We just can’t comprehend data as AI can – that’s the main theme running through this series. In this, the third and final post of a series on uncovering hidden opportunities using AI analytics, we’ll discuss the shortcomings of dashboard tools, and how traditional business intelligence (BI) tools built upon dashboards can’t keep up with the speed of your business because they provide “too little, too late” when it comes to the information you actually need: real-time, actionable insights. Let’s breakdown the specific shortcomings of these big data dashboards, and demonstrate how an autonomous AI analytics solution fills the need that traditional tools simply cannot meet.

The fatal flaws of Big Data dashboards

None of the visualizations on the dashboard are actionable.

The scatter plots and bar charts can tell you what is going well or not, and even then only in very general terms. More importantly, those visualizations don’t tell you why those metrics are the values they are and knowing why is necessary for formulating and executing the fast reaction needed when a business incident occurs.

That reaction, the how of fixing the problem or capitalizing on the opportunity, is where business intelligence meets business strategy. At least it can unless you’re using a tool that provides only general indications.

Dashboards inherently gloss over isolated, but significant data anomalies.

If you read our previous post on business metrics, you know exactly why important business incidents affecting only one component or segment of your business get lost in the crowd of statistics like totals and averages which combine different individual metrics for one overall KPI. When it comes to anomalies, “isolated” and “significant” are not mutually exclusive.

  • A small blip may represent a large, untapped opportunity. A small spike in online orders from a particular demographic may indicate that a small-scale marketing campaign could be scaled up, and generate even more revenue and new, loyal customers for the brand.
  • The “summarizing” nature of dashboard tools is a serious barrier to real-time insights, especially to the crucial why, as mentioned above. In order to get to the reason, you need to group and correlate multiple anomalies with a variety of event data. However, without analyzing data to the most granular level of detail, there can be no correlation, and thus not reach any real-time actionable insights.
  • For this type of granularity, you need a solution that scales to the millions of individual metrics (time series) you are collecting. Dashboards, however, can’t keep up with the constantly changing and massive amounts of data and thus squander the BI value of this data.

A popular quote, attributed to Albert Einstein, gives some relevant advice: “Everything should be made as simple as possible, but not simpler”. Due to their focus on the visualization of only a few metrics, instead of insight from all in real-time, dashboards leave things too simple.

Big data dashboards alone don’t detect anything.

This is why data analysts have to set (and endlessly re-evaluate and re-adjust) static thresholds, requiring continuous manual monitoring. This is the key reason why they are Small Summary toys, not Big Data tools. Static thresholds produce Alert storms, a sea of alerts that data analysts have to spend way too much time trying to figure out what is at the root of the issue.

While doing this, there is a good chance that an important business service is performing poorly, or worse, it is down!

The limited insights from dashboards are usually too late.

Even if you had the human resources to continuously monitor those dashboards by skilled data analysts, you would still not achieve real-time actionable insights.

Traditional monitoring tools, like big data dashboards, suffer from inherent business insight latency: they do not show status in real-time (a key factor for a timely response) – which means that you will really only discover business problems when it’s too late.

Take, for example, how for a short period of time on Black Friday, visitors to the Currys PC World website in the UK saw £289 iPads listed for just £4 once a discount code was applied. Thinking they were getting a great deal they jumped on it, only to have their hopes dashed once the company realized the error. Then, there was the Amazon Prime Day glitch that brought the price of a $3,000 telescope lens to less than $100. While some commenters on Reddit said they had received confirmation that their orders had shipped, others were still waiting and wondering whether Amazon would cancel.

For isolated events – such as a spike in orders for a particular product due to a pricing glitch – this lag doesn’t just mean missing the event, but losing money and damaging your company’s reputation.

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The Autonomous AI Analytics advantage

Grouping and correlating multiple anomalies by design, AI analytics brings your team the most important insights first, eliminating alert storms. An AI analytics solution which can monitor millions of metrics to a granular level, gives you both the detail and scale you need to be able to identify the business incidents that matter, including the most subtle ones, that big data dashboards would overlook and obscure. Automated anomaly detection frees your talented data analysts from the futile task of trying to manually spot critical anomalies while your business is moving forward, sometimes at breakneck speed. Really, you want to save the data analysts for just that… the actual analysis. AI analytics continuously analyzes all of your business data, detecting the anomalies that matter, and identifying why they are happening through correlation across multiple data sources to give you the critical insights that just relying on dashboards can’t.

Your analytics solution needs to be intelligent to deliver business intelligence. Unlike dashboards, by using automated machine learning algorithms in an analytics solution, you can eliminate business insight latency, and give your business vital information to strike while the iron’s hot.

Written by Ira Cohen

Ira Cohen is not only a co-founder but Anodot's chief data scientist, and has developed the company's patented real-time multivariate anomaly detection algorithms that oversee millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has more than 12 years of industry experience.

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