Organizations who manage their data effectively will gain the competitive advantage over other companies. Many companies are looking for innovative ideas to sell their data and earn value. Value Management and Data Monetization are closely linked as you will find out in the subsequent sections. Your Definitive Guide to Data Monetization in 2020.
What is it really?
According to McKinsey, Data monetization is the process of using data to increase revenue. The highest-performing and fastest-growing companies have adopted data monetization and made it an important part of their strategy.
Why do I have to do it?
As per a few research papers like Allied Market Research, the Data Monetization market was USD 44 Billion and is set to grow exponentially. Initial estimates predict this market to grow to USD 370 Billion by 2023.
DIRECT DATA MONETIZATION
Direct data monetization involves selling direct access to your data to third parties. You can sell it in raw form, or you can sell it in a form that’s already transformed into analysis and insights. For example, contact lists of potential business prospects or findings that impact on buyers’ industries and businesses. All you can do is to sell raw data and to sell the insights.
Selling your raw data:
There are multiple options for selling your data. It is possible to sell the data via data marketplaces. There are two main kinds of marketplaces out there:
• Centralized marketplaces: a central platform owned by one party to trade all kinds of data between different participants
• Decentralized marketplaces: a decentralized platform that facilitates the ability of participants to engage directly with each other in peer-to-peer transactions
There are already different kinds of marketplaces in place and the one to choose depends on your requirements derived from your strategy. In addition, selling your raw data is an option to provide access to 3rd parties to APIs in exchange for money. This might be necessary if real-time access to devices is needed
Selling your analysis and insights
Using value-adding analysis on your raw data helps to increase the quality of information you want to sell. Not every company has the full capability of analyzing data. This kind of monetization is a win-win situation for both parties who are participating in this kind of transaction. Such value-added service can be offered via marketplaces or your own channels
INDIRECT DATA MONETIZATION
Indirect data monetization is where things get interesting. Firstly, there is data-based optimization. This involves analyzing your data to reveal insights that can improve your organization’s business performance. Data can identify how to reach customers and understand customer behavior so you can drive your sales. Data can also highlight where and how to save costs, avoid risk and streamline operations.
Comparable to direct data monetization, there are also two ways to utilize your own data: conduct data-based optimization or create data-driven business models
Data based optimization
The goal here is to mainly reduce costs and to improve the efficiency and effectiveness of your processes. This has multiple fields of application. One example could be the optimization of the test benches in your manufacturing process by reducing testing time. Another example is using field data to improve your product design.
Data driven business models
With this monetization strategy, you do use your data, e.g. from products or processes, to discover new business opportunities, customer types and segments. This means developing new services or products or at least enhancing your existing ones. Creating data-driven business models helps you discover radically new businesses instead of adjacent businesses. They are also valuable when it comes to diversifying your revenue streams.
Furthermore, there are data-driven business models, where you utilize your data to find new business openings and clients. You can insert investigation into your products or administrations, giving focal points to you and your clients. Clients profit by direct access to utilization examination and other data created by every item they as of now use. You profit by offering this as a worth extra or as another level of administration, empowering client reliability.
Thus, you show signs of improvement knowledge into how your clients are utilizing your products. Without the correct data monetization systems set up, you hazard missing basic insights that could improve your business. With the essential methodologies set up, you are well prepared to hone your serious edge. Here’s an outline of the advantages of data monetization, the key data monetization strategies, and what properties you should search for in a BI and examination stage, to help the best data monetization device
The Three Types of Data
A data monetization strategy must evaluate those options in the context of the risk and compliance issues inherent in certain data types, which Koch said can be broken down into three buckets:
Raw data contains personally identifiable information, and can, for instance, show where and how a specific person shops at a specific time — and is generally the most valuable type of data, but the one most at risk for theft or misuse.
Anonymous data is the next most-valuable type, and has had removed. This type of data is much more general than raw data, and lacks such details as consumers’ specific addresses (anonymous data will have a census block instead, he said) and the accurate, specific time of a purchase (for instance, a cup of coffee bought at a major chain).
Synthetic data is often a starting point for reluctant or nervous executives who are not yet ready to move up the chain to monetize anonymous or raw data consists of what’s called “fake data sets that cannot be tracked to the original consumer or FI.” Synthetic data will lack the genders of consumers, and will not offer accurate, specific times of purchases, among other factors.
Preparation to Data Monetization
It is very important to enrich your data with other resources to make it beneficial. Most of the data is not useful and valuable in its raw form. Companies can only increase their revenue by making data useful by adding other services.
Secondly, to be able to prepare the data to work for customer satisfaction (both internal and external), it is really crucial to know the target group for your data. You need to know which of customers’ needs and goals you are going to address. Prepare use cases for your data to make it more useful. Optimization, personalization, gaining insights – all these are the possibilities that open up when the company hears its customers. And it also helps with making better decisions, based on the gathered data. Different factors such as competition, market demand or customer satisfaction are involved in making data valuable to sell.
Methods To Achieve Data Monetization
With the passage of time, methods to monetize data have developed. Valuable methods provide you capacity and flexibility to get most out of your data. There are few data monetization strategies that offers great opportunities that depends on the maturity of organization. These methods are as follows:
- Data as a service
- Insight as a service
- Analytics-enabled platform as a service
- Multiple industry platform as a service
DATA AS A SERVICE
This is the simplest data monetization method. In this method, data is sold directly to the customers and intermediaries. The data can be raw, aggregated or anonymized and the buyers mine the data for insights. Buyers gain zero benefit from analytics and insights but they drive profit themselves.
