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Key Trends in 2022 for Organizations to Improve Data Literacy

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Intriguing data questions are inherently interdisciplinary. As business leaders look to data-driven decision-making in 2022 and beyond, they must continue to make strides organization-wide in data literacy, or even simply make more individuals comfortable with data. Ultimately, decision-makers need to interpret and use data in a meaningful way to drive the business forward.

In the remote-first world we’ve lived in over the past several years, it’s becoming exceptionally clear that no organization can operate successfully with internal siloes. Leveraging business data is no exception. In a recent episode of The Data Wranglers podcast, Jeffrey Heer, Joe Hellerstein, and I sat down with DJ Patil. Patil is arguably one of the most influential data scientists in the world, including his credentials as the U.S.’s first Chief Data Scientist under former President Barack Obama.

Patil highlighted some of the ways organizations use data. Simple questions data can answer  may be, “Who are our best customers?” But how are you measuring this? Is it who buys the most? Who pays their bill on time? Who costs the least to support? Or is it who is most influential on the product roadmap?

This example is one microcosm of business, just one tiny sample, and yet all of that data is stored in different places, with different people accessing and analyzing the information. Data professionals need to access that diversity of information in cross disciplinary, cross-functional ways to unlock compelling insights.

How do you do that? Democratizing data.

Data is arguably the most powerful tool businesses have at their disposal. Imagine if another company-wide, powerful business tool was only accessible by a small handful of people with a stack of other priorities. By democratizing data, business users and data engineers can work side by side in the same environment to solve problems together utilizing both of their respective expertise.

Democratizing data isn’t just turning a business analyst loose on raw data. It’s making it so that each business skill set, from coding to analyzing, can contribute to uncovering data insights in a meaningful way. It combines low-code and no-code solutions alongside options that allow coders to dive deeper.

Once an organization is unlocking data insights at scale, in a repeatable manner with multiple interdisciplinary contributors, that’s an organization that is data literate.

In an organization leveraging data to this degree, the natural next step is to determine what else to accomplish with it. For so many organizations considering their 2022 goals and beyond, the key is in machine learning and artificial intelligence to automate processes and enable faster operations.

It’s non-negotiable: effective and accurate AI requires pristine data quality. Otherwise, if AI is automating decisions based on bad data, it’s just making bad decisions faster.

This leads to the second key trend of 2022: data quality.

Heard that trend before? Data preparation still comprises the bulk of data work for organizations. Clean, well-prepared data is the cost of entry to a world of effective data use. Downstream processes, including AI and analytics, are worthless if data quality is bad.

In 2022, businesses looking to utilize data better should consider automated ways to fix, monitor, and remediate data quality. It is no longer a situation where developing simple static rules can handle data quality, but rather organizations need the data sets to suggest their own data quality checks. This enables automating those checks and monitoring adherence to those checks over time as data pipelines grow.

Keeping “human in the loop” is important because context is important. Still, data quality cannot be a situation where the human must define and monitor all things upfront. Datasets are more diverse than ever before, and there are more of these data sets than ever before, so scalable use requires some degree of intelligence and automation.

It’s an interesting flip of the script: rather than AI/ML coming from the data, apply AI/ML to the data to improve and monitor its data quality.

With strong data quality in a data literate organization, the sky’s the limit for what businesses can achieve in 2022.

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