funded with support from the European Commission. The author is solely responsible for this publication (communication) and the Commission accepts no responsibility for any use that may be made of the information contained therein. The objective of this module is to learn what are the opportunities in the data business world and how are traditional business models updated with this emerging data opportunity. Upon completion of this module you will: - Learn how to recognize the opportunites of where big data can benefit your company - Answer the right questions about your company and then go through the steps of integrating the solutions into your company - Understand the pillars of a data driven business strategy - Find out more about 6 different data business models Duration of the module: approximately 2 – 3 hours Module 4: Data as a Business Model
Data Smart Region | www.smartdata.how This programme has been funded with support from the European Commission. The author is solely responsible for this publication (communication) and the Commission accepts no responsibility for any use that may be made of the information contained therein. – Five Pillars of a Data driven Strategy Data Driven Business – Where can Big Data create Advantages inside your company? – Steps of Integration 3 – 6 main Data Driven Business Models • Product Innovators • System Innovators • Data Providers • Data Brokers • Value Chain Integrators • Delivery Network Collaborators – Another Perspective: Data enabled business model innovation Designing Data Driven Business Models
in some cases scary — development for business. Together with the complementary technology forces of social, mobile, the cloud, and unified communications, big data brings countless new opportunities for learning about customers and their wants and needs. It also brings the potential for disruption, and realignment. Organizations that truly embrace big data can create new opportunities for strategic differentiation in this era of engagement. Those that don’t fully engage, or that misunderstand the opportunities, can lose out. Smart Data Smart Region | www.smartdata.how
to realizing its value. For many companies, the insights drawn from big data have already resulted in profitable, sustainable growth in three areas: CUSTOMER INTIMACY PRODUCT INNOVATION OPERATIONS Smart Data Smart Region | www.smartdata.how
puts the customer at the heart of corporate strategy. Organizations are inundated with customer data from interactive websites, online communities, and government and third-party data banks. Information on social-media platforms such as Facebook is particularly telling, with users sharing nearly 30 billion pieces of content daily. At the same time, it is now possible to bring together social-media feeds with disparate sources, including weather data, cultural events, and internal data such as customer contact information. Further, advanced analytical tools allow for faster, more effective, and less costly processing and create the potential to rapidly develop new insights. 1
other social product innovation techniques are made possible because of big data. It is now possible to transform hundreds of millions of rich tweets, a cacophony of unstructured data, into insights on products and services that resonate with consumers. At the heart of this work is the ability to use sophisticated machine-based computational linguistics to conduct temporal sentiment analysis on a company's product portfolio and its customers. The resulting output informs product marketing and product innovation strategies. Data, and the related analytics, is also becoming a standalone product. Technology and analytics firms have emerged to provide rich insights from data—for example, compiling and analyzing transaction data between retailers and their suppliers. Retailers that own this data, and more importantly the analytics, can use it to improve operations, offer additional services to customers, and even replace third-party organizations that currently provide these services, thus generating entirely new revenue streams. Finally, imagine the potential big data brings to running experiments—taking a business problem or hypothesis and working with large data sets to model, integrate, analyze, and determine what works and what doesn't, refine the process, and repeat. 2
offers a variety of information-rich interactions, including physical product movements captured through radio frequency identification (RFID) and microsensors. The scope extends across suppliers, manufacturing sites, customers, and in-service partners and results in reduced inventory, improved productivity, and lower costs. Such opportunities are reserved for those who understand that data is an asset to be capitalized upon. Allowing customer information to stagnate in a storage area is a wasted opportunity. And the value of all data is not so much the information captured but how the information is viewed from the customer's perspective. This means considering not only the impact on profits, but also how changing customer preferences affect the market place. For example, when considering the effect of a product promotion, collecting data on competitors' promotions, specifically on substitute products, can reveal how customer preferences have evolved. 3
