third principle involves a thorough assessment of business opportunities, dependencies, and capability gaps. This assessment is crucial for developing a robust data monetization strategy. It highlights the need for organizations to understand their current capabilities and identify areas for improvement to effectively implement their data monetization initiatives.

The slide also notes that setting up a Data Factory can be complex and requires successful implementation of various methodologies, including Design Thinking, Lean Thinking, and Agile practices. This complexity underscores the necessity for a well-structured approach to ensure that organizations can fully realize the benefits of their data assets.

Evolving Strategies for Capitalizing on External Data

This PPT slide presents an overview of 3 distinct external data monetization models: Data as a Service (DaaS), Insight as a Service (IaaS), and Analytics-enabled Platform as a Service (APaaS). Each model is outlined with key characteristics, revenue potential, and customer value impact.

DaaS is described as the simplest model, where data is anonymized and aggregated before being sold to customers or intermediaries. This approach has low revenue potential and customer value impact, making it a straightforward entry point for organizations looking to monetize their data.

IaaS builds on the DaaS framework by incorporating advanced analytics to derive actionable insights from the data. This model allows for data aggregation from both internal and external sources, which enhances its value. The revenue potential and customer value impact are moderate, indicating a step up in complexity and value compared to DaaS.

APaaS represents the most sophisticated model. It utilizes complex algorithms to provide enriched data through a self-service platform. This model is typically cloud-based and offers real-time data delivery, which significantly increases both revenue potential and customer value impact to high levels. APaaS not only enhances data accessibility, but also opens avenues to new markets, often referred to as "Blue Oceans."

The slide concludes by suggesting that these models can be viewed as a maturity framework, where businesses evolve from DaaS to IaaS and ultimately to APaaS as they develop their data capabilities. This progression highlights the increasing complexity and potential of data monetization strategies. Understanding these models is crucial for organizations aiming to leverage their data assets effectively.