DataOps for the Modern Business: Rethinking the Dashboard

Data is a critical company asset that has continued to grow exponentially for over a decade. According to a recent white paper by research firm Frost & Sullivan, 2.5 exabytes of data are created daily ("Why Embedded Analytics is the Future of Data Utilization").

However, to create game-changing business value, data not only needs to be analyzed, but it needs to be utilized. BI has long been delivered through dashboards that promised self-service, but adoption rates remain low among business users. Years of training and positive intent have failed to create the widespread embrace of insights that companies need in order to survive and thrive in a changing business world. How can we rethink the dashboard for modern business needs which require companies to be more agile and innovative for both their internal employees and their customers in a rapidly changing business environment?

Going Beyond Dashboards

In a recent blog post titled “BI Is Dead; Long Live BI,” Boris Evelson, vice president and principal analyst, Forrester, said, “The perception of legacy enterprise BI platforms comes with some legitimate stigma and baggage. It’s technology first, not business-led; the graphical user interface-based user experience doesn’t address ease of use for all business decision-makers; there are too many underutilized reports and dashboards floating around in the enterprise; and signals produced by BI applications aren’t actionable, resulting in a disconnect between BI and tangible business outcomes.”

Dashboards offer numerous business benefits with data that promote operational efficiencies, better customer service, more value in products, and new revenue streams. However, sometimes dashboards come with pitfalls. If dashboards are not relevant, they can be underused and underutilized. They also may be too broad to address multiple needs, difficult to customize, and can offer too many, or conflicting, insights.

A main challenge with dashboards is that they require an inefficient and focus-intensive deviation from a user’s central workflow. Dashboards also lack inherent analysis or guidance; they describe what has occurred but don’t prod users to make decisions or recommend particular choices based on data. That’s the missing link for companies to become truly data-driven.

Actionable and Digestible Data

While analytics and BI superusers love dashboards, most users just want the bottom line. They want data and insights in easy-to-digest bites. They need versatile  analytics with functionality that fits into their workflow.

According to the "Gartner Top 10 Data and Analytics Trends for 2021," “dashboards will be replaced with automated, conversational, mobile and dynamically generated insights customized to a user’s needs and delivered to their point of consumption. This shifts the insight knowledge from a handful of data experts to anyone in the organization.”

Instead of jumping between where the data lives and where the work gets actually done, users get insights and actions in the same place, boosting analytics adoptions, and improving outcomes and efficiency—all at once. Best of all, how this synthesis appears is up to each individual user, based on their role and where and how they work.

Embedded Analytics

Each business- and user-type has different data and decision-making needs. This means the “beyond the dashboard” analytics solution they use has to be versatile and personalized enough to suit their unique requirements. This is accomplished by embedding, customizing, and integrating it with other apps.

The standalone analytics utilities described are designed to go in a user’s workflow anywhere the information and insights are most useful. That could mean they sit in an application, a process, or a workflow, giving the user constant updates on KPIs that will drive decisions and actions throughout the day (with appropriate functionality built-in).

Embedding analytics within applications that users live in every day puts the information they need—and the actions they need to take based on that information—right in the same place. While dashboards might not be dead, switching between an analytics platform and a work application certainly is.

Whether the end users are internal collaborators or external customers, embedding analytics into applications and products can help visualize insights to the point of decision, creating increased value to end users in the context of their role. In return, organizations benefit from information that empowers them to disrupt markets and innovate, adding top-line growth and improvement to their bottom line.

Humans With AI

When we think about going beyond the dashboard to what an ideal data analytics experience should be, we have to touch on AI. It’s not about automating humans out of the process but supercharging what’s possible.

AI provides a new level of understanding that extends what existing analytics platforms can achieve. That’s because platforms with AI can learn from the patterns of data they analyze. AI can extrapolate insights and suggest new phenomena that may not have been anticipated. It can be leveraged to power exploration widgets and drill down deeper into data to answer business questions—current and even unasked.

“Smarter, more responsible, scalable AI will enable better learning algorithms, interpretable systems and shorter time to value. Organizations will begin to require a lot more from AI systems, and they’ll need to figure out how to scale the technologies—something that up to this point has been challenging,” according to the Gartner's top trends report.

AI algorithms scan entire databases and make suggestions based on both analysis and the usage patterns of dashboard users. As AI algorithms collect more input from users’ activity, suggestions will become insights specific users can act on.

The result is proactively sending actionable insights where and when people need them without them having to step out of their workflow. This provides a much more valuable and seamless way of working for business analysts and/or the company’s customers. Using AI, analysts can become more focused on forward-thinking scenario planning—asking what-if questions of the data—rather than simply analyzing current or previous trends. With this capability, analysts can sharpen their organizations’ competitive edge by identifying future KPIs and improving outcomes. Furthermore, this can be achieved in real time, and without coding skills, to instantaneously deliver new answers to the questions users have in mind when they want to uncover fresher and deeper insights directly within dashboards.

Turning Insights into Action

“In the future, BI will enable business users to turn insights into actions without having to leave whatever business or productivity application they have open—a natural extension of embedded BI, plus an emerging use of BI platforms as general purpose (not just analytical, read-only) low-code enterprise applications development,” said Forrester's Evelson in his blog post on BI. 

One innovative example is DNV, an assurance and risk management company. DNV is generating new subscription revenue through its analytics platform, serving nearly 100 utility companies in North America. This platform, “Cascade Insight,” is purpose-built to deliver actionable insights about utility asset performance. Their customers are able to get ahead of maintenance issues and avoid service outages in their grid, ultimately saving money and delivering better value and service to their customers.

Another example, Air Canada, the largest airline of Canada, serving more than 210 airports on six continents, has invested heavily in integrating AI into many different facets of their business operations. Embedding AI into the corporate safety processes has transformed operations in the maintenance department, where an airplane part can be flagged and replaced before it fails. Providing access to actionable data to their frontline employees empowers them to make immediate decisions—and improves safety in real-time for both passengers and flight staff.

Flipping the Script

Instead of requiring people to change the way they work to access insights, we see successful organizations flipping the script and infusing analytics where people work. By putting the right intelligence into workflows, processes, and applications, analytics-fueled decisions become automatic and instinctive, enabling organizations to surpass their objectives.

A dashboard or a widget is published with the hopes that people will find it and use it to make informed data decisions and take action faster. This is a good starting point, but adoption takes more. Highly customized data experiences that are pushed to users in an automatic way have the best chance of utilization. Dashboards are certainly still a part of the lifecycle, but narrowing the focus and putting the right insights in the right place for each user to extract maximum value from them and make the best possible decisions is maybe the optimum way for businesses to harness data.


Subscribe to Big Data Quarterly E-Edition