well thought-out, three-pronged approach. STEPS OF INTEGRATION Identify where big data can be a game changer •What key business and functional capabilities are required? •What IT capabilities are needed to support and grow the business? •Where are the major gas in capabilities to support the business? Build future-state capability scenarios •What are the options for future business capabilities and technologies? •How do the options compare for capabilities, costs, risk, and flexibility? •What functional, analytical, and technology decisions are needed to support these capabilities? Define benefits and road map •What is the investments payback period? •What is the implementation road map? •What are the key milestones? •What skills are needed? Where are the talent gaps? •What are the risks? •What is the third-party engagement strategy Smart Data Smart Region | www.smartdata.how
for years, and the market was getting more competitive. A member of the executive team complained that "online retailers are eating our lunch." Poor economic conditions, changing consumer behaviors, new competitors, more channels, and more data were all having an impact. There was a strong push to move aggressively into e-commerce and online channels. The retailer had spent millions of dollars on one-off projects to fix the problems, but nothing was working. Several factors were turning the company toward competing on analytics, from competitors' investments and a sharp rise in structured and unstructured data to a need for more insightful data. How did a big box retailer utilised these steps? Smart Data Smart Region | www.smartdata.how
Data Smart Region | www.smartdata.how For the big-box retailer, new capabilities were needed if the business had any chance of pulling out of its current malaise and gaining a competitive advantage—the kind that would last despite hits from ever-changing, volatile markets and increased competition. The team engaged all areas of the business from merchandising, forecasting, and purchasing to distribution, allocation, and transportation to understand where analytics could improve results. Emphasis was placed on predictive analytics rather than reactive data access. So instead of answering why take-and-bake pizza sales are declining, the retailer focused on predicting sales decline and volume shifts in the take-and-bake pizza category over time and across geographic regions. In another example, the business wanted to move from reacting to safety issues to predicting them before they occur. The retailer planned to use social media data to "listen" for problems, which would not only make the company more customer-centric but also provide a shield to future crises. The plan was to set up a business-information organization with four goals in mind: 1. Deliver information tailored to meet specific needs across the organization. 2. Build the skills needed to answer the competition, today and tomorrow. 3. Create a collaborative analytical platform across the organization. 4. Gain a consistent view of what is sold across channels and geographies.
www.smartdata.how The retailer was eager to develop scenarios for future capabilities, which were evaluated in terms of total costs, risks, and flexibility and determined within the context of the corporate culture. For example, is the business data driven, or is the company comfortable with hypothesis-based thinking and experimentation? Both are the essence of big data. They have also identified trade-offs for each scenario, including comparison of capabilities, migration priorities, and timeline estimates. For example, which is most effective: a global data topology at headquarters or a local-regional-global combination? For a big data go-forward architecture, what are the trade-offs in using Hadoop versus Cassandra? These were assessed in the context of crucial opportunities, such as leveraging leading- edge technologies and providing a collaboration platform, integrating advanced analytics with existing and go-forward architecture, and building a scalable platform for multiple analytic types. This technology would enable five key capabilities and serve as the basis for future benefits: – Predict customers' purchasing and buying behaviors. – Develop tailored pricing, space, and assortment at stores. – Identify and leverage elasticities, affinities, and propensities used in pricing. – Optimize global sourcing from multiple locations and business units. – Devise models to suggest ways to reduce energy use and carbon emissions.
www.smartdata.how Armed with these capabilities, the next questions revolved around resources. Did it make financial sense to assign internal resources? Or would it be more cost- effective to have external resources provide the big data analytics, at least initially? Naturally, the decision would depend on the company's capabilities. Technology needs were planned from two perspectives: data and architecture. A data plan was charted from acquisition to storage and then to presentation using a self-serve environment across both structured and unstructured data. Systems architecture, which may involve a Hadoop-based integration, was planned in light of the existing IT architecture, which was heavily reliant on relational data warehouses leveraging Teradata and Oracle platforms. A road map outlined a multi-million dollar investment plan that would deliver a positive payback in less than five years. The company is now positioned to realize four key benefits from its big data strategy: – Deliver consistent information faster and with less expense. – Summarize and distribute information more effectively across the business to better understand performance and opportunities to leverage the global organization. – Develop repeatable BI and analytics instead of every group reinventing the wheel to answer similar questions. – Generate value-creating insights yet to be discovered through advanced analytics.
