Making BI accessible to everyone

Cloud services that make analytics far more user-friendly are democratizing data insights to empower employees at all organizational levels.

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While business intelligence (BI) was once reserved for the likes of highly skilled data scientists and IT professionals, advances in cloud and machine learning technologies are putting powerful BI capabilities into the hands of employees across the organization. Specifically, cloud analytics are democratizing data insights to improve decision-making and productivity enterprise-wide.

Historically, companies have invested a lot of time aggregating and formatting data into dashboards and reports. They then delivered the data in these formats to analysts who understood the ins and outs of accessing and interpreting it.

Today, more user-friendly, cloud-based BI services deliver data insights to the broader workforce, with no prior BI or coding expertise required. These services use advances such as natural language queries, which can return answers in seconds, and the ability to embed analytics into familiar applications to make insights more accessible to the mainstream employee.

In addition to reducing BI skills requirements, serverless cloud models lower the cost of getting BI to the masses. Serverless computing allows organizations to pay for the actual volume of resources consumed by an analytics application, rather than having to pre-purchase units of capacity. Serverless also means that organizations can now freely scale their analytics with hundreds of thousands of users – internal or external – without needing to deploy massive infrastructure and have large, dedicated teams for management, and simply relying on cloud technologies to do the heavy lifting.

With the advent of friendlier, affordable, and scalable BI offerings, organizations are able to ramp up their ability to use data-driven insights to effect positive change.

In play at the NFL

For example, the National Football League (NFL) uses Amazon QuickSight—a cloud-based analytics service powered by machine learning (ML)—to gain greater understanding of the likely outcomes of certain plays. The NFL also uses the platform to allow fans to engage with data. The league can now provide data dashboards—which once took hours or days to build—to its ballclubs, broadcasters, editors, and fantasy football writers at in minutes.

Those dashboards have allowed the NFL to avoid writing a lot of code every time it wants to

update or share information, while also ensuring that these insights are embedded in the pages these stakeholders would normally access. It uses the insights derived to help coaches create better game plans and improve player safety. Patterns of who’s on the field and what plays are executing when there are certain outcomes, identified through ML, help coaches and others better understand the probable results of certain plays and even how players are more likely to get injured, so they can design rules to mitigate risk.

Workforce improvements in Korea

A different type of business, which matches freelance workers to employers, also makes use of Amazon QuickSight. Kmong in South Korea has seen its data volumes grow 20-fold in recent years. The company had initially created a dedicated team comprising a data analyst, server engineer, and deep-learning expert to handle data analysis, including business planning, promotions, and development of AI-based services such as personalized recommendations. When that workload proved to be a challenge for the small team, Kmong turned to cloud services.

Through the use of Amazon QuickSight, various teams within the organization can now make independent, data-oriented decisions themselves, according to Park Jae-young, Kmong’s chief technology officer. "We gained deeper customer insight that led to a 30% increase in purchasing conversion and a 40% decrease in churn," he says.

In addition, QuickSight allowed the data team to focus on strengthening its AI-based custom recommendations. The results have been strong: The purchase conversion rate for users who view personalized content recommendations is 400% higher than those who view standard content, Park says.  

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