The New Data Economy: What Marketers Need to Know for 2022

Among the trends to look out for is the transformation of the data scientist role

Did you know brand recall increases when viewers see an ad on TV AND streaming? Download "A Practical Playbook for Multiscreen TV" to learn more. Sponsored by EffecTV.

While focus on data and advancements in analytics has intensified over the past several years, marketers face unique challenges in the Covid era.

Consumer behavior has shifted tremendously, and businesses have responded by transforming offline experiences into engaging digital environments. This growing online world has created a digital consumer footprint in the form of data streams and identity systems that were previously unavailable.

By now, you’ve probably made investments in digital tools, but they likely haven’t fully paid off in driving business outcomes. A 2021 McKinsey survey found that only 30% of CEOs believe their analytics strategy aligns with their business strategy, clear evidence that simply having data within your organization isn’t sufficient.

So it’s fitting that 2020 is the decade of data—and 2022 will be a breakout year for how marketers use all this new information.

A new data economy

Google shook up the entire advertising industry when it put an expiration date on third-party cookies in its popular Chrome browser, though marketers now have until late 2023 to adapt.

Coupled with Apple’s iOS privacy updates, these changes add material complexities to the collection and aggregation of third-party data. Yet, at the same time, they will also act as a catalyst for material disruption.

With the basic observation that one business’s first-party data is another’s third-party data, we’ll see an explosion of data-sharing platforms that enable powerful joins and aggregates to happen in the cloud. This will include new upstarts focusing on clean rooms to more established players like Snowflake making huge bets around data sharing.

Identity solutions will also evolve to represent customers in a cookieless world. We expect standards like Unified ID 2.0 to get strong adoption as a way of enabling brands to pool data and understand cross-business customer behaviors.

All these changes have the potential to create a compelling multi-dimensional view of customer behavior. It’s also guaranteed to challenge massive data moats the FANGs (Facebook, Amazon, Netflix and Google) have created over the past decade-plus.

Data-enabled SaaS

Centralization also became a focus with the proliferation of data streams last year. Companies as small as a few dozen employees now have the resources to build fully fledged warehouses to collect and aggregate data. Yet, for the most part, the world of business-facing software as a service (SaaS) applications has evolved independently from the world of data.

So-called “reverse ETL” applications appeared on the market last year led by HighTouch and Census. These tools allow users to integrate data from warehouses into SaaS applications with only a few lines of code. This will mark an important step in bridging the gap between analytical strategies powered by data warehouses and business strategies enabled by various SaaS applications.

At the same time, however, reverse ETL applications have exposed a fundamental challenge: SaaS applications are limited by their data capabilities. Let’s assume you have 100 fields for each customer in the data warehouse. Yet your SaaS application supports only a dozen customer fields—any integration path will result in a considerable loss of accuracy.

Looking beyond 2022 and into 2023, the emerging SaaS leaders will be characterized by their data capabilities.

Transformation of the data scientist

Given these trends, a new form of a data scientist will emerge. Data proliferation and emerging capabilities around sharing will drive demand for the role, and data-enabled SaaS applications will enable the data scientist to work more closely to business outcomes.

A new set of data science tools has also emerged, providing powerful access to high-powered machine learning tools and workflows led by applications such as DataRobot and Dataiku. Together, these trends will enable less specialized data ninjas (with potentially higher business acumen) to rethink the shape of the data scientist role.

Completing the flywheel

The overarching theme of 2022’s looming trends is how data integrates into core business processes.

The technical competency around merely “having the data” will no longer be sufficient as a strategy, and organizational success will start with data-savvy leaders deployed across all functions of the business. Ultimately, it’s about a deep embedding of data into the people, processes and systems that power a business—from optimizing inventory allocations to enhancing the digital customer experience.