Data monetization is the generation of new revenue streams from data-driven products. This can either refer to the extraction of insights from big data to improve internal business performance, or the sale of data and insights to third parties. In either case, the data must be valuable in the sense that it is unique, actionable for the purchasing party, or cleansed or de-duplicated to maximize data quality.
The first step for organizations interested in generating new revenue streams is developing a data monetization strategy. In the following article, we will explore several types of data monetization, how an effective data monetization strategy will generate new revenue streams, and the change management required for optimizing data value.
Whether optimizing business performance or packaging insights for sale to third parties, an effective data monetization strategy should present the clearest path for extracting insights from big data. The strategy should define the need for establishing a solid IT foundation with a well-governed, centralized data store, advanced analytics, and various business intelligence (BI) tools.
Once the infrastructure is established to properly glean data-driven insights, any legal risks, data protection barriers, competitive barriers, data availability problems, and data delivery methods should be carefully considered. Improper data monetization techniques can lead to hefty fines, cybersecurity risks, and irreparable reputational damage.
The strategy should also clearly define the end state. This will involve a combination of physical, technical, and logistical conditions that transform extended conversion of datasets into new revenue streams. Change management must also be factored into the new strategy to ensure the entire enterprise adapts to the new business model.
Direct data monetization involves collecting and storing company data before being sold to third parties. The data can be sold as a raw product or processed as structured data to immediately generate insights.
Examples of direct data monetization include customer or business contact details, market research reports that reveal hidden industry insights, and behavior studies to optimize the customer experience.
Indirect data monetization refers to the internal use of data to increase business performance. For example, a company could gather data on customers and perform analytics to generate insights around sales and sentiment.
From these insights, the company could optimize its workflows, strategies, IT services, and product-service portfolios to improve efficiency in areas like sales and supply chain management. These byproducts of data monetization result in efficiency improvements, cost reductions, increased revenues, and a net positive return on data generation investment.
There are several methods for monetizing data, but the one organizations choose should give the agility and flexibility to extract the most value from big data sources. To help you decide which method works best with your data strategy, here are the four most popular data monetization strategies:
This data monetization strategy is the simplest to implement and typically operates on a direct business-to-customer (B2C) model. The data could be raw and unstructured, aggregated for a high-level overview, or anonymized when the source data contains personally identifiable information (PII). This is a form of direct data monetization.
This path also offers the lowest potential for revenue generation. Raw datasets still need to be analyzed to generate insight, and Data-as-a-Service only provides raw data. This means buyers get no value until they load and analyze data via analytics or BI software and tools.
If the selling party lacks the people power to analyze data before the sale, this is a good opportunity to generate revenue without increasing employee workloads since data can be provided largely as-is.
Where Data-as-a-Service delivers raw data for analysis by buyers, Insight-as-a-Service provides summarized analytical insights, such as competitive insights or customer behavior trends. The insight is generated from numerous sources, including internal datasets and external primary and secondary data sources.
Enterprises can sell these insights as a one-off report, or continuously through embedded analytics applications for ongoing revenue generation. This is another example of direct data monetization.
For companies using data monetization in this context, more work is required to generate insights and visualizations. This approach must also be aligned with prospective buyer requirements, meaning partial insights could generate no revenue at all.
Since analysis has already been performed, Insight-as-a-Service provides more value to buyers and warrants a higher price than Data-as-a-Service.
This approach will look similar to Insight-as-a-Service, as customers can access insights in return for payment. The difference here is the scope of data access and analytics functionality.
For instance, customers get real-time controlled access to analytics and BI visualization tools operated by the selling data provider. This data provider could be a research company with large-scale datasets on an industry. This is another direct data monetization strategy.
The benefit is zero setup and zero maintenance for the buyer, much like how cloud computing means enterprises do not need to manage server hardware. It's functionally similar to an internal analytics environment, except that ownership is solely with the data provider.
As an all-in-one solution, Analytics-as-a-Service offers the most potential to generate revenue for data providers, but also carries the greatest IT management burden. Overprovisioning data services access could also lead to data breaches and leaked confidential information. Strict cybersecurity policies should be in place with this approach.
