Breaking Down Data Monetization
Many organizations are now on board with using their customer data to grow revenue and reduce costs in their daily operations and it’s not hard to see why. Basically, every organization in today’s business environment is data-driven and has access to valuable information gotten from competitors, supply chains, customers, and partners. However, what most companies do not realize is that they aren’t making the most of this information. It’s been shown that only one out of 12 companies monetize their data to its maximum potential. Apart from the value data has on its own, insights gotten from data in turn make those data sets even more valuable. The information gotten from studying data can be used to guide an organization’s activity such as making predictions, segmenting customers, estimating demand, optimizing prices, and more. Selling data externally is also another way data can enable an organization to grow its profit.
When approaching data monetization, an organization can go about it through either of the two paths available. They can do so internally by leveraging data and using it to boost the company’s products, services, operations, or dialogues with clients. The other option open to organizations is external and has to do with generating revenue for the company by availing your data to partners and customers. That there are two paths does not mean they are mutually exclusive as an organization also has the option to take both paths. You can internally monetize data by using it to improve things like client services and operations or leverage it externally through things like geofencing for tourism, smart targeting, planning locations for retail stores, and the Internet of Things.
In order to be successful at data monetization, companies must be careful in the approach they use and concentrate on opportunities that offer the company the most value which is also in line with its business strategy.
How to prepare for data monetization
Several companies already create a source of revenue for themselves and also increase the value they provide to their customers through data. Many of such company’s partnerships with organizations that specialize in data monetization and use their operation centers to analyze, estimate, and forecast things like risk. In its raw form, data isn’t always saleable or valuable. It therefore, needs to be in harmony with other information such as market shares before it can properly yield insights. Companies can begin the process of monetization by partnering with an organization that can enhance its raw data. The next step after that is to understand who the potential customers for your data are and how they can benefit from the data you provide such as whether or not it enables them to make better business decisions based on the data solutions the company provides. You also need to be aware of how much your organization’s data is worth, it’s competitive offering, shelf life, and how It will be used by the people who purchase it. The kind of insights and trends gotten from an organization’s data should be such that it isn’t easily available from other sources or cannot be replicated.
Paths to data monetization
There are also subprocesses within the two methods of data monetization. For internal monetization, there are two major methods a company can take when monetizing their data which are;
Reducing costs – data can be used to boost productivity and cut down waste and consumption on low-value activities or raw materials.
Growing revenue – this has to do with bringing down customer attrition and improving sales performance. Many brands now optimize their operations based on data monetization insights to proactively make better decisions that benefit the company. Data helps companies gain a better understanding of their customers and thus predict their behavior or challenges they may face and put systems in place to solve them. Companies like Amazon, Netflix, and Airbnb are just a few examples of brands that use data effectively to their advantage by using it to understand their customers. They study customer information like purchase history and interactions, special needs, pivotal events or demographics and use it to offer hyper-personalized products and services that delight their customers throughout the end to end experience. This information doesn’t only influence the purchase experience but it also encompasses the discovery, post-purchase, and re-engagement stages as well which greatly ups their competitive advantage.
The second method of monetization which is the external path has the best opportunities for data monetization. This path has three primary monetization models including; offering analytics-enabled platforms as a service, insights as a service, and offering data as a service. The potentials for revenue on each model are varying as well as the degree of business sophistication and data analytics plus the value it offers customers. Let’s take a look at each one.
Insight as a service – combining external and internal data sources and using advanced analytics to develop actionable insights can help a company create models that positively influence decision making.
Data as a service – another name for this is data syndication and it is said to be the easiest of the three. Aggregated and anonymous data is sold to end customers or companies who then work on the data and develop insights. An example of this is a telco providing data on their user’s locations to city planners who in turn, use this information to design smarter traffic systems.
Analytics enabled platform as a service – the most complex of all three, this model gives its customers the most value. Here the company uses sophisticated algorithms to generate highly transformed and much richer real-time data using self-service and cloud-based platforms. Using this model, you can access new markets or even build a whole new business. An example of this is an energy and lighting company giving its customers more value by using data-based services that make its machines function in a much more effective manner as well as making predictive analysis on things like maintenance, and energy use and presenting this information to customers.
How to set up a data factory
Companies need to set up a data factory to make the most of their internal and external monetization. This makes the processes involved in collecting, transforming, enriching, and deriving insights from data more automated and easier. This process can be quite complex as it involves lean start-up, agile methodologies, and design thinking for success. Here’s how you can go about setting up a data factory.
- Create a data platform – there should be a technology and architecture stack to support the monetization business model. This’ll make use of an enterprise strategy that is robust in addition to an intuitive interfaced data platform which will make synthesis, analysis, interaction, and modeling of data happen at a higher level. The idea behind this platform is for there to be a single source of truth where data is stored, processed, and harmonized. This way, internal and external parties can easily access and use data. Making the perfect data platform will involve putting private and public cloud options in place to enable large scale, multi-party data sharing so companies can either lease, build, partner with or buy such platforms.
- Activate analytics, outcome, and insights – the data platform architecture should have self-service analytics that are interactive such as data visualization and interactive user interfaces. Is will enable your partner companies or clients locate and collaborate with each other in an open data environment thus enabling you to get more accurate analytics that translates to better results.
- Have a model of operation – it’s a no brainer that no one method or strategy of data monetization will work for every company. Some of the best companies at monetization have a separate structure for each stage of the monetization process so whatever business model you choose to use, it needs to encompass all of your operational and data monetization requirements such as infrastructure, platforms, analytics, profit, KPIs, etc.
- Compliance – companies ought to have a robust model for governance that ensures all the guidelines, standards, and compliance policies are adhered to by all teams. External compliance requirements provided by outside partners who share data with an organization and government requirements should be given particular attention. Technical and legal guidance would also be required to ensure compliance and creation of company policies concerning monetization.
- Privacy and cybersecurity – often, companies tend to make the mistake of treating cybersecurity as an afterthought when designing their solutions. Cybersecurity should be among the core areas of focus as a company sets up its data factories because it shows that your organization is capable of protecting its customer’s sensitive data. If a company wishes to partake in the sale of data, then it must prove to the people who supply this data that they are capable of protecting it by meeting their requirements.
Making data a strategic asset
These days, every company is a data company. Too many companies have a huge amount of data at their fingertips with the potential to tremendously increase the company’s value however this data is grossly underutilized.
There are three major ways of using a data factory to completely transform a business model;
- Spot prospective opportunities for monetization in your internal and external environment
- Examine your data for the areas which can be enriched to provide insights that increase value
- Build a solid strategy for monetizing your data and spot business opportunities, gaps, and dependencies.
It is absolutely possible for your organization to grow its earnings and create a future where it maximizes how it creates value both externally and internally. This can easily be done when you reinvent the game, create a market for the insights gotten from the valuable data you provide, dominate your market, and change the playing field.