Business KPI dashboards don’t provide actionable insights now Business data analysts are in dashboard hell right now. They have to interpret data from so many different sources and then try to figure out what is the best action to take, with the most important decisions being made based on this information.  Despite having “Key Performance Indicators (KPI) Dashboards” for business, they struggle to get an integrated view of all business metrics. With greater volumes of data being collected, data analysts can’t keep up with the pace. Companies are making tremendous investments in dashboard and reporting technologies, like Business KPI dashboards, to keep better tabs on their operations. You’ve probably seen business dashboards with multiple KPIs that show sales figures, customer satisfaction score, churn etc. While designed to provide high level visibility of different business metrics, nevertheless data analysts simply can’t keep up with the demand to crunch all the data and then extract answers from their business KPI dashboards. Business KPI dashboards provide too much information and too little insight There are many reasons why business KPI dashboards can’t be relied on to provide you with actionable insights in real time. While an analyst can analyze data based on one or two dimensions (for example, device and geo), when an issue happens, they then have to manually add more KPI dimensions, spending a lot of time to find the source of an issue. Standard business KPI dashboards fall short when it comes to usability, specifically the accessibility of the important signals in the data.  Unable to automatically highlight what’s important, dashboards require analysts to process the data manually and iteratively, and still lack the ability to instantly drill down into granular metric-level data.  Traditional analytics and BI solutions, like business KPI dashboards, deal with historical data, not this minute, not showing a real-time status. Due to their limitations, business KPI dashboards typically look at only a subset of all the available data. These limitations yield at best delayed and at worst incomplete results. Business KPI dashboards simply cannot provide intelligent correlation The usual business KPI dashboards can’t understand a KPI in the context of the many, complicated, direct and indirect relationships between all your metrics. Data complexity, data-type growth, and data volumes threaten to overwhelm the interface, weakening the dashboard’s consumability.  Locating information takes profound familiarity of each dashboard metric, adequately see events and an elephant’s memory as you to try to visually correlate the data from across the organization, trying to figure out what to do next. When a BI team has trouble keeping up, they often get unpleasant surprises, discovering issues and opportunities long after the financial or reputation damage is done. Despite configuring multiple business KPI dashboards, they can still face many data blind spots. With data constantly changing, looking for answers on business KPI dashboards is a struggle, as something else could come in from left field, totally off the radar, affecting the results. AI analytics identifies trends and provides correlations in real-time Instead of long investigations and analysis through multiple business KPI dashboards and making manual correlations, business analysts can rely on AI analytics to probe deeper into the data and correlate simultaneous anomalies, revealing critical insights into operations. A real-time, large-scale automated anomaly detection system using machine learning methods can free data analysts from constant manual monitoring around just a few KPIs. When working with thousands or millions of metrics, you can’t just hire a staff of thousands of analysts to analyze your data for key decisions. Using automated significance ranking of detected anomalies, data analysts can focus in on the most important business incidents. This level of automation can provide actionable insights in real-time. Insights built upon deep learning, especially in complex, fast moving digital industries like ad tech, add another level of protection to company revenue. Both Microsoft and Google rely on advances in deep learning to increase their revenue from serving ads. Advanced machine-learning analytics allow ad tech companies to identify trends and correlations in real time, like instantly correlating a drop in a customer’s bidding activity to server latency. An automated AI analytics solution maps out the relationships between all your metrics, even if they number in the millions. Turning on all of the lights in the room, instead of using just a flashlight, allows companies to correlate all relevant data, not just the typical collected information presented in business KPI dashboards. By taking in the full picture, you can make better decisions to impact business success and improve customer satisfaction. And that’s where AI analytics really shines. Unlike traditional BI tools, by detecting the business incidents that matter and identifying why they happen, AI analytics lets you remedy urgent problems faster and capture opportunities sooner. Our automated AI analytics solution uses the latest breakthroughs from machine learning and data science to give our customers actionable insights in real time, something they were not able to achieve from their dashboard based business intelligence, bringing valuable business opportunities and insights to the surface.  

