It doesn’t matter what industry you’re in — there’s more data at your fingertips than ever before. And with that data comes an opportunity to make informed decisions that will take your business to new heights.

For marketing alone, becoming best-in-class at data analytics can help you generate 20 percent more revenue than your competitors. Those benefits increase exponentially when you bring data-driven decision-making to every aspect of your business.

There are millions of key performance indicators (KPIs) that you can track. For fintechs, you may want to focus on average time to first transaction and churn. If you’re in eCommerce, it’s all about KPIs like conversion rates and cart abandonment. Telcos need to track network operating costs and subscriber acquisition costs. And adtech companies have to understand media costs and click-through rates.

But continuous, accurate measurement of your primary KPIs is only half of the battle. The other half is turning all that data into actionable insights.

That doesn’t mean you need a team of data scientists at your disposal, though. With the right tools, you can independently derive insights that increase revenue and prevent business crises.


The Road to Self-Service Business Intelligence

It’s no secret that traditional business intelligence tools are complicated. Even if you have the most powerful suite of BI tools for KPI analysis, it’s unlikely that business users can make the most of them.

Instead, business users have spent years leaning on IT departments to run queries, generate reports, and build dashboards. That approach may have worked in the past, but time is money, and your IT team doesn’t have the resources necessary to support agile, data-driven business.

This situation has sparked the rise of self-service BI, and professionals are using new tools so they can rely less heavily on data scientists and IT resources to keep tabs on their business. However, making BI truly self-service is easier said than done. Challenges include:

  • Data quality: Outdated, inconsistent, and flawed data negates the impact of business intelligence and analytics. And with so many siloed departments across organizations, cleaning up data isn’t so easy. Poor data quality costs businesses $15 million annually, so it’s imperative that they solve this problem before trying to increase analytics investments.
  • Dashboard ineffectiveness: Many self-service BI initiatives stop short at creating makeshift dashboards for business users. Having visualizations of primary KPIs can be valuable. But dashboards don’t provide insights into the context of your measurements. Without a commitment to more holistic data stores and governed metrics, business users won’t be able to gain real value.
  • Speed to value: Self-service BI tools may give business users functionality on the front end, but that doesn’t eliminate the need for significant work on the backend. You still need to build consistent data sets to fuel analyses and reporting, which can delay time to value and give competitors opportunities to surpass you.

Rather than hiring a data scientist to solve these issues (which comes with its own challenges), AI-based analytics tools can bridge the gap to self-service BI. And specifically, AI-driven anomaly detection can take your business users from simple KPI visualizations to anomaly detection that fuels data-driven decision making.


Automatically Track and Analyze KPIs with AI-Driven Anomaly Detection

Giving business users the ability to make effective data-driven decisions comes down to two key factors. First, eliminating the need for technical configuration. And second, getting rid of cumbersome dashboards in favor of KPI tracking that correlates to valuable business incidents.

Implementing AI-driven anomaly detection is the best way to achieve both. The right tool will integrate with analytics tools, applications, databases and streams, storage, CRM, and your IT infrastructure to seamlessly collect the most granular data possible across your business. It runs 24/7/365 to collect, sort, and analyze millions of KPIs. Once a baseline of behavior is set, AI-based anomaly detection systems can generate the best-fitting model that automatically tunes for more accurate measurements and real-time anomaly detection.

By automating the backend data collection and cleaning processes, AI-driven anomaly detection creates end-user-friendly analytics that prioritize insights according to business value. As the system trains itself for more accurate measurement of your KPIs, you’ll be able to forecast time series metrics automatically and enable business users to make informed decisions without the help of a data scientist.

When working with an advanced AI-driven anomaly detection tool like Anodot, cross-departmental metric correlation breaks down data silos in your organization and provides deeper insight into the impact of anomalies.

This isn’t a high-level, theoretical use case for artificial intelligence. It’s an opportunity to convert business events into actionable insights — and businesses are already making the most of the technology:

  • LivePerson ensures customer satisfaction: LivePerson’s operations service center couldn’t keep up with the need to monitor 2 million metrics every 30 seconds. Missing even one anomaly could significantly impact customer satisfaction, which is why LivePerson turned to Anodot. With Anodot, LivePerson can correlate business events (like feature updates) to performance anomalies and receive alerts in real time.
  • Eyeview protects itself against revenue loss: Eyeview’s programmatic video advertising platform receives 200,000 requests per second that need to be evaluated in under 30 milliseconds. The company’s revenue depends on its high volume, low latency, high throughput system. Setting thresholds for key KPIs wasn’t enough, because online advertising metrics can change hourly. Eyeview uses Anodot to improve its alert system and resolve anomalies before they hinder the hundreds of thousands of dollars the adtech platform handles every day.


The Takeaway

If KPI tracking and analysis feels overwhelming today, the situation is only going to get worse. The days of manual anomaly detection are long gone. Now, to keep pace with the millions of KPIs that provide insight into business performance, you need an AI-driven platform that scales alongside your needs.

Data-driven businesses, regardless of industry, can benefit from this kind of autonomous, real-time analysis. If you’re ready to get started with AI-based anomaly detection, contact us to see how Anodot can help you better analyze your KPIs.

Written by David Drai

David is CEO and co-founder of Anodot, where he is committed to helping data-driven companies illuminate business blind spots with AI analytics. He previously was CTO at Gett, an app-based transportation service used in hundreds of cities worldwide. Prior to Gett, he co-founded Cotendo, a content delivery network and site acceleration services provider that was acquired by Akamai Technologies, where he also served as CTO. He graduated from Technion - Israel Institute of Technology with a BSc in computer science.

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