The Gap

Data is the new gold or oil. Data can be stored in a reservoir and has the potential to generate value. Such are the analogies on data and its uses. The value of data is real, but the realization is hard. Oftentimes, the attempts to monetize data end up in spending more money with little tangible results. In fact, the answer might be there in those analogies, if we are willing to extend those a bit more.

Data is produced through transactions - buying and selling goods, performing services, or documenting facts. Transactions store data for reasons such as record-keeping, business reporting or legal compliance. Those reasons do not necessarily optimize data for monetization, but for efficient storage, access, and presentation. For selling data it needs to be processed, packaged, marketed, and distributed as a product. This is usually the gap in many data monetization initiatives.

The Product

Once we recognize that data needs to be packaged as a sellable product, we can apply a lot of best practices from manufacturing, distribution, and retail domains.

To start with, the data as a product should have specific utility. There must be a segment in the market, a certain type of customers who need specific data to perform or improve their business operations. The data we own makes a difference (or gets the job done) in their business decisions and operations. Identifying such market segments and customers for the data we own would be the first step. This helps to plan, design, manufacture, process, price and distribute data to the right markets.

Next step is processing and packaging. As discussed earlier, data produced by transactional systems and designed and stored for the immediate needs. This data needs to be relevant to consumers in its form, shape, size, and quality. The data needs to be anonymized, masked, or aggregated. The design of the data product might require creating industry metrics, summary layers, or causal relationships for standardization of offering and ease of consumption.

It is also possible to package the same data as multiple products depending on use-cases and market needs. For example, if the data collected by a compliance system is representative of the domain, industry, market and customer demands, there is a potential to sell data to the leading businesses, new entrants, and service providers in the domain. Established players in the industry might be interested in growth aspects, while new entrants would need demographic profiles. Service providers to both these might be looking at aggregated trends and metrics. If all of these are compelling and sustaining demands, we need to create multiple products. The granularity and details required in each product would be different.

After the product is created and packaged, we need define the distribution channels. The data products could be distributed as downloadable files, or through APIs, or through messaging channels. It could be listed in a marketplace, combined with complementary products, or used as a component in a larger ecosystem or products. The license could be one-time download, or for limited time-period, or plan-based subscriptions. This is similar to multi-channel and omni-channel distribution of products in retail and ecommerce businesses.

Once the product is developed, launched, and made available for distribution, it needs to be priced to the market. The pricing depends on the utility of the data product, market demand, competition, customer segments, buying volumes, special processing, and so on. The price of the data product may be tiered, seasonal, or dynamic.

A good data monetization platform should also track and meter the usage and consumption of data to plan, price and invoice the customers.

The Lifecycle

Products and packages improve or evolve over time. The transactional systems producing data also evolve and expire. The consumers and market demands change too. These forces an ongoing evolution of the offering of data product. Now, this can be compared to a typical product lifecycle management process.

On an ongoing basis, one needs to evaluate the market trends and demands, reconsider product design, improve processing techniques, repackage product offering, reformat data definitions, introduce new versions of services, run promotions and campaigns, provide customer support, and retire expired offerings.

Data Monetization is a deliberately planned and invested activity. It is a supply chain and digital commerce business with products made of data - planning, mining, producing, gathering, storing, processing, packaging, distributing, marketing, and selling data. It is a digitally-native, technology-driven, profit-oriented business, fulfilling the real customer-demands in a competitive market for data and business intelligence.