How Embedded BI Is Changing The Game For Advanced Analytics

How Embedded BI Is Changing The Game For Advanced Analytics

The average person might not think much about embedded BI — or even know what it is. But perhaps without even realizing it, they’ve been taking advantage of embedded analytics, or embedded business intelligence, throughout the pandemic. And it’s a trend that will only increase as it becomes part of our next normal.

Public-facing dashboards, such as the COVID-19 dashboard from Johns Hopkins University, provide valuable real-time data to the public in an easily accessible format. Imagine if this data was only available in a series of spreadsheets. For most people, that data would be meaningless unless they had the time and know-how to make sense of the numbers. Even then, the data would only be relevant for a particular point in time.

The rise of public-facing dashboards

Interactive dashboards, on the other hand, provide data visualizations on real-time data. Even better, many of these dashboards are now self-service, so everyday users — including the general public — can access advanced analytics, even if they don’t have a master’s degree in data science.

Dundas Data, for example, created a COVID-19 real-time interactive dashboard to help Canadians stay up-to-date on the status of COVID-19, allowing them to explore critical cases and resources by geography. They can easily see hospital bed capacity in their region based on the critical care rate, as well as the ventilator count by province.

They can also perform social distancing ‘what-if’ analysis and examine COVID-19’s impact on business and employment. For example, the dashboard allows them to see the effects of social distancing (based on WHO data that one person can infect 2.5 people within five days) by simply moving their cursor along a scale from one to 100 per cent. This makes it simple to visualize the concept of exponential growth and how social distancing can have a direct impact on critical case counts.

The evolution of analytics during the pandemic

Many organizations already understood the value of analytics prior to the pandemic, but the past year has shone a light on its criticality. We now have remote workforces, virtual business processes and a lot of economic uncertainty. Historical data from early 2020 doesn’t have much relevance in 2021, thanks to constantly evolving economic and behavioural shifts during the pandemic.

“Analytics supports numerous urgent tasks facing businesses today: forecasting demand, identifying potential supply-chain disruptions, targeting support services to at-risk workers, and determining the effectiveness of crisis intervention strategies, to name a few,” said Nicolaus Henke, Ankur Puri and Tamin Saleh in an article for McKinsey & Company.

As the authors note, what’s surprising is how fast these solutions were deployed out of sheer necessity — not over the course of months or years, but often in a matter of weeks. The pandemic has also resulted in another critical evolution: the “shift from top-down decision making to data-driven decision making by those on the front line.”

Making analytics available on the front lines

The pandemic has created continuous uncertainty, rendering some historical data useless. Not only do we need real-time data for decision making, but that data needs to be available to a broader range of users, including those on the front lines — whether a hospital worker, store manager or even a member of the general public. A COVID-19 interactive dashboard, for example, can help Canadians cut through misinformation and noise to find relevant, real-time data that directly impacts their health and wellbeing.

“While understanding of the potential value of data to strategic decision-making has never been greater, the extreme levels of uncertainty mean there is also a greater understanding that historical data models and assumptions likely cannot be applied to analyzing whatever the post-COVID-19 ‘new normal’ will be,” says Matt Aslett, research director at 451 Research, a part of S&P Global Market Intelligence, in an article.

“The ability of business leaders to quickly use data from operational applications to make strategic decisions and deliver on strategic outcomes will rapidly be seen not just as a potential competitive differentiator,” he continues, “but also as a fundamental requirement and strategic imperative.”

The move to embedded analytics

Traditional tools aren’t going to work in this new environment, which is why we’re seeing a shift to what 451 Research calls proactive intelligence. This refers to the “new breed of intelligent enterprise … applications that have embedded analytics functionality and are automated by artificial intelligence and machine learning,” says Aslett.

Indeed, the use of intelligent applications with embedded analytics — such as automated visualizations or natural language descriptions — will be critical for decision-making going forward, according to 451 Research.

But to be useful, these insights need to be easily accessible to users, whether an employee or customer, through self-service options and automation (say, through an app alert). That means they don’t have to go looking for insights, nor do they need a team of data scientists to help them make sense of the data. As the term suggests, embedded analytics means it’s ‘embedded’ directly in their workflows and processes.

Ultimately, this can accelerate time to insight and action — whether adapting to changes in supply or demand, managing inventory, restructuring supply chains, discovering new areas of growth, differentiating a service or arming the public with essential healthcare information.

Choosing the right embedded analytics platform

When choosing an embedded analytics platform, you’ll want one that’s easy to set up and easy to use. But you also need the right data to generate relevant insights, so ensure data management and governance policies are in place (without this, you could end up with a host of issues, from data entry errors to data breaches).

This is particularly important for self-service options — especially if users are performing their own data activities — since governance will help manage data usability, integrity and security. This will ensure they have the right data, but will also ensure that sensitive or mission-critical data isn’t being exposed to anyone who isn’t authorized to access it.

With embedded BI, flexibility is key. Choose a platform that allows you to collect user feedback and make tweaks or changes as needed. This helps to provide the best self-service experience possible — from filters, sorting capabilities and drill-downs for business users to development rights for advanced users to build their own dashboards from scratch.

Analytics is no longer bound to desktops, nor is it exclusively the domain of data scientists. Embedded BI allows a broad range of users to access data for insight and action across multiple channels, whether external portals, business applications, customer-facing applications or software products.

This lowers the barrier for access to advanced analytics, so anyone — from employees to the general public — can get the benefits of embedded BI without straining IT resources.


About the Author

Vawn Himmelsbach

Vawn Himmelsbach is a writer and editor specializing in enterprise IT, writing for national newspapers and technology trade magazines on everything from AI to zero-day threats. She also spent three years working abroad as an Asian correspondent, covering all things tech.

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