Data-Driven Business Models
A data-driven business model aims to leverage every available source of data in pursuit of efficiency and productivity. This could include sales, marketing, human resources, finance, or any other business department. This is an indirect data monetization method, serving to benefit the company by analyzing its own data.
For example, system logs and crash dump files are created when a server outage occurs. This data can be centralized and analyzed to identify repeating network problems and improve IT service desk productivity.
Another example would be if customer buying habits have changed, causing products to become overstocked. Sales metrics can be analyzed to visualize sales volumes over time and proactively identify trends to improve supply chain efficiency and optimize stock levels.
Extend this vision of full data utilization across the entire business, and you have a data-driven business model that relies on factual insights rather than the highest-paid-person's-opinion (HiPPO).
To summarize, there are three main direct data monetization approaches: the selling of raw data, the sale of packaged insights generated from raw data, and enabling direct access to a data analytics platform owned by a third party.
Indirect data monetization, such as with a data-driven business model, enables companies to strategize and optimize their operations to reduce costs or increase revenues, thereby indirectly monetizing the data via insight-driven action. As the complexity of the approach increases, the potential for greater revenues grows in parallel with the complexity of IT and cybersecurity management.
Apart from increasing revenue in the short term, there are many reasons to consider implementing a data monetization strategy. Here are the top four reasons why implementing a data monetization strategy can lead to a competitive advantage:
When monetizing data, the enterprise and its workforce will learn how to collect, store, analyze, and sell data. This knowledge can be re-used to drive digital transformation, particularly with data-driven business models.
A new reliable data selling initiative will open new revenue streams to bolster bottom line. This revenue could be used to justify further development of data monetization capabilities in yearly budgets, or to improve internal data usage to drive indirect data monetization.
Whether indirect or direct, marketing data can be monetized to improve paid search growth, organic growth, and optimize sales or landing pages to maximize inbound traffic. In fact, some of the most lucrative data monetization strategies involve pinpointing when, where, and how content reaches potential customers.
Data can be used to generate insight that reveals hidden risks. By selling this data or indirectly monetizing it internally, businesses can perform market or technology risk management and take proactive steps to mitigate their impact in the event of a data breach.
One of the world’s largest global shipping companies approached Trianz seeking to identify new revenue opportunities. They were interested in monetizing their data by building business intelligence products for various industries. Unfortunately, they found themselves without any process to effectively extract a vast amount of data that was siloed across multiple data sources.
To effectively deliver a data monetization strategy, Trianz determined the potential monetary value, opportunities, competitive landscape, and barriers to entry for providing the data products. This required Trianz to dig deep into the client's capabilities, segments, channels, and geographies while assessing costs, risks, and potential monetary return for the re-architecture of its data management platform.
After providing a thorough market opportunity analysis and proof-of-value exercise, Trianz delivered prototypes and roadmaps of selected concepts, along with in-depth primary and secondary research to validate the viability, feasibility, and desirability of several centralized data management solutions.
What resulted from the transformation was the identification of several monetization opportunities, including complementary and new business revenue streams for data product delivery across the medical, online purchasing, B2B commerce, and distribution center services industries.
Before taking advantage of data monetization and its many benefits, organizations must take several key steps before embarking on the journey. Here are four approaches to help organizations realize the most value from their data monetization strategy:
What data do you hold? What could that data be used for? If you were buying this data, what price would you realistically want to pay? This entire process requires a basic analysis of the dataset to determine the type and format, as well as a holistic determination of its potential monetary value.
Data needs data that tells people what the data is about. Since that is a long-winded explanation, we will simply refer to this as metadata. Metadata could include titles, descriptions, languages, themes, keywords, licenses, publishers, or other tags. Think of metadata as a library, where readers can find the relevant book (or data) by going to the right section of the shelf (or database). In action, this helps businesses to organize and search through complex datasets using human-readable search queries.