of achieving a competitive advantage in business. Creating a business where data are leveraged to create real value is the ultimate goal. A data-driven strategy is one where data is a basic requirement for business, a value- generator rather than a cost line-item of the balance sheet. There are five pillars of a data- driven business that serve as a framework for creating real value from business data. Smart Data Smart Region | www.smartdata.how
which serves the strategic imperatives of the business. PILLAR 1 The future vision for a business should be at the forefront in defining how data are leveraged to create value. A well-defined data strategy must start with business strategy. Tying a data strategy to the most important company initiatives allows the business to focus its advanced analytics efforts and technology choices on the areas that will provide the most value for the firm. A data strategy enables data-driven decision-making using technology and applications that help a business achieve its strategic imperatives. Data strategy must be driven by the business not by the technology that services the business. Focus first on what is driving your business, then move to defining the tactical elements of the data strategy. Data strategy must be clearly articulated and communicated to employees at all levels of the organization so that your business as a whole can understand the importance of your data to creating value. Data strategy should be based on measurable outcomes and milestones. Clear steps with time frames to get from the current state to desired outcomes are laid out and communicated across the organization. If you cannot define a clear path to executing the strategy, then you don’t have the right one.
a culture of “data-driven- ness.” PILLAR 2 The most successful businesses create a culture of data, one in which data drives decision-making. They create a culture of measurement and adjustment based on data and analytics. The culture is focused on educating the entire organization to appreciate the value which can be generated through data. Businesses teach employees how to ask the right questions of data in order to understand how data will relate to unique jobs and goals. A shared understanding of data and its value helps create consensus and consistency and avoids analytical output being viewed skeptically by the business. The culture of decision-making leverages advanced analytics as its foundation. Businesses should create a continuous cycle throughout the organization of evaluating impact and changing based on the data and outcomes. The culture of predicting outcomes and results through predictive analytics becomes the norm. Continuous improvement includes feeding prediction errors back into predictive models for continuous refinement. The culture becomes a mind-set that consists of continuous testing; continuous improvement; weighing and prioritizing decisions; sharing data with others in the organization; and using analytics to inform and influence others.
human- and technical- capital requirements. PILLAR 3 An honest recognition of a business’s capabilities for generating real value from data is imperative to becoming a data-driven business. A business must leverage strengths, adapt skill sets and shore up gaps. This includes both human capital (skills and expertise) and technical capital (technology, systems and infrastructure). Human capital capabilities Ensuring the right human capital capabilities is paramount. It makes little sense to spend money on expensive systems without having the talent to derive substantial value from those systems. • While businesses often recognize the need to bring on more expertise, they struggle with identifying which skill sets are most critical when hiring and training. A business should base skill-set requirements on the data strategy roadmap, identifying the skill sets which are critical to execution. • A primary goal for the business should be to build a deep bench of analytical professionals throughout the organization. Professionals should not only know how to run analysis and use the analytical tools at their disposal but have the capability to think critically about business issues, applying tools and methods to sophisticated and sometimes abstract questions. • Human capabilities and skill sets need to be backed up by continuous training and development. Technical capabilities New technology solutions may be needed to enhance current IT and communications capabilities. Businesses should be open to investment if it is determined that new technology is aligned with the data strategy and will generate value. • Businesses should avoid implementing sophisticated IT systems until the business is prepared to leverage the features provided by the systems. This includes having the required data strategy, analytics talent, institutional will and data-sourcing to allow the business to realize the value that the technology can provide. • Attention should be paid not only to back-end infrastructure but also to data reporting, communication and visualization tools. Effective reporting tools should streamline data collection while simplifying query functionality, allowing employees to more easily access and refer to particular data. • Significant consideration should be given to eliminating data silos and centralizing data. Data is increasingly powerful as it is brought together with other data, opening the doors to today’s advanced analytics methods.
