KPIs are the Cliff’s Notes of business metrics: a handpicked selection of the many measurable quantities which a business can – or does – collect. They can provide vital clues as to how well the business is doing as a whole, for instance which marketing initiatives are and aren’t working, and thus they provide feedback and can guide decisions on how resources are expended, for instance discontinuing a promotional offer that’s not driving any new sales. KPIs are considered “key” often because managers deem them directly related to profit, which is why metrics such as year over year same-store sales are such a common KPI.

In order to tell a clear and convincing story, a given KPI needs to be transformed by analysis and visualization from raw data into business insight, and the correct way to do so isn’t always obvious. This is why traditional KPI analysis requires human data science knowledge and is very iterative, two aspects which prevent discovering actionable insights in real time.

Traditional KPI Analysis

In order to understand how Anodot’s full-stack solution makes dashboard-based KPI analysis tools obsolete, it’s helpful to review the typical KPI workflow which relies on those tools.

First, the underlying data for a KPI needs to be pulled in from whatever files or databases they live in. Then, that data needs to be structured or formatted in a way that makes analysis smooth. This step is called “data cleaning” and is often required if the files containing the data for the KPI have rows that serve as table titles or headers, or if data from two sources needs to be combined before analysis. Finally, the data is imported into the dashboarding tool, where it is analyzed and visualized.

Usually, the analysis and visualization isn’t a single step, but rather multiple iterations of reviewing, giving feedback and implementing changes. This is due to the fact that a whole team of analysts and data scientists often work on a single KPI, and the multiple iterations are needed before everyone on the team is confident that the visualization of the analyzed data will meet the requirements of management.

Although there are obvious benefits to this collaborative approach, it is very time-consuming and takes data scientists and analysts – of which companies have a limited supply – off of other tasks. Furthermore, even after a KPI visualization is created and added to a dashboard, it may be modified or even replaced by another one when it is reevaluated as time goes on and business goals are (or aren’t) met.

Once created, the dashboard visualization of the KPI is then monitored either manually or semi-manually via the use of static thresholds and alerts.

Drawbacks to traditional KPI analysis, tracking & monitoring

We’ve already mentioned the problems of tying up limited data science and analyst personnel as well as the fact that KPI analysis via dashboards is far too time-intensive to alert you to business incidents in real-time, but there are other drawbacks to this approach as well.

Since KPIs tend to be aggregated metrics (like averages or totals), they can hide isolated yet significant problems or opportunities, as we’ve seen in temporary ecommerce glitches, such as listing a top-brand headset as free, rather than at its normal price, even for just a number of hours. Also, this KPI analysis workflow can very easily miss important signals in your data if you choose the wrong KPIs for monitoring to begin with.

Lastly, the low number and aggregate nature of KPIs results in data that isn’t very granular. Granularity, as discussed in our previous post, is required to actually understand and fix the specific problems discovered on the dashboard.

Monitoring your KPIs with Anodot: a comprehensive tracking solution for all your metrics

As we mentioned above, Anodot provides a “full-stack” real time AI analytics solution. Not only can it accurately detect anomalies in all your metrics in real-time – eliminating the need to select a handful of KPIs – but Anodot’s correlation of related anomalies gives your analysts the benefits of granularity (specific actionable insights) with the conciseness you’d expect from a traditional dashboard tool. By giving you built-in data science, the majority of manual KPI analysis can be replaced by intelligently correlated and collated anomalies which point directly at business incidents.

Anodot is scalable to millions of metrics, far beyond the manual, iterative approach that relies on traditional BI dashboard tools. That scalability enables Anodot to track all your metrics, thus giving you the granularity you need to find the important signals in all of your data, not just the specific KPIs you think you should be looking at.

The more pixels, the sharper the image, and the key advantage of AI analytics is the ability to learn both what’s important and what’s related, not only showing you what’s there in the image, but also what it means.

Topics: AnalyticsArtificial Intelligence
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Written by Ira Cohen

Ira Cohen is chief data scientist and co-founder of Anodot, where he develops real-time multivariate anomaly detection algorithms designed to 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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