Originally published at AW360.

Anyone who has ever forgotten where they left their car at an airport car park or tried to search for a particular face in the crowd at a football stadium understands the meaning of the phrase “looking for a needle in a haystack.”

Yet these people do have one significant advantage; they know what they are looking for. The owner of the lost car knows the color, make and registration number of their vehicle, while the person looking for a face in a crowd can look out for clothing color, approximate height, or their likely companions. Even the proverbial needle seeker knows the difference between a sharp metal object and a piece of dry grass. Imagine how much harder these tasks would be if the individual didn’t have any information about what they were searching for.

The same principle applies to marketing data analytics. Analysts spend huge amounts of time trawling through data to identify patterns and trends, even when they have a good idea of what they are seeking and are expecting to find. If they don’t know what they are looking for, effective manual analysis of today’s vast and complex data sets becomes almost impossible, with vital threats or opportunities easily missed.

Fortunately, automated data discovery technology now exists to take over the tedious task of manual data analysis and identify hidden trends. Powered by artificial intelligence, machine learning and advanced statistics, these technologies can manage and analyze data from multiple cross-channel campaigns. They can employ specialized techniques such as anomaly detection to intelligently augment analytics with precise insights that would otherwise have remained invisible.

The anomaly detection capabilities of augmented analytics have widespread benefits for marketers, particularly in identifying expensive errors and valuable opportunities.

Identifying costly errors and hidden threats 

A blunder that costs millions might sound like the stuff of nightmares but it is entirely possible, as Google’s “Night of the yellow ads” aptly demonstrates. A small mistake by a programmatic training team led to a large sum of money being wasted on plain yellow display ads across the US and Australia. Although the mishap was rectified quickly–within 45 minutes–Google was estimated to be out of pocket by $10 million. Luckily, Google identified the error quickly, most likely by having some sort of anomaly detection technology in place, but for smaller businesses, it would have been catastrophic.

Having technology like augmented analytics with data discovery and anomaly detection in place today can make the difference for marketers, by enhancing the reaction speed to critical business events. These technologies spot errors or unusual patterns in data that may look completely normal to the untrained eye. Mistakes of this magnitude are rare but if it can happen to Google it can happen to anyone. Technology that identifies errors and anomalies of any size can prevent businesses from wasting money and increase marketing efficiency.

Proactively uncovering new opportunities

In addition to identifying and mitigating risks, augmented analytics and anomaly detection can positively help marketers uncover and optimize opportunities. Marketers don’t necessarily make the best use of their budgets, wasting around a quarter on ineffective channels and strategies, but this can be improved by finding the right insights and optimization potential.    

Augmented analytics and anomaly detection present marketers with meaningful updates on significant developments within their data that would otherwise go unnoticed. For instance, they may discover a particular creative is resonating unexpectedly well with a specific audience segment, or that a certain type of ad is generating greater response in one environment than in another. Blind spots that occur in manual analytics because of time limitations – or human assumptions and experiences – are avoided, allowing marketers a complete understanding of what is driving or hindering success.

Based on pre-defined campaign goals and KPIs, augmented analytics not only identifies opportunities but also makes spend recommendations that allow marketers to optimize these opportunities. Machine learning algorithms calculate how to meet desired goals, such as revenue or conversions in the most efficient way, delivering recommendations to optimize marketing spend within and across all marketing channels. This precise insight enables marketers to maximize in-the-moment impact and align campaign strategy with audience preferences and requirements.

Much like looking for a lost vehicle in long-stay parking, or a single face in a packed stand, finding anomalies in marketing data is time-consuming and tedious. And, when analysts don’t even know what they’re looking for it becomes unfeasible. Through automated data discovery and anomaly detection, marketers can benefit from insights they never knew existed, finding invisible insights in the data haystack that allow them to avoid costly errors and optimize opportunities.