selecting and prioritizing data types. PILLAR 4 To best achieve powerful results from data, businesses need to source and select powerful combinations of data. Sourcing external data to combine with internal data can yield impactful analytics. It is especially important to not overlook sources of internal data that can give new and proprietary insights. A process for determining and ensuring data are accurate, timely and secure is critical. With- out certainty that data is accurate, it will throw into question any insights generated. Collect the right data to meet the needs of the business’s data initiatives. Choosing data based on the data needs generated by initiatives provides several benefits: Interesting data is not always the most useful. Grounding data-sourcing in the initiatives makes it easier to discern between the two. There are near unlimited sources of data, so focusing on just the data that will meet the needs of specific initiatives allows the business to home in on value-generating activities. The data currently being collected might not be the best data for the business needs. Understanding how the current data reconciles with the data needs allows the business to adjust which data are being collecting and how it is being collected. Don’t overlook the potential value of unstructured data such as text, voice and other under-utilized data types. Advanced data mining techniques, natural language processing and text analytics allow for this information to be used in powerful ways. Consider the power of data from unconventional sources when combined with the firm’s own data. For in-stance, sensor data from smart devices or data from Web and social media are examples of potential useful data that could be powerful additions to a business’s data strategy and associated analytics initiatives.
data while maintaining high levels of data security, quality and agility. PILLAR 5 Businesses often recognize the need to use data but struggle when it comes to implementing a company-wide data strategy. This often results from a poorly-defined data governance structure. It is easy for a business to fall into one of two camps: 1) data and analytics initiatives are kept structurally separate from the firm’s ongoing operation, often in their own division or department, hindering the ability for the business to create a culture of “data-driven-ness;” or 2) all data and systems are open company-wide, which leads to data quality and security issues. Data and analytics should not be left entirely to data scientists and IT departments – they require technical savvy and organizational coordination. To succeed, businesses need to embed data and analytics deep into their organizations to ensure that information and insights are shared across business units and functions. Businesses should identify how analytical decisions are currently being made. Examine how that decision- making process can be reinforced and altered with data and feedback. There should be clear understanding of who is accountable for facilitating any given analysis and leveraging its insights. From executive-level to analyst, there should be no questions of ownership. Firms need to effectively manage the supply and demand for analytics services across the business. This can involve tracking departments or units that are consistently under-utilizing analytic capabilities, which will reveal divisions that may be lagging behind in becoming data-driven. Breaking down organizational walls between initiatives, workflows and employees can be key to combining data in powerful ways. Data silos are often created by departments or units not just keeping their data techno- logically separate but also structurally. Pay attention to regulatory and compliance requirements, both to meet industry-specific requirements and to ensure individual-level data meet the requirements defined by the business – client/customer expectations.
This effort has proven, time and again, to bring about benefits that far outweigh the costs. The five pillars described here are the framework for achieving that type of success but specific application will depend on industry, vertical and context. Smart Data Smart Region | www.smartdata.how
Models – Product Innovators – System Innovators – Data Providers – Data Brokers – Value Chain Integrators – Delivery Network Collaborators 2. Another Perspective: Data enabled business model innovation
data storage and data analytics bring countless opportunities for businesses to learn about their customers wants and needs, diversify their products. • The explosion of data also brings opportunities to create new business models with these assets • Organizations that embrace big data can create new opportunities for strategic differentiation Complication • Organizations not directly in the business of doing something with data struggle to connect data opportunities to their current products and services • This is usually caused by a lack of strategy, a lack of capabilities or of supportive processes and systems • Often this results in failures of big data projects or difficulty to launch new initiatives due to skepticism. Question • To help companies define data- driven strategy, the following questions need to be answered: • What kind of data-driven business models are emerging in the market place today? • What value can be created using data to create a new business model with data? • What is the approach to create and implement a new business model with data? • What aspects does an rganization need to think of in order to implement a new business model with data? Smart Data Smart Region | www.smartdata.how Organizations are looking to leverage the value that lies within the data they generate, process or acquire.