How does eToro stay on top with so much changing and vital data to be aware of? We visited their headquarters and met with Elad Gotfrid, eToro’s Director of IT, who shared their approach for handling the company’s data.

Learn how leading eCommerce and retailers are leveraging the power of machine learning - identifying problems and leveraging business opportunities faster.

GetYourGuide's engineering team leverages anomaly detection across the business, from monitoring revenue to watching for brand hijacking and affiliate fraud. Anodot alerts bring attention to the underlying issues much more quickly than any other method, which is helping prevent financial loss and keeping their global business running smoothly.

KPIs are the Cliff’s Notes of business metrics: a handpicked selection of the many measurable quantities which a business can - or does - collect. They can provide vital clues as to how well the business is doing as a whole, for instance which marketing initiatives are and aren’t working, and thus they provide feedback and can guide decisions on how resources are expended, for instance discontinuing a promotional offer that’s not driving any new sales. KPIs are considered “key” often because managers deem them directly related to profit, which is why metrics such as year over year same-store sales are such a common KPI. In order to tell a clear and convincing story, a given KPI needs to be transformed by analysis and visualization from raw data into business insight, and the correct way to do so isn’t always obvious. This is why traditional KPI analysis requires human data science knowledge and is very iterative, two aspects which prevent discovering actionable insights in real time. Traditional KPI Analysis In order to understand how Anodot’s full-stack solution makes dashboard-based KPI analysis tools obsolete, it’s helpful to review the typical KPI workflow which relies on those tools. First, the underlying data for a KPI needs to be pulled in from whatever files or databases they live in. Then, that data needs to be structured or formatted in a way that makes analysis smooth. This step is called “data cleaning” and is often required if the files containing the data for the KPI have rows that serve as table titles or headers, or if data from two sources needs to be combined before analysis. Finally, the data is imported into the dashboarding tool, where it is analyzed and visualized. Usually, the analysis and visualization isn’t a single step, but rather multiple iterations of reviewing, giving feedback and implementing changes. This is due to the fact that a whole team of analysts and data scientists often work on a single KPI, and the multiple iterations are needed before everyone on the team is confident that the visualization of the analyzed data will meet the requirements of management. Although there are obvious benefits to this collaborative approach, it is very time-consuming and takes data scientists and analysts - of which companies have a limited supply - off of other tasks. Furthermore, even after a KPI visualization is created and added to a dashboard, it may be modified or even replaced by another one when it is reevaluated as time goes on and business goals are (or aren’t) met. Once created, the dashboard visualization of the KPI is then monitored either manually or semi-manually via the use of static thresholds and alerts. Drawbacks to traditional KPI analysis, tracking & monitoring We’ve already mentioned the problems of tying up limited data science and analyst personnel as well as the fact that KPI analysis via dashboards is far too time-intensive to alert you to business incidents in real-time, but there are other drawbacks to this approach as well. Since KPIs tend to be aggregated metrics (like averages or totals), they can hide isolated yet significant problems or opportunities, as we've seen in temporary ecommerce glitches, such as listing a top-brand headset as free, rather than at its normal price, even for just a number of hours. Also, this KPI analysis workflow can very easily miss important signals in your data if you choose the wrong KPIs for monitoring to begin with. Lastly, the low number and aggregate nature of KPIs results in data that isn’t very granular. Granularity, as discussed in our previous post, is required to actually understand and fix the specific problems discovered on the dashboard. Monitoring your KPIs with Anodot: a comprehensive tracking solution for all your metrics As we mentioned above, Anodot provides a “full-stack” real time AI analytics solution. Not only can it accurately detect anomalies in all your metrics in real-time - eliminating the need to select a handful of KPIs - but Anodot’s correlation of related anomalies gives your analysts the benefits of granularity (specific actionable insights) with the conciseness you’d expect from a traditional dashboard tool. By giving you built-in data science, the majority of manual KPI analysis can be replaced by intelligently correlated and collated anomalies which point directly at business incidents. Anodot is scalable to millions of metrics, far beyond the manual, iterative approach that relies on traditional BI dashboard tools. That scalability enables Anodot to track all your metrics, thus giving you the granularity you need to find the important signals in all of your data, not just the specific KPIs you think you should be looking at. The more pixels, the sharper the image, and the key advantage of AI analytics is the ability to learn both what’s important and what’s related, not only showing you what’s there in the image, but also what it means.

