The Need for Better External Data Discovery in 2021

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Organizations increasingly turned to data science and data science platforms to gain some visibility and better respond to the rapidly changing world. 2020 was, in some ways, a pivotal year for data science. As the field grows, however, the things that drive it forward are going to change. Explorium discusses the need for more enhanced data discovery in 2021.

data discovery

No one was expecting 2020 to end up the way it did. Even so, 2020 was not all bad. Even with the difficult times, technology offered people and organizations the ability to adapt and face their new circumstances. From new paradigms on remote work to improvements in how businesses understand their customers, adaptation was the name of the game.

The pandemic was bad news for almost every industry. Retail, food services, hospitality, and most other customer-facing businesses were hit hard by lockdowns, mask mandates, reductions in foot traffic, and a rocky economic landscape. This new status quo forced companies to adapt and find ways to navigate these murky waters effectively.

Organizations increasingly turned to data science and data science platforms to gain some visibility and better respond to the rapidly changing world. 2020 was, in some ways, a pivotal year for data science. As the field grows, however, the things that drive it forward are going to change.

2020 broke everyone's models, so how can organizations adapt?
One of the biggest takeaways from 2020 in data science was the validation of what the industry has been seeing for some time. Fine-tuning machine learning (ML) models will only get businesses so far in the absence of quality data. On the one hand, there are powerful ML tools for organizations regardless of their data science expertise. On the other, as more competition took place in terms of models, there was little room for uplift and improvement in results. Tweaking hyperparameters is a game of diminishing returns — after a while, the gains become marginal.

When companies finally grasped the magnitude of the pandemic, there was a moment of panic. However, no one truly understood until a few months later that the historic data being fed to ML models was suddenly a lot less informative and valuable. Those finely-tuned prediction engines suddenly couldn’t make the right calls, leaving organizations scrambling for answers.

So, where to turn when models that have already been tweaked to their maximum potential need to give organizations the answers they expect? To the other side of the equation — data. What happens if the last three months of data collected looks nothing like the 16 months before it? Early on, it became apparent that to find solid footing in the new landscape, organizations needed more than just their own data, and consequently, there was a rush to find the right data to feed models.

This is the new reality organizations live in. More than ever, they need to understand this new shifting landscape, and that their models are stumbling in the dark without a guiding light.

2021 will be the year of data discovery
The need for new data means that it has to be accessible quickly and affordably. Data by itself is great, but really, it’s not that there’s significantly more data now than there was a year ago (or even three years ago). What’s going to be the major difference in 2021 is the emergence of data discovery tools to help organizations find the data they need quickly and without expending weeks’ worth of efforts and resources.

Data discovery platforms provide organizations with a valuable tool for data science that can cut down on a lot of the legwork. Instead of weeks spent looking for a single valuable dataset, data discovery tools give access to thousands in a matter of minutes and ensure that they’re all relevant.

Even broadly, data discovery will play a key role in expanding the value and ROI of ML in general. Even without the pandemic to account for, ML models are most effective when they have more data to learn from (or, rather, more quality data), and until now, the inability to find it effectively was a major roadblock to adoption and reaching ML’s full potential. Automated data discovery is poised to radically change the way data is evaluated, models, and how organizations think of the answers to the predictive questions asked.

The ability to connect the science and data sides of data science will allow organizations and data scientists to focus more time on building new models, innovating what they have, and understanding their predictive problems. They’ll also be able to do this with a much better foundation, using external data as a springboard for new insights, greater perspective, and scalable implementations.

Picking up the pieces and reimagining data science
2020 was a rough year, no doubt about it. 2021 will be better, and data science will play a large role in helping multiple industries adapt to the new normal. However, to do this, the field will have to embrace the fact that hyper-parameter tuning is no longer the only factor in building better models.

To take the field to the next level, there is a need for faster pipelines to the external data organizations and data science practitioners. Fortunately, 2020 showed us that data discovery is a scalable, viable solution, and one that is already being implemented in the field to resounding success.

Explorium offers a first of its kind data science platform powered by augmented data discovery and feature engineering. By automatically connecting to thousands of external data sources and leveraging machine learning to distill the most impactful signals, the Explorium platform empowers data scientists and business leaders to drive decision-making by eliminating the barrier to acquire the right data and enabling superior predictive power.

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Shelby Blitz
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