Analytics & Insights, Decision Intelligence

Dashboards and the Journey Towards Decision Intelligence

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Across every industry, companies are generating and storing data at an exponential rate, yet businesses struggle to know what insights are available, how it’ll impact them, and what insights to act on when making critical decisions. For years, the key output of almost every data analytics initiative has been to deliver dashboards to business users and decision makers, a visualization that’ll help tell the story of the “what, how, and why” questions when trying to look at the bigger picture.

But that’s not where the journey ends. There will always be more questions, so most organizations begin a never-ending cycle of creating new, one-off dashboards. Not only does it create a growing backlog of requests for the data analytics team to work through, but after a few cycles of this, end users are presented with an overwhelming number of dashboards, with no idea how to get the answers they need from them. This is what we call “dashboard overload,” and user frustration is driving organizations to turn the focus away from traditional dashboards and going beyond the colorful graphics and numbers.

Dashboards Can’t Tell You the “How” or “Why"

Every organization spends a lot of time talking about the power of data insights and how to better align what we’re learning and how to act on what we’ve learned. That being said, there’s more than just visualizations or machine learning to get to the answers you’re looking for.

Modern business intelligence extends beyond monitoring your operational metrics with dashboards. It is essential for every business to know:

  • What is happening and changing across the business for every slice of data you can possibly imagine, not just for the ones you have historically deemed to be most important.
  • Why things change. In our example, you might want to know that a change in revenue was tied to, let’s say, coupon usage, social media interactions, or website visits. And while these might be things you tracked at a macro level, your dashboard won’t have investigated all the possible metrics and slices of data at a granular level to uncover the reasons performance has changed and help you explain why.
  • How to improve performance. This is the key information for all decision makers, and yet, your traditional dashboard is not built to tell you which segments of customers are most likely to buy to help improve revenue. This level of advanced analysis requires much more intelligence than the typical static query-driven data visualization found in dashboards.

Consider this, on a daily basis we’re asking our phones questions such as “How many COVID-19 cases have been reported?”, “What are the most popular fruits by region in the US?”, or “How much does a trip to Italy cost in the summer vs in the fall?” Maybe not those questions exactly, but you get the idea.

Our phones aren’t popping up charts and graphs or taking days to get back to us; instead the search functionality is able to pop up our answers on the screen instantly, giving us breakdowns of information pointing us to the answers we seek. With that information instantly provided, I know what time of year I should plan a  “trip to Italy” based on the rise and fall of expenses per season and can make the best decisions based on the data provided on how much to budget. This is the model a data analytics initiative should strive for.

Tools vs. Answers

There are dozens of enterprise data tools that advertise themselves as being the future of analytics. Data lakes and warehouses, visualization tools, streaming analytics, and data science programming languages, just to name a few types of tools. Most data analytics tools will not meet the promise they aim to deliver. The first generation of legacy BI tools were focused reporting and static visualizations – they were not built to handle the scale and complex environment of dynamic data landscapes. The second generation of BI gave us “self-service” tools and interactive dashboards that delivered data more often but still left us turning to our data specialists to understand why things changed.

The question is not if data science and machine learning will bring about the next wave of business intelligence, but how it will usher in change for every decision maker.

Data science tools require smart, trained people with knowledge of open source languages like Python and R, or traditional, legacy statistical and data science packages. One problem is that these individuals are in such high demand, there aren’t enough data scientists to help every part of the business.

What are data analytics leaders supposed to do? 

To better align with the business, data analytics leaders need to look at what business users need from a decision making perspective, and not from a data and algorithms perspective. Business users want:

  • Answers delivered quickly and efficiently
  • Information that is easily accessible to them
  • Insights that are easily understood and not subject to interpretation
  • To collaborate with data specialists, instead of waiting days to re-shape incorrect analyses

Data Analytics Delays – A Deeper Dive

When someone has a new question related to the dashboard they’re being shown, they filter down their dashboard even more or to look through multiple dashboards to find the answer they need. But what happens when all of that fails? They turn to the data analytics specialist that sets the analysis process in motion:

This process is an issue because not only are there a lot of steps to be completed, but there’s also the complexity of the process. Because all of these steps need to be completed before a business user can know what the data tells them this causes the time delay from beginning to end. What if the new details still don’t answer the question? The entire process begins over again. There are so many roadblocks and bottlenecks that what once seemed like a simple question in an executive meeting, is now what’s standing in the way of taking the next best business action.

Decision Intelligence – Helping Business & Data Analytics Teams Get Answers More Quickly

Compared to the process outlined above, decision intelligence guided by AI enables a much more streamlined process that delivers answers much faster. An instant response gives business users better access to data and allows them to ask those new questions as they come up without kicking off the long and daunting process all over again.

AI-driven insights are the shortest distance between questions and answers. The workflow offers a much faster and more direct route from a business user’s questions to acquiring an answer backed by the data.

Guided Insights Workflow

  1. Ask a question or click a chart to get an answer (even a complex one)
    Users can ask questions of data in a Google-like search interface by typing or spoken by voice. Or, when viewing a data visualization with interesting trends or differences between data points, users can just click on the chart to kick off a deeper analysis.
  2. Get advanced insights guided by AI automation
    When advanced analysis is required, such as learning why metrics changed over time, comparing groups of transactions to discover differences between them, or uncovering segments of customers who are most likely to behave in a certain way, machine learning algorithms run behind the scenes to automatically reveal these insights to the user without coding. Narratives are automatically generated to help users interpret results.
  3. Receive proactive analyses
    As the system learns what metrics and data users are interested in, it will monitor these metrics and anticipate the types of insights users will find meaningful, and proactively push these findings before users even ask for them.

Through this process, the business user and the analyst are able to provide an answer that is more thorough and actionable without slowing the business down.

Decision intelligence is built on a paradigm and technology foundation that is different from  traditional business intelligence and analytics. It applies machine learning algorithms with AI-driven automation to get answers from all data into the hands of users faster and more easily. Traditional business intelligence and visualization, however, focus primarily on aggregating data and creating subsets of data for manual analysis that can take days or weeks to complete. Decision intelligence handles the complexities and volume of data to show the insights necessary for businesses to make critical decisions.

Death of the Dashboard?

For years, industry analysts have said that dashboards are declining, but in reality businesses today are still using dashboards with operational reports, and even products that claim to be replacing dashboards end up living side-by-side with them.

But there is truth in saying traditional dashboards are not the cutting edge of data intelligence – this isn’t “new news.” In fact, dashboards are table stakes for first-level monitoring of metrics and sharing of information. More and more organizations look beyond dashboards to give greater data intelligence, faster, and within reach to more people.

So, it may not be the “death” of the dashboard as a business analytics tool, but more the death of the dashboard as the differentiator which is a more realistic way of framing it. Businesses have enormous potential if they are willing to embrace this new way of engaging data: happier users, streamlined workflows, faster insights, productive data teams, and more confident decision making.

We have the technology, now we need the confidence to say “no” to relying on dashboards solely, and “yes” to a process designed to generate business-ready answers from data.

Learn more about how Decision Intelligence delivers critical answers from your business data when Dashboards Aren’t Enough.

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