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Why Alerts Aren’t Enough: The Rise of AI-Driven Automated Analytics

In this special guest feature, Glen Rabie, CEO of Yellowfin, discusses how alerts are commonly used as a basic business intelligence tool, but there’s a better alternative: AI-driven automated analytics. AI has the power to parse the data behind dashboards and send a signal when significant activity happens. Here are five reasons why AI-driven automated analytics are better than alerts in today’s evolving business landscape. Yellowfin is an Analytics and Business Intelligence software company focused on helping businesses understand their data. Rabie is passionate about data and improving business performance through analytics. Prior to starting Yellowfin, he worked in various roles at National Australia Bank including senior e-business consultant and global manager of employee self-service. Rabie holds a Masters in Commerce from the University of Melbourne.

Alerts are commonly used as a basic business intelligence tool, which can serve a useful purpose as long as the tasks the user asks alerts to perform are simple and the business environment doesn’t change. The reality is, changes in data are hidden by the sheer scale and complexity of the data being analyzed. And so AI-driven automation is needed to detect the hidden changes user-generated alerts have not been set up to find.

Alerts are triggered by a threshold the user sets, meaning people set up a rule such as, “If sales fall below $1 million over a 30-day period, send an alert.” The problem arises when people try to use alerts to keep tabs on more complex performance indicators or spot emerging data trends. An alert is sent when something they suspect might happen actually does happen, but alerts fail at providing real-time performance insights or spotting timely opportunities.

Finally, there’s a better alternative: AI-driven automated analytics. AI has the power to parse the data behind dashboards and send a signal when significant activity happens. Here are five reasons why AI-driven automated analytics are better than alerts in today’s evolving business landscape:

1. Alerts focus on narrow patterns; automation detects all patterns: Since users set the threshold for an alert, the patterns alerts track are inherently limited. But AI can track a broad range of patterns — without requiring users to set thresholds. So, if you’re using an alert set to notify you if sales surpass $100,000 in the Southeast and there’s an uptick in sales in Florida that doesn’t cross the threshold, you won’t get an alert. However, with automation, you would know about the regional spike immediately and be able to act on a great new business opportunity.

2. Alerts are limited by rules; automation acts independently: Users don’t have time to set up rules for every conceivable combination of data to spot trends as the number of possible combinations is just too large. But AI-driven automated analytics can analyze millions of data combinations and send a signal when something happens, such as a spike in pay-per-click ad activity in a country where you’re not advertising. In that scenario, automation can flag possible detrimental activity for a business that an alert would miss.

3. Alerts aren’t personalized; automation determines relevance and adapts: It’s difficult to create rules to address every user’s unique needs. For instance, if you manage European operations for a shoe company, you’ll need different data than the person in charge of shoe sales in France. So, you can set up a different set of rules for each user. Or you can opt to use an AI-driven solution that learns from user activities which datasets are important to that individual and surfaces relevant insights — without the hassles of setting and managing rules.

4. Alerts don’t respond to changing conditions; automation detects business context changes: When users manually set up rules to trigger alerts, the system works as long as the scope of work and/or underlying business conditions don’t change. The problem is, those factors always change. So, users get tired of having to adjust the rules and simply stop using the alerts altogether. Whereas an AI solution can adapt to changing conditions and market factors dynamically while sending signals that are relevant for the evolving business context.

5. Alerts don’t account for unknown factors; automation surfaces opportunities: Alerts are fine for tracking specific data points. If you want to know when new product sales reach $100,000, an alert can tell you. But it’s often the unknown factors that identify a business opportunity to explore or threat to address. For example, if sandal sales are down in France but boot sales are up, the overall shoe sales trend may hold steady, so you won’t get an alert. But with AI, you can send a signal in real-time to quickly capitalize on the rising new interest in boots.

As these five points illustrate, alerts simply don’t work when business intelligence requirements are complex and market conditions change. In fact, alerts can be a wasted investment in that scenario, due to their inherent limitations and the hassles involved in setting up and changing rules as conditions evolve in the business landscape.

It’s time to try something more impactful instead. AI-driven automated analytics allow businesses to adjust to new information and learn from user activities. AI solutions are capable of considering huge datasets, recognizing a broad array of patterns and surfacing relevant insights based on individual needs. And that’s a lot more helpful to your business than an alert system that nobody bothers to use anymore.

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  1. A great post that comes down to “progressing if current processes are no longer sufficient/if further investment will produce a correlating ROI”.

    If a business hasn’t yet added processes to respond to and benefit from alerts, adding more complexity/sophistication to how those alerts are triggered wont add value to the business.

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