We recently took a closer look at data made publicly available by the Bay Area Bike Share to see if we could find some anomalies by streaming the available data into Anodot's business incident detection system. The company, based in the San Francisco, set up bike rental stations throughout the Bay Area. Users can sign up for a 24-hour, 3-day or annual membership, which grants access to the bikes. When examining the numbers, we found seasonality consistent with what was demonstrated in the 2014 Data Challenge. For example, in the graph below - Total Number of Rides - there are many more rides on weekdays and fewer on weekends and holidays. The constant numbers probably indicate that the bikes are being used by people that need to get from point A to point B on a regular basis...in other words, commuters. In the graph below - Ride Duration - weekday rides average 10 minutes, while weekend rides are nearly twice as long. This also is indicative of weekday work/weekend pleasure usage. We then looked at the number of rides for annual subscribers (green) versus other customers, also called non-subscribers (blue). The graph below shows that non-subscriber rides peak on the weekends. There are fewer non-subscriber rides on weekdays, and the opposite is true for subscribers. This makes sense if the subscribers are using the bikes to get to work, since they (hopefully!) aren't working on weekends. Non-subscribers could even be tourists. Additionally, the length of rides (in minutes) for subscribers (green) versus non-subscribers (blue) varied greatly. We see below just how drastically the length of rides varied. Subscribers ride for an average of seven minutes, while non-subscribers ride from 20-40 minutes. We also see that ride duration is much longer on the weekend. When we looked at the number and duration of rides together, the following graph shows the duration peaks are anticorrelated with the number of rides (and happen on the weekend). So, with the data as our basis, we can jump to some interesting conclusions: that Bay Area Bike Rider subscribers are residents who most likely use the bike share for commuting to and from work while non-subscribers (i.e. other customers) are tourists or visitors who don't ride much during the week but use the program for longer sightseeing and recreational rides on the weekends. From the shape of this data, it seems that Bay Area Bike Share is providing a crucial service that replaces other forms of commuting, such as car or bicycle ownership, or another form of public transportation. And as for the anomalies...of course there were major drops in number of riders during Thanksgiving and Christmas/New Years. More interestingly, you can see the anomaly below that we found an anomaly (a decrease) in ride duration that originated from the 2nd at South Park station. It happens immediately after a long period of duration increase. You can see in the graph that the length of time each person kept the bike increased over time, and then suddenly decreased. To understand this a bit more deeply, we compared rides that started at this station with rides that ended at it, and it's clear that the growth trend happened only with rides that started there, not those that ended there. Our initial guess was that there was some construction in the area that may have lengthened the riding time, but we researched it a bit and did not find anything relevant. The increasing duration was consistent to the two main stations that riders rode to from this station, in two completely different directions. Another possibility is that there was an increasing problem with subscribers unlocking the bikes that was then fixed. Units in the graphs above are seconds, so you can see that the major drop (which was maintained afterwards) was around three minutes. We also noticed a major anomaly in mid-December, when the duration spiked. We checked with Bay Area Bike Share and one of their employees, Ashley Turk, explained that this could have been due to outlier trips of greater than two hours during that time period.

In programmatic advertising, every minute translates into tens of thousands of dollars, and Anodot gives advertising technology companies the crucial insights you need in real time.

Like most web based businesses and SaaS companies, one of our go-to tools for a quick check of the usage of our service is Google Analytics. Typically we log in and we see the pleasing wave of application traffic, with the predictable seasonal drops during the weekend, and increases during the week. We get a daily report of usage per customer and look at the main flows in our service. Rapid Anomaly Detection Of course, Google Analytics is a great source of time series data, so we decided to stream the analytics about the Anodot application into the Anodot service, to see what our algorithms could discover. Sure enough, we quickly uncovered some interesting anomalies, such as this one, showing a major traffic drop at one of our customers for one of the pages in our app (one of about 50). When we checked a little further, we saw that a similar anomalous drop was happening to multiple clients, and it all happened at the same time. Challenging to Investigate the Issue in Google Analytics Interestingly, when we went back to Google Analytics to see what we had missed, we saw… not much. In fact, if you look at the Audience Overview graph (below) for the same period, you might notice a slight drop in sessions, but nothing major. Yet Anodot picked up and highlighted an anomaly in seconds. What gives? If we dig a bit deeper within Google Analytics, especially knowing exactly what time period, page and customer to look for, we do see that there was a major drop in total pages viewed during the period in question:   But even with Google Analytics Page Views graph, it can be challenging to detect the issue and isolate the root cause, or to understand which customers are most affected, and which pages, and what the heck is going on. Anodot Finds the “Slow Leak” Using Anodot it was very easy to do a quick root cause analysis. Turns out we had done a version upgrade right before the drop. The upgrade caused an issue in part of the app, but didn't break it, just reduced the page views. Of course if it had broken something major, we would have seen it right away (and been flooded with phone calls). But since it was a “slow leak,” we might not have noticed it for weeks. In Google Analytics, the issue was hard to spot and isolate, but with Anodot’s Business Incident Detection Platform, we immediately saw the drop, correlated it with the software upgrade, and quickly rolled out a fix. -- Start detecting anomalies in your Google Analytics data now.

