How to Detect Anomalies in Your Data?
Data is an essential thing in today's world; all the big digital conglomerates that you witness today are the combined result of different forms of data interacting with each other constantly. Finance, banking, internet, cloud computing, and even day to day tasks can't be pulled off without that data working properly and in a typical fashion. This is why the security of the data and other information is prioritized among digital and cloud-based companies. If the data's very integrity can't be promised, then there is no reason for carrying on with sorting it, categorizing it, and shuffling it around for it to mean something.
Like every other thing in this world, data has various behaviors of its own, not in a human way but in its digital form; it can reside in various types, and every type has its unique properties. This article revolves around the concepts of data anomalies, why these happen, and some of the lingering effects.
Data mining is a classic field of study that focuses on extracting the data from various resources, categorization, and processing so that some insight or hidden meaning can be spelled out. But as it happens during processing, sorting, or storage of the data, some anomalies or bad patterns emerge that compels the data sets to behave in an unconventional way. Data clusters start breaking and become haphazard; this is the beginning point for any data anomaly there is. More details are shared as follows;
What is Anomaly Detection?
Every dataset subjected to data mining has its specific properties, as illustrated above, and should behave in its most natural or expected way. But when it doesn't and start deviating from its natural behavior, this is called an anomaly. Anomaly detection is merely a step in data mining that identifies the data points, events, and observations that deviate from a particular behavior when it comes to the datasets.
A change in consumer behavior and a technical glitch both point towards the data anomalies; in order to detect such anomalous patterns, machine learning is being used, and so far, it has provided some impressive results.
Why Does an Anomaly Occur in Data?
There are a various data analytics and BI tools that businesses worldwide use to drive insight from the data they receive from their customers. These tools can process such hefty amounts of data for it to mean something, and so future decisions about various key elements of the business can be taken, such as KPIs (key performance indicators) can be studied and explored at a decent pace.
On the other hand, datasets consist of the data patterns that represent the business performance as usual. So, anything that directs towards an unexpected change within these data patterns or an event that does not comply with the data metrics at hand is considered and labeled as an anomaly. In simpler words, an anomaly represents deviating from the normal functioning or emergence of the patterns.
Let's take an example to understand it better; suppose you visit an online store to buy something, and they don't have discounts on anything; why would there be? It is a straight day, and no special occasion is near. But when you visit that store at a Cyber Monday or Black Friday sale and no item is on sale, that will be an anomaly. This was only for developing a reference you can take on any example you want and probably finer than this one. Anything that sparks abnormality or suspicion in terms of the datasets will be recorded as an anomaly.
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What Is Time-Series Data Anomaly Detection?
If you are to detect the anomaly's overall potential that you are observing right now within the data sets, then it has to be done in real-time or at the spot if you may; this is done with the help of time series. Time series data of a specific or suspicious data set will be taken into account and observed closely in real-time. There are different sets of values in the dataset at a given time. Each and every point is a pair of two items. These can be classified as; a timestamp for when a specific metric was measured and the value that is associated with the metric at that time.
Time series data isn't a projection of anything; you can't expect it to be presented before you in terms of charts and graphs. It only contains the information necessary to make the educated guesses about what can be expected from the current numbers about the company's financial future. Actionable signals that are present within the data can be identified with the help of the anomaly detection systems about future expectations. This will alert you about your organization's key events by tracking down the outliers or unexpected activity that is taking place by studying various KPIs of your business.
Following are some of the dedicated examples for which the time series data anomaly detection can be applied;
- Number of sales your business secured in the past.
- Daily active users that your website has
- Cost per click
- Bounce rate
- Average order value
These are some of the events listed here, which can be effectively monitored with the help of the time series anomaly detection. The results that seem to deviate could be identified in the form of the outliers.
