how machine learning enables real-time KPI monitoring

The new reality of an always-connected world requires an always-informed business. With rapid global communication comes even more rapid global commerce. Traditional key performance indicator (KPI) analysis, however, is a holdover from an earlier business era in which decision-making was less frequent and more centralized. It’s not surprising then that KPI analysis via dashboard tools is time-consuming, historical (not real-time), and too general to be helpful even if the data being used were in fact updated in real-time.

Even after all the time and effort spent on data processing and visualizing, a blip in a KPI’s trend is only the very beginning of problem analysis and discovery of the root cause. Spending days manually searching for the clues in all your metrics is a time-consuming process. Meanwhile, that price glitch you’ve yet to discover is costing you several thousand dollars per hour.

Giving KPI analysis a speed boost

To achieve the real-time decision making required for today’s businesses, you have skip the middle man—the reports which often feed those KPIs with stale data – and use anomaly detection to monitor the raw time series data of all your business metrics.

As real-time anomaly detection and analytics vendor Anodot explains, traditional KPI monitoring requires iterative preparation and careful presentation, a fact that builds business latency into traditional dashboarding tools. This, combined with the fact that even great KPIs are too general to begin with, means that traditional KPI tracking tools like BI dashboards are unable to provide actionable insights in real time.

Anomaly detection, however, guides you to the root cause faster. Large-scale anomaly detection, in turn, is able to identify the individual metrics contributing to what you’re seeing in the KPI, since monitoring all metrics is necessary for catching all anomalies.

Let’s get granular

The ability to pivot from high-level KPI analysis to granular metric-level data in an instant is a superpower which only comes from a full-stack analytics solution that has data science built in. It’s an ability that new AI-powered automated anomaly detection solutions can deliver. Consider it x-ray vision for business metrics.

By combing large scale anomaly detection with machine learning, individual related anomalies can be correlated, resulting in condensed, concise alerts which allow your analysts to quickly figure out what metric is the root cause and which represent the downstream effects. This in turn frees up your human analysts to add some actual value, instead of slogging through alert storms and manually reviewing past trends.

A prescription for scalable analytics

For a good example of how AI can multiply the productivity of your in-house data science and analyst team, consider Human Dx, an online platform which combines AI technologies with crowdsourced medical expertise. Human Dx uses natural language processing (NLP) to analyze case reports which have been uploaded from primary care physicians. The results of the NLP analysis tell the platform which specialists to send the case reports to for their diagnosis. When the diagnoses all come in, machine learning is then used to check the responses against previous cases, and intelligently synthesize them together into a single, concise, coherent diagnosis.

Since machine learning is used in a feedback to loop to evaluate current diagnoses with past ones, each patient who is helped benefits every future patient. Over time, the human medical expertise and training of the specialists gets encoded into a large scale, widely accessible platform. Pairing human medical knowledge with machine learning multiplies the effectiveness of the specialists who contribute, reducing wait times for additional tests and prescriptions for many underserved (and predominantly low-income) patients.

If scaled up, this “specialist in the cloud” could revolutionize the delivery of medical care by slashing wait times associated with specialist visits. In the business world, analytics powered by machine learning is already revolutionizing KPI analysis by reducing the lag between business incident occurrence and business incident response. An anomaly detection solution that monitors everything can catch everything, eliminating overlooked business incidents and saving companies money.

Data: your most valuable asset

By spotting the anomalies in both the top-level KPIs and in each of the millions of individual metrics, automated anomaly detection can connect the dots between causes and effects, yielding insights into your business which were previously obscured by the sheer amount of data you’re collecting. That huge amount of data can be a goldmine of business intelligence you can now tap not only for quick responses to costly business incidents, but also for longer term strategic investment. Did an email promotion for a particular product result in a sharp spike in orders for that same product? You now have the data you need to convince the C-suite to fund and launch a more aggressive campaign for the next budget cycle.

AI-amplified anomaly detection zeroes in on what you should have been focused on all along: the actionable insights you need for today’s business world.