Inside AI

Time to Move Beyond Anomaly Detection? Why Strategic Data-Driven Decisions Require More

AI-based tools can reveal trends and changes that are otherwise impossible to find in today’s vast sea of data

Data drives nearly every aspect of business today but as volumes of data grow, so do the headaches. Up to 73% of data within an enterprise doesn’t get used for analytics because of the difficulty and time required to analyze it. Even if you are making use of all the data at your disposal, turning it into actionable insights is another hurdle.

Part of data analysis is understanding what’s happening within your data. For example, some companies use anomaly detection tools that alert managers to changes in data. These basic tools are well suited for areas of a business where data varies within consist bounds, like operational data. If the goal is to keep a network or production process running, anomaly detection can quickly spot issues like lagging connections or overheating systems. They offer near-immediate alerts, which can equate to hundreds or thousands of alerts over the course of one day.

But business data is not like operational data. Business data can be inconsistent and unpredictable because it’s impacted by changes in consumer behavior. It doesn’t make sense to use the same tools that we use when we want to understand changes in operational data. Using anomaly detection to understand complex shifts in sales, advertising and website data trends will generate a lot of noise, but nothing actionable.

In order to generate useful insights, companies need more than anomaly detection. You need a tool that can automatically sift data, analyze it in relation to other data sets to provide context, and shape it into prioritized, targeted and usable business insights. You need daily business insights, as opposed to immediate operational alerts.

Automate Your Data Analysis

There’s a lot happening across your organization. For example, would you know if a spike in sales is the result of a marketing campaign, a seasonal factor or a trending topic on Twitter? Would you know if key customer data, like a sudden increase in website traffic or app downloads, are being driven by real users or by bots?

With anomaly detection, you may get a glimpse into the answer in the form of data points that you then need to piece together. An automated business analysis platform, on the other hand, can do the work for you. It does the work that humans would never have the time or ability to do and serves up just the right information that provides the change, the impact and the potential root cause.

An automated business analysis platform monitors data, contextualizes it with other data and notifies you of only the most important unexpected changes. By using artificial intelligence to reveal hidden patterns and relationships that are time-intensive — or even impossible — to find manually, it can create more personalized insights for the unique needs of your business or your role.

Automated Tools in Action

Companies across industries are using automated business analysis tools to take a deeper look at data and discover unknown problems and trends.

A leading pharma company used automated business analysis to reduce manual data sifting and uncover meaningful insights from its 331,000 metric and dimension combinations (i.e., time-series) and 8.6 billion data points. The company integrated its commercial drug data to get even deeper insights, allowing them to identify shifts in drug prescription rates across hospitals, providers and health groups, which indicated changes in treatment strategies.

They analyzed the relationship between delivery and supply chain to pinpoint issues impacting the availability of drugs in specific locations at certain times of the year, helping them stay in front of potential distribution issues.

In another case, a large financial institution used automated business analysis to uncover a sudden increase in the average fraud score for one its credit or debit cards. The tool helped the company identify the merchant type and eventually the specific merchant who was causing the higher fraud score. It also showed that the company’s systems were mostly approving transactions for the merchant despite the fraud score’s sharp increase. From this instance, the financial institution was able to better understand new patterns of potentially fraudulent behavior.

Get Insights When You Need Them

With anomaly detection, harvesting usefulness from data can take hours, days or months due to the sheer number of alerts and the limited context. That’s not good enough for analyzing business data. It’s important to learn about trends and changes soon after they happen, so you can quickly capitalize on opportunities or reduce the potential harm of problems. But it’s just as important to understand why these trends are happening.

Automated business analysis delivers on what anomaly detection never will: the ability to mine vast amounts of data and provide insights that can improve business practices each and every day. Unlike anomaly detection, automated data analysis doesn’t overwhelm with a flurry of out-of-context alerts. Instead, it offers a handful of daily insights that shed light on how to make real, important changes to your business.