Published on Fri 27 Jan, 23


João Sousa

Sales Manager

Dashboards have been around for over 20 years as the go-to product for business intelligence. Back then, businesses didn’t collect much data and dashboards enabled both reporting (i.e., metrics monitoring) and actionable insights (i.e., diagnostic analytics).

However, a lot has changed in the meantime. Businesses collect more data than ever. In fact, 90% of the world’s data has been collected over the last 2 years. Businesses have millions of data points across products, marketing, etc. Moreover, the needs and expectations have significantly increased. Business stakeholders need to be agile, hence expect quick and comprehensive answers in order to make decisions before it’s too late.

This new complexity and expectations make dashboards fall short for many use cases, especially around data exploration and diagnostic analytics. Many teams navigate around multiple dashboards as if they are navigating in a foggy forest hoping to have a light to help them uncover a path.

“Dashboards started out as reporting tools for measuring the health of the business. They’re great for this purpose. The problem arises when you extend this simple purpose into data exploration.”

Where dashboards are valuable

Some vendors claim that dashboards are dead, which I consider to be exaggerated. Dashboards are here to stay. They are very valuable in business monitoring, understanding how key business metrics are evolving, and to catch high-level trends. (e.g., marketing spending across regions). They enable people to observe what is happening in the business over time.

Where dashboards fall short

The problem raises as dashboards are trying to be repurposed. Every time business stakeholders ask data analysts for a new dashboard on a key business metric, what they actually say is “We need to understand what’s driving this key business metric and what can we do to improve it.” Adding more charts looking at the business metric from different angles is likely useless. All these dashboards won’t answer the most fundamental question: “Now what?”.

Oftentimes data people believe that a comprehensive, well-designed dashboard is a very capable data product that gives business stakeholders endless possibilities to answer any questions they might have.

The reality? Frequently, it’s far from it. Oftentimes business stakeholders and data analysts run into an insights dead end. To reach the “so what”, teams first need to understand what triggered the change in the metric. And the process of diagnosing a key business metric change can be very tedious. And outcome looks something like this:


The outcome of dashboard-based analysis (image by Anderson for eQuest)

The new reality — needs and requirements

The new reality in the amount of data and business speed is making these shortcomings more evident and these 3 requirements critical:

  • Super-human speed — business stakeholders expect quick answers to performance changes to be agile and stay ahead of the competition. Teams can’t wait days or weeks to get answers.
  • Comprehensiveness —with more data points than ever, stakeholders expect more granular insights to drive better decisions. This requires leveraging all the available data to connect the dots and find granular insights.
  • Focus — Remember the foggy forest and the light analogy? Nowadays BI platforms are expected to help teams fast-forward their analysis, by pinpointing where to look.

Consequences of dashboard-based workflows

Relying on reports and dashboards that only present what happened with key metrics and having to drill down to why these changes happened manually doesn’t meet the expectations anymore. Nowadays organizations set carefully designed metrics, expect to be able to monitor these on a daily basis and make fast decisions based on the latest numbers.

Continuing with traditional approaches results in 4 main negative consequences, leading to significant lost business value:

  • Reactive — Most of the time business teams spot a sharp increase or a decrease on a dashboard. As it isn't clear which next step to take, they depend on the data team to pull out trends/insights to work on which creates a long feedback loop.
  • Slow — Dashboards require manual analysis to understand what triggers changes. Hence numerous factors and combinations of factors need to be checked to catch a meaningful trend, which becomes extremely time-consuming.
  • Hidden insights - This problem is two folds. Firstly, dashboards do not show what’s happening underneath the hood. Meaning, if there are two factors canceling out one another, your metric will still appear stable. Secondly, it is not possible to test all the different hypotheses at hand manually, analysts tend to go after the usual suspects. Your unknown-unknowns remain never tested.
  • Waste of resources — 2-4 hours of manual analysis might appear as part of the job, but throughout the year, it takes up 3-6 months of your team’s time. When the work is repetitive and can be done accurately and more comprehensively by machines, it brings a lot more value to free up your analysts for strategic tasks.

Ideal workflows

Dashboards rarely provide actionable insights. Teams should focus on complementing reporting dashboards with analytical products that enable comprehensive diagnostic analytics at the speed of business.

Dashboard vs Kausa

(image by Kausa — dashboards for reporting and high-level trends; augmented analytics for diagnostic analytics)

New requirements push for solutions based on 5 pillars:

  • Augmented: leverage Machine Learning to run statistical tests, identify anomalies, and detect relationships between metrics and segments/dimensions with the highest contribution (more detail on augmenting workflows)
  • Proactive: Business should not have to check 20 dashboards every day to assess whether it’s business as usual. Data teams should not be waiting for business teams to submit requests. Leverage ML to deliver automated insights on key business metrics, covering what’s happening and why.
  • Insightful: go beyond alerting or showing what’s happening. Provide insights into why it’s happening (read more about the diagnostic analytics gap) Integrated: leverage already existing workflows and daily tools as the main interface (e.g., Slack). Reduce attrition by not forcing business stakeholders to use multiple platforms/UIs.
  • Conversational: provide a natural language interface where questions and insights are available in plain English.

Screenshot 2023-01-27 at 11.12.44.png (image by Kausa - new requirements of Dashboards)

Use cases

Dashboards need to be complemented whenever speed and/or comprehensiveness of the analysis is paramount. A couple of real use cases that have been rising:

Automated insights messages on key business metrics drivers

Many business teams take daily/weekly decisions to optimize the KPIs they own. For instance, performance marketing managers adjust their campaigns and bids on a daily/weekly basis to optimize ROAS (Return on ad spend). With dashboards-based workflows, these teams either spend hours slicing and dicing on dashboards or cut corners and miss out on insights. Augmented analytics can automatically test all the hypotheses, rank the drivers and deliver automatic messages on key business metrics. They can be delivered over Slack, Teams, or Email, making analytics more accessible.

Product - ahead of game.png (image by Kausa - delivering data-driven insights over Slack messages)

Comprehensive exploration into drivers of key business metrics

While business teams enjoy easy-to-consume insights, data teams need a smarter way to explore data. The need for diving deeper into the analysis and contextualizing it will never disappear, but it is critical for tools to bring focus to the analysis. So that teams know exactly where to start and focus on segments/subsegments which are prioritized after automated analysis. In the example below, augmented analytics can show that one specific marketing campaign (across hundreds) in Germany is driving a significant increase in AOV for younger customers. Finding these insights in a manual way is like looking for a needle in a haystack. Through augmentation, it takes just a couple of clicks (read more in this article).

Kausa ranking.png (Image by Kausa - Priotization of drivers by impact)

Conquering the last mile of the analytics journey

As businesses collect more data and change faster than ever, BI expectations and needs have evolved. Data teams need to deliver effective self-service to scale impact and outcomes. Actionability requires insightful information delivered in a proactive and easy-to-consume manner. Thus BI needs to become augmented, proactive, insightful, integrated, and conversational. This is the only way to improve speed to insight, make analytics more accessible and multiply the outcome of analytics teams.

Want to make sure your organization is ready for the new requirements of business intelligence? 

About the author and Kausa

João Sousa is the Director of Growth at Kausa. Stay tuned for more posts on how tomore posts on how to nail diagnostic analytics and increase the value of data.

Using machine learning, Kausa can analyze all your data, diagnose why metrics are changing, and provide actionable insights to improve business performance. With Kausa teams can save countless hours on data analysis and get ahead of competition by finding opportunities to unlock hidden value.