Data monetization is the process of extracting commercial value from your internal data assets. Chief Data Officer at Gartner, Alan D. Duncan, recently reported that data monetization is the fastest growing benefit and #1 differentiator of data & analytics. Long story short, data monetization is important for any business in the age of analytics and AI. And to do it correctly, you need a strategy.
And we predict that every company will. To keep up with other businesses becoming commercial data-as-a-service companies, you need a monetization strategy which will withstand the oncoming competition characteristic of the Digital Age.
…as it’s described by Douglas Laney in his book Infonomics (2017). You need a strategy to wrangle such an asset into a commercially viable - and valuable - business.
In a survey conducted by Harvard Business Review, 91% respondents agreed that ‘democratizing access to data and analytics is important to the success of their organizations’. And to adopt an even larger view, data sharing is key to driving positive social change. Data is key to answering the toughest problems facing economic growth, business innovation, healthcare, global trade, sustainable development, equal distribution of resources, and international relations. Data monetization means information previously locked within closed organizations is made available to be put to positive use.
The most successful data companies follow these 5 steps to developing a successful, scalable data monetization strategy: parsing, products, pricing, platform, and partners.
“Effective data literacy, or data fluency, goes both ways.” - Valerie Logan, CEO and Founder of The Data Lodge
The phrase ‘data monetization’ merges the vocabulary of two distinct linguistic groups: the language of analytics, and the language of business. Because ‘data monetization’ is joint endeavour between business and analytics folk. As such, successful data monetization demands fluency in both analytics-speak and business-speak. Business teams need to understand the information assets they’re monetizating, and analytics teams need to understand the commercial value of the data being sold, as well as the wider external data economy.
That’s why data parsing is fundamental to successful data monetization. Parsing data turns data from an unreadable, unstructured format into a language humans can understand. And by ‘humans’, we don’t just mean analysts and CDOs, but also the sales reps, account executives, pricing strategists and marketers selling the data.
If the data is parsed into a universally understandable format, then everyone involved in the commercial chain of data monetization can appreciate its substance and its value. Then come the next steps: creating meaningful information products, and pricing these products optimally.
“Unless data and analytics leaders create information products by design, the true value of their information assets cannot be realized.” - Gartner
Data is like any product. It is produced as the fruit of intelligent labor, and, like a product, exists to serve a purpose. That purpose is to answer questions by providing information. Software developments like Narrative’s Data Shops testify to this trend towards treating data as a product, ready-to-buy from a shop.
However, many data sellers have neglected to adopt a product-driven approach to selling their information assets. Good products should be standardized so they’re easy for the seller to mass produce, and so they’re understandable and appealing to a wide customer group. As Auren Hoffman notes, it’s ‘really helpful if data producers and data consumers agree on a common standard.’
For the optimum data monetization strategy, you need to start organizing your data assets into standard, digestible, and saleable products. You can do this internally to build a catalog of data products. Then using this catalog, you can create product listings across the data monetization platforms you’re using (which we’ll look at in Part 4).
“Appraising your data before creditors compel you to do so can engender new and innovative data-driven value streams that keep the vultures from circling at all.” - Douglas Laney for Forbes
The price of external data varies massively according to the kind of data you’re offering. You’re unlikely to find a weather data product for the same price as a location data product. For this reason, it’s crucial you appraise your internal data before offering it as external, readily purchasable products.
Once you’ve done your internal appraisal, you can conduct in-depth market research to determine the optimum price point for your data products. Your research should take into account the going rate for the category and volume of information you’re selling, as well as competitor data sellers.
“You have to leverage channels aggressively to speed up your sales and sell more data” - Thani Shamsi for World of DaaS
Data monetization is just one aspect of the wider data commerce revolution. There’s been a proliferation of data commerce platforms which are powering this revolution. Much like how e-commerce platforms like Spotify empower merchants to sell goods online, data commerce platforms are channels designed to make your data monetization strategy easier and more effective.
There are various advantages to making platforms like Data Commerce Cloud™ (DCC) part of your data monetization strategy. DCC enables you to sell your products across sales channels with one central account. This reduces the overhead spent managing a data business as you being scaling your data monetization efforts.
“The more connected a dataset is to other data elements, the more valuable it is.” - Auren Hoffman, CEO of Safegraph
The word ‘data’ is so often followed by terms connoting partnership: sharing, collaboration, exchange. The data industry remains, at its core, a people-powered ecosystem. Strategic partnerships are the final, crucial ingredient to successfully monetizing data.
Data partnerships can take several forms. First, there’s the partnerships you build with other providers. By compiling your data assets and connecting them to each other, both parties’ data becomes more valuable. This is because the resultant, combined data product is richer, cleaner (because you can cross-reference between datasets and remove anomalies), and able to answer more questions. So it’s worth forging alliances with fellow data-as-a-service companies. It’s mutually beneficial for your respective data businesses: everyone’s products are worth more.
A second kind of partnership is needed to build a scaleable data monetization strategy, this time with the data commerce platforms we looked into in Part 4. Most platforms offer tiered plans, beginning at self-serve data seller accounts. However, the real value of data commerce platforms come by joining a premium plan and truly partnering with them. Doing so unlocks opportunities including active PR and promotion, access to the highest-budget buyers, and direct lead referrals. As The Data Appeal Company, a leading location data provider, puts it: “We love our partnership with Data Commerce Cloud™ because it gives us access to prospects around the globe across a variety of industries”.
Data monetization is a combined effort, requiring partnerships - just like any other business. To find out how Data Commerce Cloud™ can support your data monetization strategy, get in touch.
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