How does your company handle anomaly detection for data quality management? Human, rules or artificial intelligence? Each system has its benefits and drawbacks, but in this article, we will show you how Artificial Intelligence. can improve your current anomaly detection solutions.
Anomaly detection involves identifying outliers or anomalies in your data. These anomalies, data quality issues, unusual patterns, or outliers can help your business develop insights about factors and conditions influencing the critical decisions you make.
Anomaly detection is so vital to all business work environments and industries. For instance, banks can use anomaly detection to find and prevent fraudulent behavior. With anomaly detection solutions, Insurance companies can filter out fake insurance claims, and manufacturing industries deal with machine faults proactively to avert downtimes. In a report in 2019, one of the largest US banks found 30% more fraudulent actions like stolen identities, forged identities, and co-ordinate attacks on customers’ data. This saved them over 15 million dollars, which would have been lost to fraud.
Even more, Anomaly detection is also a vital aspect of data quality management to help businesses, businesses leverage new opportunities by finding and understanding changing market behaviors. Such insights may be used to improve production, retarget specific customers, or optimize your marketing plan.
It’s no secret that anomalies can either limit or help grow your business. In reality, every single incident in your business is an opportunity to save, prevent, or take advantage of new opportunities. No wonder millions of companies are using one anomaly detection tool or the next, which has skyrocketed the global anomaly market from $3.48 billion to $5.45 billion in 2020.
Although there are endless benefits to apply anomaly detection to your businesses, the critical question is:
What’s your preferred anomaly detection technique?
Generally, you have three major selections:
• Manual checks using your eyes, statistical knowledge and intuition (human-based anomaly detection)
• Creating a set of rules to find these anomalies (rule-based anomaly detection)
• Using artificial intelligence that finds these anomalies based on historical data (AI-based anomaly detection).
To determine which system is suitable for you, let’s look at these different approaches based on their anomaly detection principles and algorithms.
How Human-Based Anomaly Detection work?
As said earlier, human-based anomaly detection occurs manually. So, your data expert will manually check incoming data. She will detect anomalies based on her statistical knowledge and intuition. Her intuition usually improves as her experience with the data set improves.
• Your data expert can find even the smallest deviations and anomalies.
• They can easily manipulate data sets to draw more insights
• It’s also easier to find what caused the anomalies.
• Human-based anomaly approach is slow and time-consuming
• It requires a lot of highly skilled data experts, which is expensive, and your costs increase as your data sets grow bigger.
• The increased risks of missing opportunities or errors because analysis happens too slow.
• Human-based anomaly detection systems are only feasible if you have a few parameters and a small dataset.
What is rule-based anomaly detection?
When using a rule-based anomaly system, you need rules already defined by you to sort through your data for errors. So, you must already have sets of likely events that might occur, such as sales increase, fraud patterns, and so on. You must create rules based on those possible events.
Here’s what your system routinely does: “if I find this pattern, then it should do Y action.” Hence, you are using pre-defined rules to find anomalies automatically and receive alerts based on those defects.
• Rule-based anomaly detection is straightforward to implement or integrate into your company’s systems
• You can use the same rule-based algorithms across many systems as long as you know the best anomaly practices in your industry
• Rule-based systems are less expensive and less time-consuming than human-based anomaly detection.
• You cannot solve complex problems with rule-based systems
• Rule-based systems are static, but your data changes over time. Therefore, you must regularly refresh, which is expensive.
• You can only detect anomalies that you define. You cannot find anomalies you haven’t thought of.
• If you have incorrect rules, then the answers you get will be incorrect.
• Adding new rules is challenging without contradicting what’s already in the system.
• The rule based-system cannot work anything out on its own, won’t change, update or learn from mistakes either, without your input.
How does AI-based Anomaly Detection Work?
AI looks like rule-based systems, but this anomaly detection solution is entirely different. In this case, AI uses your historical data to build models that automatically creates rules. With these self-learned rules, it will analyze new datasets and raise alerts automatically whenever those self-learned rules are violated.
Therefore, AI-powered anomaly detection software does not need you to set the rules, but will determine the actions to learn, and take. Even more, as changes appear in your datasets, AI-based systems simply learn new rules and adapt accordingly without your help or supervision.
• Your AI-system self-learns and does not need an expert.
• It’ extremely scalable, so you can automatically monitor a vast amount of data without human supervision.
• You can easily tailor the system to meet the demands of your business environment
• It can check millions of datasets for errors and inconsistencies in real-time, which is efficient.
• Because you waste no time in detecting data quality issues, it helps your company save more money and time that would have been spent on analysis.
• AI-based systems are proactive and can keep your business one step ahead of the market’s dynamics.
• Rules may not be as accurate as rules defined by an expert.
• Depending on the AI-based anomaly detection tool, it might be challenging to implement for small businesses
• Not many systems already in the market
So, what is the most perfect anomaly detection tool for your data quality management? There is no straightforward answer because the right approach depends on figuring out which system is best for you.
Human-based anomaly detection systems are okay when you are dealing with a small amount of data but become costly otherwise. Rule-based systems are also sound if you have a moderate amount of data, but these systems are not dynamic. Therefore, you must consider if refreshing your pre-defined rules is too expensive before using rule-based systems.
On the other hand, if you are dealing with millions of data sets, then there is no manual or rule-based approach to stay on top of your datasets. Your team members will never be able to interpret millions of data in seconds. Therefore, if your data sets are enormous, then AI-driven anomaly detection is the better technique to find all data quality issues. AI anomaly detection can help you manage your vast data in real-time to meet the dynamic and immense demands of your market.
AI-based anomaly detection solutions are perfectly suited for really big and dynamic data. It runs without any human interaction and can be extremely scaled. It can also spot anomalies you never knew existed in the first place. However, it isn’t necessary or useful when you have a small amount of data.
Your company’s growth needs anomaly detection, to leverage new business opportunities, stay ahead of your competition, deal with dynamic market changes, and see problems in your processes as they occur. So, you must carefully consider the costs, limitations, and benefits of each anomaly detection approach to figure out the right path for your business.
Whichever you use, anomaly detection presents a huge opportunity to empower your business with data-based and accurate decisions that can accelerate your business growth like never before.