Data-based delivery networks • The product is still the primary source of value, but using data drom the product is used to improve the product or service offering • Data-enabled differentiation is typically a solo opportunity – products from a single vendor are the dominant gateway to the opportunity • There are situations where company data only provides sufficient value when combined with other sources or the company does not have the capabilities to fully tap the opportunity on its own. • When the opportunity cannot be tapped by a single vendor with a single product, data brokering opportunities arise. • Multiple companies work together and share data to tap data opportunities. • Companies specialize in one or two capabilities needed to enable the delivery network. Solo opportunities Collaborative opportunities 1. Product Innovators enhance their products and services with data 2. System Innovators use data to integrate multiple product types 3. Data Providers gather and sell raw data without adding too much value to it 4. Data Brokers gather and combine data from multiple sources, create additional value with analytics and sell insights 5. Value Chain Integrators share data with system- integrator partners to extend product offerings or reduce costs 6. Delivery Network Collaborators share data to drive deal making, foster marketplaces and enable advertising 6 MAIN DATA DRIVEN BUSINESS MODELS
Schematic view of key elements of the business model using the business model canvas • Key activity • Value Proposition • Customers • Data Repository • Channels • …. Capability Requirements Indication of the capabilities needed to implement the business model using the four stages of the data value chain: • Data generation • Data storage • Data analytics • Data usage Characteristics Key characteristics of the business model Example Example of a company that has implemented this business model Value realized What value dos the example company derive from the business model? EXAMPLE
Key Activity Data Repository Value Proposition Customers Data Generation Data Storage Data Analytics Data Usage Usage or sales data from a single product type from a single vendor is used to add features to the product, improve the service offering or to create an additional product Tvilight is a Dutch start-up that has developed a smart streetlamp system. Lamps only light up in the presence of a person, bicycle or car, and remain dim the rest of the time. • Key activity (1) of Tvilight is the designing and manufacturing of embedded streetlamp sensors • The main value proposition (2) is a sensor-enabled wireless streetlamp which is sold to municipalities (3), enabling the customer to reduce their energy costs by 80% • Monitoring data from individual streetlamps is sent wirelessly to Tvilight’s Data Repository (4). • The data is used in a new value proposition (5) that improves the service offering: web- based software for remote monitoring, management and control of street lighting infrastructures Value realized: • The functionality of the original product (street lamp) is improved by sensors and wireless communication • Usage data gathered from the product is used to create a second value proposition (software for remote management) 1 2 3 4 5
Key Activity Data Repository Value Proposition Customer Relationship Data Generation Data Storage Data Analytics Data Usage Looks beyond a single product category to a broader smart systems offering – different product types from a single manufacturer are architecturally related and can interact in order to deliver value to the customer. In 2006 Nike introduced a new range of personal tracking and measurement products. • Key activity (1) of Nike is to manufacture sports apparel • Value proposition (2) delivered to customers (3) is a range of related products: A running app for mobile phone, network-enabled tracking bracelet and sports watch. • Product usage data is sent to Nike via mobile (4) and stored (5) • The data is communicated to the user through the Nike+ Platform (6), where the athlete can track and analyze its sporting activities and share them with others. • The Nike+ Platform provides a new channel to stimulate product sales in a context- specific way, or enable third-party advertising • Customer engagement is realized by community building and allowing the user to share personal achievements on social media Value realized: • Customer lock-in – Products gain utility when combined, switching costs are high • Customer engagement - social media integration, community • New channel to sell and promote products (Nike+ Platform) 1 3 5 Customers Channel 2 6 4