Because of fluctuating bandwidth, data storage, security, and processing requirements, enterprises should build IT infrastructure and software designed specifically for data monetization — rather than adapting existing infrastructure to enable data monetization. By building dedicated infrastructure with data monetization requirements in mind, enterprises can avoid bottlenecks in the mid to long-term by ensuring their data environment is scalable, accessible, governable, and secure.
Speaking to prospective data buyers will help the business understand what, where, how, and why a customer needs data. It is fruitless to build an Analytics-as-a-Service platform when most data buyers would prefer access to raw data sources. Similarly, selling raw data to digital-first companies may be leaving revenue on the table, as they likely have analytical capabilities of their own. They can buy the raw data, analyze it in-house, and sidestep your data monetization department’s Insight-as-a-Service function.
In short, it is essential to determine the role of data and its potential value on the market. Metadata provides information about data itself, streamlining data management to improve long-term data monetization efficiency.
In addition, the existing infrastructure may be unable to support the bandwidth, storage, processing, or security requirements. This is another example of why it is important to build for rather than around data monetization requirements.
Finally, building a platform that does not cater to the data buyer could lead to lower sales, or sidestepping by technically literate enterprises with their own analytics and business intelligence capabilities.
Data monetization is highly lucrative, but it requires complex architecting and ongoing management to be successful. As data monetization strategists with decades of experience architecting, configuring, and monitoring complex data environments, Trianz can help find the fastest route to generating new revenue streams.
Our knowledge stretches across all major cloud platforms, data management sources, and business intelligence tools. This enables us to tailor a high-throughput analytics environment that facilitates even the most complex distributed data access. No matter what your BI and analytics need may be, Trianz is here to help you realize the most value from your data monetization initiative.
Want to learn what type of data monetization is right for you?
If you are interested in data monetization but unsure where to start, check out our data monetization services. There you will learn about the different types of data monetization and how each can be used to generate new revenue streams for your business.
Connecting more people to data has become imperative for organizations worldwide. In Top Trends in Data & Analytics for 2022, Gartner stated, “Connections between diverse and distributed data and people create truly impactful insight and innovation. These connections are critical to assisting humans and machines in making quicker, more accurate, trustworthy, and contextualized decisions while considering an increasing number of factors, stakeholders, and data sources.”Explore
Since the dawn of business, users have looked for three main components when it comes to data: Search | Secure| Share. Now let's talk about the evolution of data over the years. It's a story in itself if one pays attention. Back then, applications were created to handle a set of processes/tasks. These processes/tasks, when grouped logically, became a sub-function, a set of sub-functions constituted a function, and a set of functions made up an enterprise. Phase 1 – Data-AwareExplore
Practitioners in the data realm have gone through various acronyms over the years. It all started with "Decision Support Systems" followed by "Data Warehouse", "Data Marts", "Data Lakes", "Data Fabric", and "Data Mesh", amongst storage formats of RDBMS, MPP, Big Data, Blob, Parquet, Iceberg, etc., and data collection, consolidation, and consumption patterns that have evolved with technology.Explore
Enterprises have, over time, invested in a variety of tools, technologies, and methodologies to solve the critical problem of managing enterprise data assets, be it data catalogs, security policies associated with data access, or encryption/decryption of data (in motion and at rest) or identification of PII, PHI, PCI data. As technology has evolved, so have the tools and methodologies to implement the same. However, the issue continues to persist. There are a variety of reasons for the same:Explore
Application Modernization at Speed and Scale Enterprises are pursuing greater application scalability, cost efficiency, and standardization with containerization and virtualization platforms. So, what’s the difference? Containers are a type of virtualization technology that allows users to run multiple operating systems inside a single instance of an OS. They are lightweight and portable, making them ideal for running applications across different platforms.Explore
Container Orchestration or Compute Service? Amazon Web Services (AWS) offers a range of cloud computing services to meet enterprise needs. Included in its service offering is the elastic compute service (ECS) and elastic compute cloud (EC2). Choosing between these two services can be difficult, as one focuses on virtualization while the other manages containerization. In the following article, we will explore the differences between Amazon ECS and EC2 to help you better understand which service is right for your use case.Explore