Data Monetization, sell data

How to Sell Alternative Data

Companies have become increasingly data-focused businesses. Why? Data runs the world. Everything is algorithm based, digitally recorded via footprint, all feeding into big-picture understanding of the world. Every industry uses data differently. For example, marketing firms use the data to understand an audience run targeted advertising while investment firms use the data to identify signals and trends that funnel into a mosaic model and allow analysts to buy or sell stocks. Regardless of industry, the end goal is the same: make more money.

Data monetization, as defined by Wikipedia, refers to “the act of generating measurable economic benefits from available data sources (analytics)…data monetization leverages data generated through business operations, available exogenous data or content, as well as data associated with individual actors such as that collected via electronic devices and sensors participating in the internet of things.” Long story short, it’s the process of creating an additional revenue source for your company simply by using the data you already have in-house and collect through your everyday service and product offerings to the industry.

The vast majority of companies have data that may be valuable to investors – anything from healthcare claims data, geolocation data, rewards/loyalty memberships, point of sale/transaction data, etc. Currently there are two types of entities that hedge funds purchase data from: 1) alternative data firms that sell off-the-shelf type reports/data feeds to dozens of investors and potentially sell-side firms and 2) companies that have nothing to do with selling data and provide non-financial services to clients outside of the investment management community.

Option 1 is the easy route: makes the data broadly distributed (safety in numbers), typically no concern about a lack of compliance framework internally, everyone on the street ingests it BUT the overall value and uniqueness drops significantly. There’s also the issue of the product being ‘packaged’ versus a raw data feed. The packaged product leaves little analysis for the investment team. The signal is already translated for you (and everyone else). When it’s right, cool. When it’s wrong, you drown.

It’s kind of like being with somebody that’s been with everybody.

Option 2 takes more work both on the hedge fund and company side: data is exclusive, almost never distributed to any other firm on the street, potential to lock the data in for an exclusive proof-of-concept is high, and the hedge fund can mold the data offering into what it wants it to be. The deliverable is raw data that is completely uninterpreted. BUT, this data is riskier to consume. The company is usually in early stage data monetization conversations, has no internal legal or compliance framework, little idea of privacy or third party rights ramifications and will require a lot of hand holding to get things ‘right’. This effort is well worth it in the end though if that big data is generating alpha and beating the market.

Small and mid-sized companies can start this second option by: identifying the data they’ve been sitting on; parsing data into fields and aggregating content that may be converted directly into actionable insights; thinking through data vetting, storage, delivery options; performing analytics on the data internally, etc.

In the coming blog posts we will examine the hedge fund audience, potential use cases, ways to productize the data with a go-to-market strategy and do’s/don’ts with regards to legal and compliance.

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