It’s like eBay, but for your company’s data sets.
Leading cloud infrastructure and database providers have created data marketplaces that enable companies not normally in the information business to have a side hustle hawking their internally-generated data to other companies.
Data marketplaces are a great opportunity for any business to maximize the value of their fastest-growing asset. However, there are some emerging best practices that companies need to follow to sell successfully. In this blog, I will:
For anyone in the data industry, news about emerging data marketplaces have been coming fast and furious in the last few years, with a definite acceleration taking place during COVID-19.
They include:
Source: Nokia.com
Also launching in 2021 were two data vendors — one old, and one new — releasing data marketplace-like platforms:
Data marketplaces are hardly a new thing. Audience data marketplaces and data exchanges selling consumer profiles, and location and contact info to e-commerce companies and Internet advertisers have been around for decades. Audience data was a $22 billion market in the U.S. alone last year.
Traditionally, the sellers in audience data marketplaces have been market research and adtech companies focused on collecting, processing, and delivering ready-for-campaign datasets. And most of these marketplaces — with names such as Adform, Adobe Audience Manager, Lotame, etc. — are tied directly to Demand Side Platform (DSP) or Data Management Platform (DMP) software that is sold to the same e-tailers and marketers buying the data.
So what is different about this new crop of data marketplaces? Four things:
The Buyers. Audience data marketplaces are the stomping grounds of retailers and advertisers looking for demographic and contact info to better target consumers. By contrast, these new data marketplaces cater both to those existing buyers as well as a new breed of users coming from a wide variety of organizations:
The Sellers. With the mainstreaming of technologies such as big data, data science, machine learning and AI, today’s companies are not only proficient at using data to drive their businesses, but also boast many data engineering/ops experts capable of curating, preparing and sharing data. They create comprehensive data catalogs and oversee metadata taxonomy systems that enable efficient self-service discovery of data by any employee. Their goal is to dismantle dreaded data silos, democratize access to data, and maximize the value of data through maximum reuse. Those are the skills and mindset that companies need if they want to take the next step and enter an external data marketplace.
The Technology. In the era of data centers, server clusters and proprietary databases, sharing data was clumsy and slow. According to AWS Data Exchange’s general manager, Stephen Orban, even recently many companies were “still literally shipping hard drives to each other” or using 80s-era FTP sites. Selling data simply wasn’t worth the trouble for companies that weren’t 100 percent dedicated to it. In today’s era of cloud data lakes, managed, serverless databases, and quick-build microservices, it is feasible for any company to start selling data — especially when the marketplaces are integrated with their existing database or cloud platforms. These data marketplaces also make it much easier for buyers to ingest, transform, and stay synced with sellers’ data sources. No need for difficult-to-build ETL pipelines. The marketplaces also handle all of the small-but-vital details to make a transaction go through, including tracking (whether usage-based, query-based, compute time, or time-based), invoicing, billing and payments.
The Economic Opportunity. As noted above, audience data is already a massive market worth tens of billions of dollars every year. But as data marketplaces bring mainstream enterprises on board en masse, they will soon dwarf the audience data market, experts say. Accenture, for instance, estimates that by 2030, more than one million organizations worldwide will be selling data assets to the tune of $3.6 trillion.
For AWS, Snowflake, Oracle and SAP, running data marketplaces can earn them a tidy sum for every dataset rented or sold. More importantly, data marketplaces add value in the same way that app stores add value to smartphones: driving greater usage of their cloud-based platforms which translates directly into more revenue.
Today, it’s not just the high-profile data-driven companies like the LinkedIns or Netflixes of the world that have data worthy of selling. Every company — from large enterprises to small businesses to startups — generates large amounts of data more valuable than they may suspect.
Your company’s data doesn’t have to fit into one of the big buckets — the $12 billion market for location data, or sectors such as retail, financial services, and healthcare — to be of value, either. Any large-scale set of customer behavior or transactional data, including B2B customers, can be useful. It just needs to be properly formatted, cleansed, anonymized and transformed so that it’s ready for queries.
Whereas companies in the past used to buy pre-packaged market research to insert into static reports, nowadays companies have the skills to analyze external datasets to generate their own original insights or build dashboards for ad hoc data exploration.
For instance, during the early days of COVID-19, economists were measuring the severity of the virus by analyzing daily restaurant reservation trends, and tracking compliance with governmental lockdowns by crunching mobile phone location data . They were also tracking employment levels by analyzing employee hours from a business time scheduling app.
As SAP put it: “Data is the new currency of the digital economy, and data providers are at the heart of it.”
While data marketplaces make it incredibly easy to get started selling data, that doesn’t mean your company will be effective.
Below are the four most common issues that newbie data sellers need to solve if they want to sell effectively. While most are familiar to any data engineer experienced with creating an internal data catalog or self-service platform, that doesn’t mean they aren’t difficult and time-consuming to solve. Which is where a multi-dimensional data observability platform can come to the rescue.
Acceldata’s multidimensional data observability platform can help companies like yours more effectively monetize your datasets through data marketplaces.
In particular, our Acceldata Torch module provides a full suite of capabilities that automatically ensure ongoing data quality and reliability across the entire data pipeline. Torch also automates the validation of your data reliability by classifying, cataloging, and managing business rules for data at rest and in motion.
While improving data quality and reliability is nobody’s idea of fun, it’s a task that can be greatly eased and automated with Acceldata Torch. It can turn your data engineers into 10x superstars. And it helps your team more easily address the four pitfalls of newcomers to data marketplaces.
Photo by CHUTTERSNAP on Unsplash