Using the past data, the time series data anomaly detection should create a normal behavior speculating the normal KPIs at first. Now when the baseline is effectively covered, the time series data anomaly detection would track seasonality, which corresponds to the cyclical patterns of the behavior residing into dedicated datasets.
Types of Time Series Anomalies
There are different types of outliers that get identified by different time series anomalies, and that is why it is essential to understand its various types, to begin with. Suppose you don't know what you are dealing with. In that case, there is always a chance of making the wrong decision and then regretting it later because the anomaly detection system would alert you about the issue or opportunity based on the metrics you have selected. In order to fill you in all about the different types of time series anomalies following are some of its kinds that you should know about well in advance before going on with its detection;
- Global Outliers
These are also known as the point anomalies and exist far outside the entirety of a dataset. It means that it is implausible to affect the performance of your datasets or the analytics tools that you are using for the sake of studying data and extracting insight from it. It is likely to affect your datasets but is currently affecting the global market, so there is really no need to worry about it. But it is better to be prepared than sorry, so the least that you need to do is to know about this very type.
- Contextual Outliers
These are also known as conditional outliers. These are the anomalies with values and would significantly deviate from various other data points in the same context. There is something else that you need to note right here, and that is the fact that an anomaly within the context of one dataset might not be the same anomaly that is present in the context of the other dataset. There is a chance of diversity sprouting right here, and that is why you need to have all the information that you can get your hands on. These outliers are more common in the time series data because these are the datasets that are a record of specific quantities present in a given time period.
Although the anomaly is likely to exist within the global regime, there is no reason for it not to show up in the seasonal data patterns, so you should know about it well in advance.
- Collective Outliers
When a subset of the data point within the entire data set is anomalous, then those values are known as the collective outliers. Anomalies become clearer when you give this specific type of outlier some attention, and you would see that these are making some sort of pattern here that even becomes easier to understand in the long run.
Why Does Your Company Need to Work with Anomaly Detection?
If you are an entity that heavily relies on the uptake of fresh data and its processing so insights can be drawn, and future decisions can be made, anomaly detection is for you. You probably may want to understand what happened with the latest anomaly that occurred within the data and why the results associated with the whole dataset also changed; this is where the concepts of anomaly detection come into use.
There are thousands of independent metrics and variables that need to be measured here and the cross results of these anomalies. Such as 'what was the results before and what they are now' is what makes the whole thing more challenging and advanced, and that is where anomaly detection finds its use case.
- Application Performance
The performance of the application is what can make or break the game of any financial institution. If you are a tech company whose app is not working or has stopped responding to consumer requests, then that is a terrible sign, and something needs to be done here and fast. But what if you could find out what was wrong with the application in the first place with the help of anomaly detection and could correct it in a given time, then you won't have to experience the downtime, and the whole situation would never have occurred.
This is what those large digital conglomerates are doing out there, and this is what you need to do with your company as well if you want to avoid these unnecessary downtimes in the future.
- Product Quality
E-commerce would be the ultimate platform that needs to be taken into account here as an example. As the products are emerging at a faster pace and new entries are being made, the quality of these products can't be speculated at every checkpoint, such as during shipping, when it is packed and received. That is why anomaly detection would have to be put to work here; it can cross analyze various departments of your business to find out deviations that are happening from the normal operational behavior, which will provide you with enough to pinpoint the exact location of the anomaly. This way, you will be able to keep the quality of your product at an optimum scale.
- User Experience
Suppose you have various users interacting with your products and services; they interact with these things regularly and pass on their thoughts in the form of consumer feedback. But what were to happen if the feedback system you are using went kaput and consumers can't submit their feedback they have a general complaint about. You would not see any feedback in the feedback section, whereas you saw a good number of feedback emerging every day. This is an example of an anomaly right there.
The anomaly detection would be able to cross-analyze the fact that there was too much feedback emerging on a constant basis in the past, and now there isn't even single feedback whatsoever? It means that it is an anomaly and something needs to be done in response to it so that you can take some action.
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