Key Activity Data Repository Value Proposition Customers Data Generation Data Storage Data Analytics Data Usage • In addition to the company’s core activity, raw data or aggregated data from its data repository are sold to another business customer for a fee or a share of the earnings. • Two types can be distinguished: Raw data sales and sales of insights/benchmarking Since 2012 Vodafone sells anonymized raw network data to a partner company (Mezuro) for a fee. • Key activity (1) of Vodafone is providing telecom services. • Value proposition (2) delivered to customers (3) is voice call, text message and internet service through the company’s mobile network. • Mobile phone usage data (4) is collected as part of the company’s core activity • Data (5) about the geographical location of the company’s mobile sites is added to the mobile phone usage data • The dataset is anonymized by hashing (6) and sold to a partner company, Mezuro (7) for a monthly fee • Mezuro uses the data in addition to other sources to provide crowd analytics to the public sector, estimating the usage intensity of city centers, train stations and roads Value realized: • Predictable revenue stream by using a subscription based model to sell data • Access to a new market / customer segment 1 2 3 4 5 7 6
based on existing blood glucose data stream. • Key activity (1) of Glooko is database management and analytics • Glooko licenses the data specs and standards from glucose meter manufacturers (2) to make its product compatible • First of the value propositions is a link cable (3) that is sold to diabetes patients (4) to connect their phone to their blood glucose meter • Blood glucose meter data from the patient (4) is transmitted by the patient’s phone and added to a meter reading database (5) • Second part of the value propositions (6) is a log book and incidence reporting solution that is delivered through a free app to patients (4) and for a subscription fee to hospitals (7) Business Model Capability Requirements Characteristics Example: Glooko 4. Data Broker Key Partners Data Repository Key Activity Value Proposition Data Generation Data Storage Data Analytics Data Usage • Companies acquire data from key partners, from open sources or through data mining. • The Data Brokering company focuses on excellent Data Analytics and Data Usage and leaves Data Generation to others Value realized: • Complimentary products are sold to the customer - a mobile app and a cable to link blood glucose meters to a mobile phone • A predictable revenue stream is generated by offering a subscription service to hospitals • Better effectiveness for hospitals and insurance companies 2 5 Customers 1 6 3 4 7
field level data to farmers – supporting decision making related to planting, field management and harvesting to maximize crop yields . • John Deere’s key activity is manufacturing farming equipment (1) • DuPont’s key activity is selling seeds and agricultural consulting (2) • Both companies cater to the same customer segment: farmers (3) • Value proposition of John Deere is farming equipment outfitted with sensors, GPS and wireless transmission technology (4) • John Deere equipment gathers data on crop yields, moisture and location, which is sent wirelessly to a data repository owned by Deere (5) • DuPont integrates John Deere’s data (6) in its value proposition (7), precision farming software that uses field-specific data to support decision making Business Model Capability Requirements Characteristics Example: John Deere & DuPont 5. Value Chain Integrators Company 1: John Deere Data Generation Data Storage Data Analytics Data Usage • Companies that serve the same customer segment exchange data with distributors and system-integrator partners with the aim to extend the existing product offering or reduce costs • The business model is not geared towards sales or licensing out data, but rather towards integration to optimize operational results Value realized: • Products from both companies gain utility by sharing data • Risks and revenues are shared and individual competitive advantage is improved • A barrier to competition is created, because use of product generated data allows to offer services more intelligently than competitors 1 7 Customers 3 Company 2: DuPont 5 4 2 6
