Data monetization pov
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Data monetization is generating revenue from data sources by capturing, analyzing, and disseminating data. It allows companies to sell data generated from customer interactions. To successfully monetize data, companies must understand their data assets, potential consumers, and how to add value through insights. A framework is needed to move raw data to solutions by assessing the data value stages from transactions to analytics. Various sectors can monetize data through use cases like banks providing merchant insights or telecom location data enabling targeted offers. Privacy and strategy are important considerations to effectively monetize data in the new digital economy.
Data monetization pov
- 1. Authored by: Radhakrishnan R Data Monetization Is Data the new Currency? A Point of View
- 2. 1 DataMonetization Data Monetization Is Data the new Currency? Contents INTRODUCTION ....................................................................................................................................2 UNDERSTANDING PLAYERS IN THE DATA ECONOMY ...................................................................................3 THE DATA VALUE STAGES ......................................................................................................................3 THE DATA MONETIZATION FRAMEWORK..................................................................................................4 DATA MONETIZATION USE CASES ...........................................................................................................6 CONCLUSION .......................................................................................................................................7 ABOUT INFOGAIN .................................................................................................................................7 REFERENCES ........................................................................................................................................7
- 3. 2 DataMonetization Introduction A new type of asset is emerging in the digital economy – ‘Data’. Companies are dealing with humungous volumes of customer data from various sources. This data is further collected, reported and analyzed. Analyzing and using data is nothing new, it is a well know way to enhance efficiency, build relationships, and convert opportunities. The concept is gaining more traction now because digital technologies enable massive data collection along with the power to analyze data effectively. This has led to a new paradigm, Data Economy that uses data to generate more value. As defined by Wikipedia, “Data monetization, a form of monetization, is generating revenue from available data sources or real time streamed data by instituting the discovery, capture, storage, analysis, dissemination, and use of that data.” Data Monetization to put it simply is the process where an organization sells data that is generated by interactions with their customers and business partners. This brings in the potential of a new revenue stream based on information based offerings. While the concept of data monetization excites CXO’s, there are several questions that will need to be considered. This includes: Where do I start? What is my data asset value as of today? Is the data unique to my business? Who is the target data consumer? What are the use cases that can be mapped to the data I have? Will I run into privacy issues? What are the risks and investments? How do I start? Do I have the infrastructure? Do I have resources and skills? Do I have the data that is ready consumable by the end user? Does my data provide insights into consumer behaviour? Can I transform the raw data into a usable and scalable format? The above questions will definitely need to be answered and detailed out. A methodological approach, with the right business model and the right partner is necessary to operationalize the Data Monetization dream.
- 4. 3 DataMonetization Understanding Players in the Data Economy Before getting into the approach, let’s understand the players involved in the Data Economy. The Data Producers are the players who generate and control the data. The data can be collected through various channels. The Data Aggregators collect data from disparate sources to uplift the data. The Data Insight Providers provide additional insights by using various statistical and computations methods, machine learning, semantic models and other techniques. The Platform Providers provide the infrastructure to host the data. In addition, they also provide the API infrastructure to directly access the data. The Data Presenters focus more on the user engagement and user experience. Data Consumers are the ones that consume or buy this data. There is a possibility of one player playing multiple roles. The key question to handle is what role do you want to play in this market? The Data Value Stages Organizations over a period of time have collected and analyzed huge volumes of data. The value your data can provide to Data Consumers can be best understood with the model of ‘Data Value Stages’. Stage 1: The journey of data monetization starts with an organization offering the transaction data. The data is raw with no additional processing. Examples of such data include retail POS, clinical health. This is an area where an organization can start with limited investment. Stage 2: This stage requires processing and uplifting of data from its raw state into a more consumable form. Some of the activities in this stage include data transformation, data enhancement, data manipulation, developing taxonomies, business rules etc. This stage will require an investment.