A national banking client was seeking ways to boost customer acquisition. They used the following data sets to identify in-market targets:
- Mortgages: First Time Home Buyers:
- Identified a list of the bank’s customers and targeted prospects.
- Suppressed those that are current homeowners, leaving non-homeowners with specific traits (age, head of household, HHI, etc.).
- Monitored the resulting file for mortgage activity with a specific FICO level indicator – e.g. 680. When a “hit” is identified, bank was notified and a fair offer of credit can be made to the potential homeowner.
INSIGHTS AS A SERVICE
It is accomplished by combining internal and external data sources and applying analytics to them to provide insights. Either the insights can be sold directly or provided in formats such as analytics-enabled apps that provide updated data. The insights are limited to specific datasets or contexts that the buyer has purchased.
One chemical company created a decision-support model that enables ship operators to save funds on CO2 and fuel. Thanks to the mobile application they can optimize their investments, by relying on the analysis of coating choices
Analytics Enabled Platform As a Service
This one is a complex and provide value to customers as well. This model takes the insights from the second model and makes them available in real-time through a cloud-based platform. Partners of the platform can access it anytime, and if enriched with an API, they can even use it to create real-time based triggers, so you can use data from any source and any format. Expert set-up and support are needed to get the full benefit from cloud-based tools.
GE’s platform Predix is responsible for developing energy management systems for lighting and energy. The company allows customers to make cost-reduction decisions by simplifying energy processes, leading to automation and operational efficiencies. All these thanks to their prescriptive analysis around energy use, maintenance, and other outcomes.
Multiple Industry Platforms As a Service
The most advanced model of how to monetize data, a multi-industry platform as a service we like to call the synergetic data model. It is the most advanced model of data monetization. We embed features normally associated with BI software such as dashboard reporting, data visualization, and analytics tools – to existing applications. Product teams can build and scale custom actionable analytic apps and seamlessly integrate them into other applications, opening up new revenue streams and providing a powerful competitive advantage.
It includes companies that work on gathering data sources from various partners and anonymously make available to third parties. They match the data before giving to third parties
The above diagram states the flow of data. It depicts how data in a raw form is enriched with other resources to make it valuable. Further steps are added to make it worthy to sell
Rules Of Data Monetization
There are 5 basic rules for data monetization.
- Understand the role and value of data in your business
Good data management is about making sure you have the proper data to support your business and improve performance. Smart data utilization also helps in managing risk and provides assurance that the business is compliant with laws and regulations. But it can only serve this purpose effectively if you know where your data resides, how relevant it is and how valuable it could be. Often companies fail to accurately value their data because it is not strictly accounted for as an asset even though it has real worth in external markets.
- Get your data house in order
Many companies lack metadata i.e. data about data such as the data quality, where it’s stored and what it means. In fact, many companies are more likely to have a more detailed inventory of their office furniture than their own data. Before thinking about monetizing data, companies need to discover what kind of data they hold about their partners, customers, products, assets or transactions and what publicly available data can be called on to increase the value of their proprietary data. They must also work out whether that data is of value internally to cut costs, streamline operations or improve sales processes, or as an external revenue stream such as customer intelligence as a service, or both.
- Embed data monetization into business strategy and get the right structures in place
Executives should evaluate their key business goals and strategic initiatives through the lens of how data can support them. Consider the energy sector where a company that applies service-oriented asset management can gain a lot of insight into individual materials and parts or the equipment. This information can be used to barter better terms and conditions with the material and parts vendor.
Once you understand the quality of data and have tied it to business strategy then you can put the right structures in place to monetize it. Often this involves assembling a multi-disciplinary, cross-functional team – including information product leads, data management experts and executives from sales, marketing and operations – to create a platform for business innovation powered by the data. Together they can determine ownership structures for different data sets while making sure that sense of ownership doesn’t result in data bottlenecks or siloes.
- Be open to new opportunities
The potential for data to deliver value for many parts of the business is enormous. Sometimes, though, it’s hard for companies to imagine quite what the opportunities could be because they are so used to pursuing growth through established strategies and revenue streams. That’s why all companies should be open to learning from other businesses and partnering in ways that make sense from a data point of view.
Communicate data’s value internally and externally to foster growth
Monetizing data is still a relatively new experience for many organizations, and even when successful initiatives are in place they aren’t always known to the business as a whole. As data becomes more and more important, companies will need both to communicate and educate internal and external stakeholders so they fully grasp the value data can deliver
Benefits of Data Monetization
When your data is monetized well, you can gain maximum profit from it. You can maximize profits, reduce costs and optimize opportunities for your organization and customers. This graphical representation can help us to understand that how data monetization is helpful and essential.
There are several advantages of data monetization such as:
- Optimizes use of data
- Extracts more and better insights for you and your customer
- Streamlines decision making and planning
- Improves data sharing
- Improves collaboration between internal and external stakeholders
- Build strong partnership
- Increases operational productivity and efficiency
- Reduces operational costs
- Improves understanding of customers
- Improves customer experience
- Strengthens customer loyalty
- Enhances insights into how best to improve products
- Increases targeted marketing and product or service proposition
- Maximizes the value of your products and services
- Increases revenue streams multiple times
- Helps identify new opportunities for growth
- Identifies and reduces risk
- Increase profits
- Improves compliance
- Strengthens competitive advantage
Credits: Allied Market Research, Gartner and Bosch