Data as a Service
Bridging the gap between your predictions and reality.
Problem
You don’t know how to make better decisions.
Solution
Data as a Service (DaaS) bridges the gap between your predictions and reality. The lower your spread, the better your decisions.
Data is a means to an end, if consumed as a product. However, insights are closer to the "job to be done", if consumed as a service. In this article I'll cover aspects of both.
In competitive environments, the more data is shared, the less valuable it becomes. For example, shared lead lists for marketers have low close rates, publicly shared transactions are already priced in to the value of stocks. However, in cooperative environments, like public health, data sharing is extremely valuable. Consider the case of covid at the moment. Keep this distinction in mind for the rest of the article.
Motivation
So what is powering DaaS? Very few companies had the ability to make use of raw data in the past. An order of magnitude more companies are buying data today than did 5 years ago. That's because a good engineer with a tool like Snowflake can be as productive as a great engineer 5 years ago.
We are seeing a commoditisation of data-warehousing with Snowflake, data lakes with AWS S3, and even Lakehouses with Databricks. The end user has ease of data ingestion with Fivetran, aggregation and monitoring with dbt.
Fivetran offers fully managed data integration from source to destination
These solutions are driving a rise to the market of alternative data, where companies are not only interested in internal (private) data sources but also external (public) data sources.
One example is hedge funds. Less than 3 years ago, just ten hedge funds were buying alternative data. Today there are 500-700 funds currently making investments to ingest large amounts of data. I’ll cover the market of alternative data in a separate article.
Data as a Product (DaaP) vs Data as a Service (DaaS)
It is the difference between providing "data" and providing "insights"
There are two broad mandates (I'm being overly simplistic on purpose) that data teams are formed with:
Provide data to a company
Provide insights to a company
In a DaaS model, the data team partners with stakeholder groups to tackle specific problems using data. Team members have more functional experience, and are responsible for providing insights as opposed to raw data.
Let's go through an example.
The product team wants to improve and redesign the customer onboarding process.
The data team partners with the product team to identify what drives long term customer retention and value, gaps in the current onboarding process; using the data that the data team is responsible for.
In a DaaP model, they would provide the product team with the data they could use themselves to answer their questions. It's the difference between a service-oriented partnership and a product-oriented SLA.
Challenge
There are hundreds of companies that sell software and tools (traditional SaaS) to data scientists and machine learning teams. But there aren't many that specialise in selling data to teams that require it, in whatever shape or form (DaaP or DaaS).
Being a data-only business is less exciting because data is a supporting role.
Data companies support the true innovators.
It's like selling high quality butter to pastry chefs. The end consumer of the pastry may not even know there is butter in it, but the chef knows how important the ingredient was. Of course, the most important player in making a tasty pastry is the chef. A data company is just one of the important ingredients that goes into its creation.
Prediction
The last 15 years have been about how companies get insights from their own data -- Tableau, PowerBI, Snowflake, Palantir have played into that trend.
Companies that are far along the curve of getting insights from their own data need external data if they want to continue getting value from their data teams. Because even the largest companies only knows about 0.1% of the world from their internal data alone.
Nonetheless, there are still very few data buyers today. Most companies want applications (answers), not data (collection of facts). The only reason to start a data business today is if you believe the number of data buyers will go up an order of magnitude in the next five to ten years.
It's a great time to build a pure-play DaaS business if you’re bullish on this trend.
Selling intelligent data to companies is starting to be a big business.
If you’ve reached here you might be interested in a few players currently active in the DaaS Market:
Very useful