Recently, one of the world’s largest global shipping companies was seeking to identify new revenue opportunities; specifically, they were interested in monetizing their data by building other, related business intelligence products for different industries. Like many other businesses, they had found themselves sitting on a mountain of actionable data without any processes in place to explore or leverage said data.
Their intentions were now pointed in the right direction, but what they were missing was a data monetization strategy.
A data monetization strategy will aim to extract insight from big data sets and all data types, before packaging that insight ready for sale to third parties. The data itself may relate to customer sentiment, product sales in the digital economy, or prospects for a sales department. This data must be valuable in the sense that it is unique, actionable for the purchasing party, and cleansed or de-duplicated to maximize data quality.
To get there, companies adopting a data monetization strategy need a solid IT foundation with a database or data lake solution, as well as data visualization or business intelligence (BI) tools. This facilitates the storage of data and the analysis of stored data to generate insight. Then, any legal risks, data protection barriers, competitive barriers, data availability problems, and data delivery methods should be carefully considered.
To spin your big data into value, a strategy is imperative to success because it defines the destination. The end state is a combination of physical, technical, and logistical conditions that enable extended conversion of datasets into new revenue streams – even new business models – for enterprises.
Before we explore data monetization strategies, we first need to understand direct and indirect data monetization.
Direct data monetization is where available data sources are collected, stored, and delivered to third parties in return for payment. Simply, the data itself becomes a product that can be sold on the market. The data can be sold raw and untouched, or processed in an analytics tool to generate insights that can then be sold. Examples may include customer or business contact details or market research reports that reveal hidden industry insights.
Indirect data monetization refers to internal use of data. A company could gather data on customers and perform analytics to generate insights around sales and sentiment. Using this data, the company could then optimize its workflows, strategies, IT services, and product-service portfolios to improve efficiency in areas like sales and supply chain management.
These efficiency improvements are a byproduct of data monetization, resulting in a cost reduction or increased revenues for the company to achieve a net positive return on data generation investment.
There are numerous ways to apply a data monetization strategy:
This data monetization strategy is the simplest to implement, and typically operates on a direct business-to-customer (B2C) model. The data itself could be raw and unstructured, aggregated for a high-level overview, or anonymized when the source data contains personally identifiable information (PII). It 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 sale, this is a good way 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 to buyers – think of valuable competitive insights or customer behavior trends. The insight itself 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 the company using data monetization in this context, more work is required to generate insights and visualizations. These must also be aligned with prospective buyer requirements, meaning the wrong insights could generate no revenue at all. Since analysis has already been performed, Insight-as-a-Service provides more value to buyers and thus warrants a higher price than Data-as-a-Service.
At first glance, this approach will look similar to Insight-as-a-Service. Customers can access insights in return for payment. The difference here is the scope of data access and analytics functionality. 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; their product is customer experience insights and competitive advantage. 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 is 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 confidential information being leaked, mandating strict cybersecurity policies with this approach.
Data-Driven Business Models
A data-driven business model will aim 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 their own data.
When a server outage occurs, for instance, system logs and crash dump files are created. This data can be centralized and analyzed to identify repeating network problems and improve IT service desk productivity—indirectly monetizing the data.
Here’s another example: Customer buying habits have changed, resulting in overstocking of certain products. Sales metrics can be analyzed to visualize sales volumes over time and proactively identify trends to improve supply chain efficiency and optimize stock levels—again, indirectly monetizing the data.
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 first-party companies to strategize and optimize their operations in order to reduce costs or increase revenues, 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.
There are many good reasons to monetize data using direct or indirect means:
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 the business’ 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.
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 mid- to long-term.
Remember the global shipping company? Through an approach that included a full market opportunity analysis and proof-of-value exercise – determining the potential monetary value, opportunities, competitive landscape, and barriers to entry – our team analyzed the options to deliver an intelligent data product to the market.
We dug deep into capabilities, segments, channels, and geographies while assessing costs, risks, and potential monetary return of each option. Our team delivered prototypes and roadmaps of selected concepts, along with in-depth primary and secondary research to validate the viability, feasibility, and desirability of each.
The client was pleased. We had identified a wide variety of monetization opportunities including complementary and new business revenue streams across the medical, online purchasing, B2B commerce, and distribution center services industries.
When preparing your business for data monetization, first you should consider the following:
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 whole process requires basic analysis of the dataset to determine the type and format, as well as a holistic determination of its potential monetary value.
Data itself needs data that tells people what the data is about. Since that is a long-winded explanation, we simply refer to this as metadata. Metadata could include titles, descriptions, languages, themes, keywords, licenses, publishers, or other tags. Think of metadata like 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.
There is a subtle but important difference here. Enterprises should build IT infrastructure and software designed specifically for data monetization, rather than adapting existing infrastructure to enable data monetization. This is because of bandwidth, data storage, security, and processing requirements. 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.
The customer is always right. Therefore, speaking to prospective data buyers will help the business understand what, where, how, and why a customer needs data. It is pointless building 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 important 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. Existing infrastructure may be unable to support the bandwidth, storage, processing, or security requirements with data monetization, making it 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 (BI) capabilities.
Data monetization is highly lucrative, though it requires complex architecting and ongoing management to be successful. That is where Trianz comes in.
Trianz is a data monetization strategy service provider with decades of experience architecting, configuring, and monitoring complex data environments. Our experience stretches across Oracle DB, Microsoft SQL, Hadoop databases, and Tableau data visualization software. This enables us to design high-throughput database environments that facilitate distributed data access—perfect for data monetization.
These environments are scalable, pay-as-you-go, and inherit security policies from cloud service providers like Microsoft Azure and AWS. We then integrate with data visualization platforms like Tableau to enable rich insight generation and the creation of Analytics-as-a-Platform services.
Our experts can explore the options with you and devise a plan of action. If you are interested in data monetization but unsure where to start, start by learning about our data monetization services.
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