enabling advertising: • KLM (light blue), an airline company, (1) sells flights (2) to travelers (3) • Booking data is stored in a database (4), combined with flight scheduling information (5) and shared with an advertising agency • The advertising agency (green) (6) can identify the traveler through a tracking cookie and determines the date and destination of the traveler’s flight (7) • Hertz (black), a car rental company (8), is looking to rent cars to travelers (9) • Hertz shares data on available cars with the advertising agency to add to its algorithm (10) • The advertising agency then shows available rental cars through websites that the consumer visits (omitted), on the city and date that the traveler will arrive there Business Model Capability Requirements Characteristics Example: John Deere & DuPont 6. Delivery Network Collaborators (1/2) Data Generation Data Storage Data Analytics Data Usage • Stakeholders work together in a value creating network rather than a traditional value chain. Often it is unclear who is the vendor and who is the customer or consumer, all stakeholders benefit • Companies share data to drive deal making, enable advertising and foster marketplaces Value realized: • Hertz obtains a new channel to reach consumers • A fee is paid by Hertz to the advertising agency and to KLM each time a consumer clicks the ad to rent a car 2 Customers 3 Company 1: KLM 1 9 Company 3: Hertz 8 7 Company 3: Advertising Agency 6 10 5 4
problems from businesses to a community of data scientists. • The two key activities for Kaggle (dark blue) are fostering a community for data modelling competitions and connecting companies to top data scientists • Company 1 (black) pays Kaggle to organize a data modelling competition. It provides raw data and the challenge and receives the winning data models • Company 2 (light blue) pays Kaggle for matchmaking to the community’s top data scientists • Kaggle’s community of data scientists (green) partakes in competitions to solve data problems Business Model Capability Requirements Characteristics Example: Kaggle 6. Delivery Network Collaborators (2/2) Data Generation Data Storage Data Analytics Data Usage • Stakeholders work together in a value creating network rather than a traditional value chain. Often it is unclear who is the vendor and who is the customer or consumer, all stakeholders benefit • Companies share data to drive deal making, enable advertising and foster marketplaces Value realized: • Kaggle – Fees through competitions and matchmaking • Company 1 – Gets solution to data problem • Company 2 – Finds skilled data scientists • Data Scientist community – Exposure, connect with other experts, prize money for the top data scientists Kaagle Corp Kaagle Community Company 1 Company 2 Kaagle Competition Kaagle Connect Data Scientist Winning Model Scientist Top 0.5% Exposure Fee Raw Data + Challenge Data Models Winning Model Prize Money Raw Data+Briefing, Money Data Model Fee Find best data scientist Community Access
Systems Data Pricing Unit based Volume based Activity based Value based Channels Integrated value chain Delivery network • Deep sell: selling more of current offerings to existing clients • E.g: Internal supply optimizations, data-enabled replenishments • Cross sell: Data-enabled sales of new offerings to existing clients • E.g. Amazon, Bol.com, (“other customers also bought…”) • New sell: data-enabled sales of new offerings to new clients • E.g. Insurance companies, banking services, online retailers • Added functionality to existing product categories • E.g. Smart metering, intelligent lighting • Combined offering categories, potentially in ecosystem • E.g. Lifestyle devices (Nike+, iPod with Itunes, FitBit) • Commercialization of data through provision or brokerage • E.g. Financial information (Experian); usage statistics (Vodafone) • Dynamic pricing per unit based on economic modeling • E.g. airline ticketing, online advertising • Pricing based on (expected) volumes • E.g. Quantity discounts (, freemium models (Spotify, LinkedIn) • Pricing on (expected) time & material • E.g. Engineering & Installation companies; service organizations • Pricing based on client’s (expected) valuation • E.g. Stock markets; Telecom companies • Data enabled partnerships providing an extended offering • E.g. Tomtom & Apple; John Deere & DuPont • Data enabled delivery network to distribute content / products • E.g. KLM & Hertz, Kaggle ANOTHER PERSPECTIVE: Data has the ability to transform business models at many different levels. Data enabled business model innovation Smart Data Smart Region | www.smartdata.how
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