Not all that long ago, the only way for businesses to readily access their data was through dashboards. And, even then, these predefined and static dashboards provided data that was restricted only to citizen data scientists and data analysts. Standalone, static dashboards also inadvertently distract users and force them to shift their focus from their typical tasks in order to glean insights from data that are often overly broad to begin with. These days, however, businesses are increasingly adopting modern business intelligence (BI) tools to analyze their large volumes of data.

Burgeoning, innovative technologies such as machine learning and automated analytics are making it both feasible and viable for organizations to deeply analyze their data sets in order to glean valuable insights. With that said, dashboards aren’t “officially” gone: They may no longer be the best choice for a primary analytics solution, but many businesses still use dashboards to visualize and summarize data. 

Let’s take a look at how – and why – businesses are migrating toward modern BI tools and why those tools are so useful for analyzing data.

Why Dashboards No Longer Work as Primary Data Analysis Tools

When people talk about moving away from dashboards in order to address their modern business intelligence requirements, they don’t necessarily mean that dashboards are dead and completely obsolete – rather, the “death” of the dashboard really means that businesses have alternative methods through which they can communicate their data. 

While dashboards once were the only reliable source of data that drove informed business decisions, modern analytics stacks allow organizations to analyze every piece of data that they gather; the same can’t be said of traditional business intelligence dashboards, which now limit the agility of their data analysis and competitive edge. 

So, essentially, the “death” of the traditional dashboard is a simple acknowledgment that there is no such thing as a one-size-fits-all solution when it comes to business intelligence. 

The catalyst behind this shift? Without a doubt, innovative data analysis tools: AI and ML technologies and automated analytics tools deliver in-depth data analysis and make it possible for organizations to seize data-driven opportunities. Many of these emerging technologies don’t even require that their users possess the technical expertise to use them, which means businesses can glean deeper insights more quickly and efficiently without requiring their employees to develop new skill sets and shift their attention from their typical workloads.

Why Modern BI Tools Are So Useful for Varied Data Analysis

In our modern and hyperconnected world, data is driving businesses and informing their decisions in innumerable ways. Key trends such as AI/ML, data science, and big data analytics are at the forefront of the modern market, and as organizations strive to streamline their business processes, it becomes imperative that they embrace data-driven models. 

Below are some of the biggest reasons why modern BI tools are more flexible and ultimately more useful than dashboards when it comes to data analysis.

Ability to Work with Smaller Sets of Data

In the wake of the COVID-19 pandemic, historical data has lost much of its relevance as the modern business landscape undergoes permanent changes. Scalable and intelligent AI/ML techniques are supplanting more traditional ones, and they can visualize and summarize relatively small sets of data. 

Smarter, more scalable systems are much better than traditional AI techniques at protecting data privacy – particularly useful for medical, health care, or dental businesses that need to make sure any patient data collected in their software is properly protected – and they provide more rapid ROI. By combining scalable AI/ML techniques with big data, organizations can largely automate most of their manual tasks and subsequently make their teams more productive.

Digital Differentiation and Innovation

Data analytics models can facilitate significant digital innovation and growth. Agile, composed data and analytics models aim to create an experience for users accessing and interacting with data that is flexible and intuitive. These models allow business leaders to drive their actions based on business insights gleaned from data and facilitate improved collaboration and productivity among their employees.

Edge Computing for Faster Analysis

Despite the preponderance of big data analytics tools that are saturating the modern market, there still persists the issue of businesses needing to process huge volumes of data. To address the need to process so much data, the data industry has begun applying laws of quantum computing to accelerate data processing capabilities in order to visualize and summarize large volumes of data. 

Thanks to quantum mechanics, processing capabilities require less bandwidth and can provide improved data privacy and security. Edge computing for faster analysis presents a much more attractive alternative to classical computing considering that a processor’s quantum bits can solve problems in just a minute or two. Although it still requires more fine-tuning before becoming suitable for widespread public adoption, edge computing for faster analysis will inevitably become crucial to many organizations’ business processes.

Automated and Enhanced Analytics, Data Sharing, and BI Tools

Among the popular modern business analytics trends is what’s known as augmented analytics: Augmented analytics is a concept that leverages areas of data analytics such as AI/ML and NLP in order to automate and enhance things such as BI and data sharing and analytics. 

Augmented analytics streamlines the process of preparing and automating data for the sake of gleaning useful and in-depth insights. Augmented analytics are so useful, in fact, that they can take over the functions of a traditional data scientist. On top of that, augmented analytics – when paired with data inside and outside an organization – can improve business processes and make them easier for a greater number of employees to carry out.

Conclusion

As the traditionally predefined and static dashboard steadily declines in prominence, a new age of analytics is being ushered in. Exciting and burgeoning technologies are becoming more readily accessible to non-technical users, who will no longer need to spend hours interacting with dashboards and forgoing their typical workloads. These new technologies and features are placing sophisticated analytical capabilities into the hands of more users who will soon be able to perform functions previously reserved for data analysts. 

The “death” of the traditional data-based dashboard is here, and it’s now limiting for businesses to exclusively leverage dashboards for the sake of gleaning data-driven insights. Instead, organizations should seek to explore modern BI tools in order to interact with and analyze data in multiple useful ways.