Not surprisingly, digital transformation is a prerequisite for forward-thinking businesses. The catastrophic disruption of the global pandemic did not slow down the need for systems, processes, and people who will help modern organizations move faster. Data, as always, is top of mind.
With so many trends and tools available, it can be hard to see the forest for the trees. What the pandemic has done, for many, is highlight a need to future-proof their data environment from future disruptions.
With more data flowing into businesses and a greater need to automate processes and maximize impact, these are the data trends that will define 2022.
There are a few major data catalog providers, and their platforms are increasingly touted as a necessary component of a modern tech stack. Data-driven organizations (or those that aspire to be) are looking for solutions that let them discover, manage metadata, and supervise access from a single control panel. What’s unclear, however, is how organizations are managing the flow of external data into their centralized catalog environment.
External data is a clear differentiator. This became clear during the COVID-19 pandemic, where organizations realized overnight that existing models were rendered obsolete by the rapid shift in markets. Forecasting changes or understanding short-term economic outlook became effectively impossible without the addition of new data that augmented existing or outdated models. Organizations turned to external data sources like Google’s Mobility Reports, the World Health Organization’s Situation Reports, and even Twitter or search traffic to gain insight into the rapidly shifting landscape.
While any organization with a decent data team can spin up a few people to connect to this data, managing its flow over time requires a different set of tools than those available in a traditional data catalog. As Bernard Marr noted recently in Forbes, “There are challenges when it comes to working with external data, even if it’s provided at no cost.” Because organizations connecting to the data aren’t the same organizations as the ones providing it, you may become overly reliant on data providers. You may also need to merge datasets from multiple locations, in multiple formats, that are updating at multiple frequencies. Finally, Marr writes, by pulling in data from multiple places, you will need to adhere to different use restrictions and run into compliance issues that are different from those you have with internal data.
It’s clear that external data provides a benefit to organizations who can find a way to connect to it. Making sure that your data catalog supports the ingestion, discovery, and management of this data is going to be top-of-mind for organizations in 2022.
The data monetization market is set for rapid growth in 2022. According to a recent business intelligence report released by Data Bridge Market Research, the growth, size, and CAGR of data monetization is set to grow at a rate of 21.95% from 2022 to 2029.
For the past 5 years, data monetization has been a catchall term for any organization that’s trying to find a way to generate ROI from their data over and above using it to enhance analytical capabilities. A good monetization strategy allows a data science division to continue using data to experiment with new models because the bottom-line investment in the team is balanced by revenue-generating datasets coming out of the same division.
The problem is that data monetization, like AI, is a goal achieved only when a data infrastructure is set up on rails. The reality is that most companies don’t yet have an environment that’s ready to spin data exhaust into monetizable data products. Even if they did, most businesses don’t have the capacity to build a distribution mechanism to get the data into the hands of consumers.
