"We’re not a SaaS company; we're a data company."
"The data we generate from Phase 1 will be a goldmine. Phase 2 is when we launch the data business."
These are just a few of the many times we've heard the term 'data business' used as the ideal future-state solution to everything. Whether it's the last slide of a fundraising pitch, the rallying cry at an all-hands, or the 'future' section of a product roadmap, it's clear that operators and investors alike believe that data is the future. But with all of this overwhelming intent, where are all the data businesses?
That is the question my long-time friend and now cofounder Ameya Pathare and I set out to explore. We spent the last six months speaking with over 50 data providers and consumers to understand the challenges associated with data businesses, and here's what we found:
This stat blew my mind - today's global data economy is $250B and is expected to cross $400B by 2030. Today, this massive volume of data is transacted primarily through data brokers, companies that aggregate and sell data. These brokers solve the consumer need for aggregated data and domain insights but, as a result, cut off the actual data providers from the end consumers of data, making data lineage opaque.
The newer and alternative distribution channels are the data marketplaces - AWS, Snowflake, and Google all launched their version of a data marketplace within the last three years. While they make it easy for providers and consumers to interact directly, the same ease makes them noisy and challenging to navigate. Additionally, to interact on these marketplaces, all parties must run on the marketplace's tech stack, creating engineering overhead for cross-party data sharing.
While each channel has its advantages, to succeed, data providers must rely on multiple distribution channels, creating a high barrier to proving value.
The unsung hero in the data economy is unequivocally the flat CSV file, especially the ones that are copied, sent as email attachments, and uploaded to SFTP sites. This surprised us, given the 'modern data stack' world of 2022. Still, it quickly began to make sense when we realized that third-party data consumers are typically business stakeholders, not engineers. Whether it's logistics data, lead data, or industry trend data, these business analysts want to be able to open these files in Excel or another data manipulation tool to tell a story or prove a point. Each analysis is unique to each consumer; therefore, the timing and frequency of consumption also varies for each consumer. We also noted that there is a desire to move towards APIs and programmatic access and the lines between data engineer and data analyst are blurring.
A viable data business needs to support multiple formats and frequencies of data delivery on the consumers' terms. This makes scaling a challenge for data providers.
For a data business, data is the product. So age-old challenges around data quality come front and center here. Unfortunately, today, there is no simple (read: non-technical) way to enforce data quality thresholds, especially as the underlying data continues to refresh. The teams we spoke to were resorting to manually scanning and checking CSV files and then personally distributing them to consumers, voiding any rudimentary data pipeline or automation that may already exist. The terms to govern these data requirements in the industry are 'Data SLAs' or 'Data Contracts' (more on why this is important in a future article).
The same unfortunate state of affairs exists when it comes to data privacy and regulatory compliance, an increasingly topical field in data sharing. Given the severity of consequences on the downside, experts end up scrubbing and certifying data manually.
The manual nature of data quality assurance and compliance screening places a burden on already resource-strapped data and IT teams.
Our interviews also revealed a few common attributes (a practitioner wishlist) that any technology solution should possess to enable companies to rapidly launch and scale data businesses.
In summary, every business has the potential and capacity to be a data business if the above challenges can be addressed. It is an exciting prospect that technology can now help bring down these barriers once and for all, allowing every business to finally become a data business. To learn more about how technology can help, book a call with us and we'll give you a walkthrough!
Stay tuned for future articles and case studies to learn how you can monetize your data and turn it into a revenue channel.