- 5. 4 DataMonetization Stage 3: Going up the stack is the stage that adds analytical capabilities to the data that can support in business decisions. Various insights can be offered on the data by applying machine learning, statistical modeling and other analytical techniques. This stage will involve data scientists. Stage 4: This stage provides a full blown solution that will present insights in an intelligent format. Additionally, this will also have a robust API gateway for consumers to access data. This will require substantial investment with focused sales and support. This stage offers high risk and high reward. By assessing your current data and mapping to your monetization strategy, one can decide to take the right position. The more insights the data can offer, the more valuable it gets. The Data Value stages are demonstrated for representative purposes only. Organizations may choose to select the stage(s) based on their market needs. The Data Monetization Framework Data Monetization is about converting the enterprise data into profits. The transition from raw data to offering a full solution is not a straight line. While it is understood that technology plays an important role in collecting, storing, analyzing and delivering data, the most important point to consider is having the right strategy and business model that helps to effectively monetize data and create a competitive edge. A ready reckoner framework for data monetization is provided below:
- 6. 5 DataMonetization Data Monetization focuses on solving problems that create a high utility value to end customers who could not have generated this on their own. Data Consumers move towards providers who can provide these valuable insights The top question that Data Owners should strive to solve is “what problem can my data solve?” In addition, Data Owners must also consider the following aspects: What? - The value your data can provide Who? - The potential buyers of the data Why? - The reason for getting into the data monetization play How? - The strategy to get into the play Organizations must also weigh the pros and cons of entering the data economy market as shown below: An important aspect to consider in this play is Data Privacy. Data Monetization involves analysis of customer data (demographics and personal) and deriving insights. Data providers must exercise extreme caution while dealing with privacy. They must ensure the necessary security and encryption to protect customer data based on the local laws. Additionally, they must have a proactive policy that encourages customers to share their data. Customers can be incentivized to share the data. The policies must be clearly defined and data should be shared only after mutual consent. As enterprises start addressing more data consumers, there will be a need to provide data in different ways for difference audiences. This makes it necessary to have an evolving and dynamic data monetization framework.
- 7. 6 DataMonetization Data Monetization Use Cases The following are some representative use cases for data monetization: No. Data Producer Data Consumer Usage Scenarios 1 Banks and Financials Companies Merchants Data owned by banks can generate insights on the transactional data of customers. The offers provided by merchants can be pushed through the bank channels. Data Consumers can expect a better conversion rate and Data Producers can get commission on conversion 2 Retailers Fashion Design Firms Retailers who deal directly with customers have insights into what designs customers prefer 3 Telecom Providers Retailers, Restaurants Telecom providers have the GPS data of mobile users. Retailers can push their marketing offers in real time. As the offer is more relevant, the Data Consumers expect a better conversion and Data Producers can get commission on conversion 4 Utility Companies Insurance Companies, Retailers Utility companies have customer’s home attributes data such as temperature, application usage. These insights can help data consumers like home insurance companies, retailers in several ways 5 IoT Devices Retailers, Pharmaceutical Companies, Insurance Companies, Automobile Companies With IOT, it is possible to get location (GPS Coordinates), vehicle (driving speed, breaking force, diagnostic information) and living data (blood pressure heart rate, temperature. This information when applied to various scenarios can provide opportunity for monetization 6 Travel Companies Retailers, Airlines, Restaurants (F&B) Travel companies possess the itinerary information, the travel contracts and policies for corporate customers that can be of high value to the listed consumers 7 Insurance Companies Medical Research Groups, Pharma Companies, Hospitals Analytics when applied on patient data can generate insights that are valuable to these sectors
- 8. 7 DataMonetization Conclusion The digital economy generates huge amounts of customer data. Data is inarguably a core asset. The value of data that provides signals of opportunities, risks etc. is best determined by the data owner. Enterprises have often considered this an option to gain a competitive advantage. The process of rolling out a data monetization plan is complex. The focus for the data monetization strategy should be on the defining the value of data to the end customers rather than getting influenced by technological aspects. Further, the cost of capturing and storing this data is significant. However, there is an opportunity of monetizing this valuable data. Companies will need to evaluate their current structure and overall positioning by taking a systematic approach. The success depends on the business model, adopting the right framework, commitment, connecting with the right partners and of course the quality of data provided. About Infogain Infogain provides front-end, customer-facing technologies, processes and applications that lead to a more efficient and streamlined customer experience for enterprises in the US, Europe, the Middle East, Asia Pacific and India. Offering solutions for the high-tech, retail, insurance, healthcare and travel & hospitality verticals, Infogain specializes in areas such as software product engineering, digital service automation, cloud, mobility, testing and business intelligence & analytics. The company has 9 delivery centers and more than 4000 employees globally. Infogain has a customer retention rate of 90%+ over a five-year period. References https://en.wikipedia.org/wiki/Infonomics https://en.wikipedia.org/wiki/Data_monetization