We've been tracking the elections with our own business incident detection solution for the past several weeks, and it's been so interesting to see the day to day elements of the campaigns such as debates or key news stories affect certain metrics. As I explained in this previous post, we have been using the word "donate" together with the candidates' last name to measure support of the candidates, analyzing time series metrics from Google Trends. Besides "donate," we've followed key terms such as "women," "taxes, and "Russia." If we look at the three presidential debates together, it is easy to see that there was an increase in donation searches for both candidates immediately following the debates. Interestingly, however, Trump's donation interest was significantly lower overall than Clinton's, and reached its biggest peak after the second debate. The bump after the third debate was fairly small for Trump, whereas Clinton's hit the 100 mark. What did jump sky high, however, for Trump, was the word "Russia", much more so than Clinton who also saw an increase. Makes sense after Russia featured so prominently in the third debate. I daresay the data may indicate that support is waning for Trump - either that or his supporters are running out of money. Tying it back to the business world...these graphs demonstrate why it's so important to correlate events with data metrics, since an anomaly (e.g. the jump in online searches for the candidate's name & the word donation) is often correlated to an event. In this case the events were the debates, however other types of events more related to our customers' world include holidays, extreme weather, marketing campaigns, and more.    

Jason Bloomberg – a leading industry analyst and globally recognized expert on agile digital transformation – takes a closer look at how real-time anomaly detection is a game changer for digital technology companies.

As a leading adtech company, Netseer provides publishers with targeted ads, and bidding on advertising exchanges. Anodot provides real-time business insights to quickly identify performance issues.

With millions of users across dozens of professional publications, PMC’s data science team uses Anodot to track business incidents and identify software bugs early, before small issues turn into major crises.

With two debates behind us, the first presidential debate and the vice presidential debate, public discussion has swirled around the question of who won, and what impact did it have? Since these are not the rigorously judged debates of the high school/college debate circuit, winners may be determined by pundits, by polls, or even by the candidates themselves. We decided to take a slightly different approach. Since we're data fanatics, we have been tracking the candidates and election issues for quite some time, feeding the results of Google Trends searches of various keywords into -- of course -- our own business incident detection system, to see if and when anomalies occur in the data. Web Searches for "Donate" = Support for Candidate Early on, we decided we needed a way to measure support for the main candidates, Hillary Clinton and Donald Trump. We determined that the word "donate" together with each candidate's last name was a decent proxy for this. In other words, if users increased the number of times they searched for "clinton donate" or "trump donate," this indicated increasing support for the candidate in question.  (BTW we checked if it made any difference if we used the candidates' full names or just the first name, and we checked various forms of the word "donate", and the most representative results came from using the last name together with the word donate.) We also tracked certain hot button issues, like abortion and taxes, to see if there were any correlations between changes in the search data for these issues and support for the candidates. You can see some of the results of our data tracking in the screenshot below. Data Reveals: Increased Support for Clinton Post Debates, and After NYTimes Tax Article Searches for both candidates + the word "donate" increased immediately after both debates, however the increase in Hillary Clinton's "donate" searches surged upward more significantly than Donald Trump's in both cases, indicating that she generated a groundswell of public support after the event. Clinton had another anomalous data event with regard to people searching to donate right after the New York Times broke the story about Trump's tax returns, indicating that he may not have paid taxes for 18 years. The story went live on the publication's web site on Saturday night, Oct. 1, and then in the Sunday print edition October 2, and there is a clear jump in Clinton's "donate" searches on the 2nd, which correlates to searches for "Trump and taxes." The interest in "Trump + taxes" tapered off rather quickly -- in a matter of days -- and by the time of the VP debate it had declined significantly. Sometimes All You Need is a 3am Tweet Storm Interestingly, Trump's donation searches jumped soon after his 3am tweet storm, on Sept. 30, to a level nearly as high as his post-debate peak in donation interest. Trump has another few peaks, on Oct. 1 and 4 (before the VP debate) for which we were not able to find any rhyme or reason - we welcome your ideas in the comments. Will the 2016 election be decided between the Twitterverse, staged TV events and the New York Times? By tracking time series data it's possible to to see trends, identify anomalies and correlate between the various pre-election events and the mood of the electorate. - featured image credit: CNN

With massive amounts of business-critical data and metrics, Eyeview recognized the need to improve its alerting method. Anodot AI-powered analytics crunches Eyeview’s data streams in real-time, providing accurate alerts on critical issues.

We've been working so hard and are so excited to be able to share that we have raised $8 million in funding led by Aleph Venture Capital with participation by Disruptive Technologies L.P.. This brings our total funding to $12.5 million, and we couldn't be happier. These funds will go straight towards our efforts to serve our customers better. We will expand our global sales and operations and continue to focus on our commitment to innovation. Aleph is a $150 million early stage fund which partners with Israeli entrepreneurs looking to build companies that are scalable, global change agents. Since its founding three years ago, Aleph has quickly become one of Israel's leading venture funds and is a well-recognized brand in Silicon Valley as well. We are delighted to be joining the ranks of their other investments including WeWork, Windward, Nexar, Lemonade, Colu, Freightos, and Honeybook. Disruptive is an early stage fund that has invested in 14 companies to date. They have pioneered the industry’s post-seed investment phenomena, investing in early stage companies from seed to Round A.  To find out more about this latest investment, check out the full press release here. And to hear Eden Shochat's take, read his blog post here.

Wix needed a real-time alert system that would indicate issues without manual threshold settings in the key metrics. Anodot's AI-powered analytics provides the necessary insights to the company’s analysts.

The Anodot elves have been busy in the workshop, building new features to improve Anodot results and user experience. Over the last few weeks, we have rolled out the following new features: Events We introduced an "Events" feature to help you create a timeline of milestones and events including deployment, holidays and occasional alerts that may affect the behavior of monitored metrics. Each event or set of events includes a time and meta-data description and is plotted as a single vertical line on a chart. This feature can be useful as we get closer to the holiday season. Read more about it in our documentation. Event and Alerts Correlation You can now easily correlate between events and alerts by going to your alert and defining the types of events you want to see with the alert. Read more about it in our documentation. Simplified Metric Search You can now use simple search for metrics based on a limited number of predetermined parameters. Read more about it in our documentation.   We have also made some improvements to existing features including: Dashboards Customize y-axis values range Set graph title as a hyperlink to external resource New analytics functions "Exponential Moving Average" to create a dashboard and alerts with an exponential moving average function "Moving Median" to create a dashboard with moving median function API Improved API error codes and error descriptions New API for posting a metrics in JSON format (AKA Anodot metric 2.0) UX Increased dropdown size in metric composer Display dashboard name in full screen Display alert name in alert simulation Do you have ideas or suggestions for features or improvements? We would love to hear from you. Let us know at [email protected].