Gain valuable insights from all of your data in real time, with Anodot time business incident detection and analytics.

We recently announced a version upgrade to the Anodot service that included a new feature: Anomaly Dashboards, aka Anoboards. Huh? I thought Anodot Always Had Dashboards Since the beginning of time (ok, since the solution was launched), Anodot offered dashboards, which is the central repository for accessing and viewing groups of metrics. Dashboards are tiles that you can use to track whatever metrics are most interesting to you. Tiles can be graphs, meters, even free text. For more information about working with dashboards, see the relevant help section. Anoboards are Dashboards for Anomalies Anoboards are similar to dashboards in that they are shortcuts to view information that you need quick access to. They let you create customized dashboards that show anomalies for a predefined set of metrics. An Anoboard is a new way for teams and individuals to create customized views of anomalies within a set of metrics that represents the parts of the service that are their responsibility to track. More info about how to set up and use Anoboards in our online help. Anoboard Uses Let’s say you are responsible for conversion on an ecommerce site, you might track conversion from each region you are responsible for on a dashboard. However to see where (or even on which server) conversion suddenly spiked or dipped, the anoboard will give you quick access to see exactly where it happened, so that you can investigate it quickly. Similarly, if you are a factory manager you might want to always view a dashboard of machine temperature for the thousands of machines you monitor. Perhaps you would set up an average of all the machines to display on the dashboard. But if you want to check to see which machine is suddenly heating up more than the others, you could see that most easily on an anoboard. Different Roles, Different Anoboards Most of our customers have lots of people using Anodot, each for his or her own incident detection needs. For the executive user, tracking revenues is most important, for the operations user product inventory or factory output, and for the technical user CPU and server errors. All of these and other roles can benefit from Anodot, and using anoboards and dashboards, each user or user group can create their own shortcuts to see exactly what interests them, to get the most out of the system.  

As a financial technology company, you deal with a vast number of critical data streams that behave in complex ways, such as transactions, payments, money transfers or loans. Performing with the highest standard of availability and reliability is a crucial asset to securing customer trust.

Learn immediately if any of your key indicators are under- or over-performing, including numbers of passengers to and from specific cities/regions, brand or display advertising click-through rates, flight delays due to technical issues, customer service communication counts, average resolution time of support calls, number of bookings or cancellations, weather per geographic region and holidays per country.

I'm excited to share this new customer case study in the ad tech industry. Eyeview is a New York-based video advertising technology company that provides brands with ROI on their video advertising spend. Their VideoIQ® platform provides best-in-class access to highly viewable and guaranteed fraud-free inventory across television, desktop, tablet and mobile. In the real-time bidding business, traffic is king. Eyeview deals with enormous amounts of traffic and they must make complex decisions with minimal latency. With so many indicators and data streams – a spectrum of around 200k metrics – the throughput is so high, they’ve got less than a second to analyze each query. Enter Anodot. Eyeview recognized its need for an anomaly detection platform to improve its alerting method. “For me, the main value of Anodot is being a single system monitoring, detecting, and alerting on everything that happens within Eyeview,” said Gal Barnea, Eyeview’s CTO. “Anodot adds another tier of protection to company revenue.” Read the full case study here to find out how we help Eyeview by delivering accurate and timely warnings, as well as providing rapid signals for positive anomalies of their most critical metric – the number of ads served. Next step? Eyeview's business team will start using Anodot to monitor revenue, delivery and financials. What features do you look for in revenue loss prevention tools? Read the case study. -- image source: http://www.higheredtechdecisions.com/

Linda Musthaler, Principal Analyst with Essential Solutions Corp. delves into the benefits of Anodot for companies that need real time business insights.