The Production Services team at leading fintech Payoneer uses Anodot Autonomous Detection integrated with the Coralogix Log Analysis Platform to accurately identify system issues with real-time alerts and to accelerate remediation time.

Recently Gartner released their latest Hype Cycle report for Data Science and Machine Learning, advising Data and analytics leaders to use this report to better understand the data science and machine learning landscape and evaluate their current capabilities and technology adoption prospects. The report states that "to succeed with data and analytics initiatives, enterprises must develop a holistic view of critical technology capabilities." One of the technologies noted that is 'On the Rise' side of the curve is Advanced Anomaly Detection. Advanced Anomaly Detection is Accelerating Expecting Advanced Anomaly Detection to accelerate to mainstream over the next ten years, the Gartner report explained that "Anomaly detection is relevant in situations where the existence, nature and extent of disruptions cannot be predicted. Systems that use advanced anomaly detection are more effective than those that use simpler techniques. They can detect subtle anomalies that might otherwise escape notice, provide earlier warning of impending problems or more time to capitalize on emerging opportunities, reduce the human effort required to develop a monitoring or measuring application, and reduce the time to solution for implementing complicated anomaly detection systems." In the report, Gartner senior analyst Peter Krensky and Gartner VP and distinguished analyst W. Roy Schulte provide their insight for how Advanced Anomaly Detection will impact companies, and the benefits to be gained. They say that “virtually every company has some aspects of operations for which it is important to distinguish between routine conditions and matters that require extra attention. Much of advanced anomaly detection is a competitive advantage today, but we expect most of the current technology driving it will be widespread and even taken for granted within 10 years because of its broad applicability and benefits. Advanced anomaly detection is already being embedded in many enterprise applications.” Forces Driving the Need for Advanced Anomaly Detection We find the main drivers that are creating the need for Advanced Anomaly Detection solutions come as businesses are having to manage increasing amounts of data and tracking more metrics. An unavoidable consequence of the scale and speed of today’s business, especially when lines of code sets are changed in seconds, are costly glitches. Moreover, as many processes happen simultaneously, many businesses monitor activities by a different person or team. Anomalies in one area often affect performance in other areas, but it is difficult for the association to be made when departments operate independently of one another. With businesses constantly generating new data, they need a solution that can analyze metrics on a holistic level and give people the insights they need to know what is going on. Applying Advanced Anomaly Detection Looking at how Advanced Anomaly Detection has already been applied, the analysts note that "its use is increasing in applications as diverse as enterprise security, unified monitoring and log analysis, application and network performance monitoring, business activity monitoring (including business process monitoring), Internet of Things predictive equipment maintenance, supply chain management, and corporate performance." High Business Impact of Advanced Anomaly Detection Advanced Anomaly Detection received a maturity level of ‘Emerging’, meaning that it is expected to grow and evolve. The strong momentum that Advanced Anomaly Detection has gained has drawn the attention of innovative vendors. The report lists some sample vendors who offer Advanced Anomaly Detection solutions, such as Anodot. Our Take on the Gartner Hype Cycle on Data Science and Machine Learning Each year, Gartner produces Hype Cycle reports to provide a graphic representation of the maturity and adoption of technologies and applications, and how they are potentially relevant to solving real business problems and exploit new opportunities. In our case, the challenge with big data is how to find the right insight, or really what are the right business incidents in the data. While Gartner outlines a clear need and drive for Advanced Anomaly Detection for data driven businesses, we believe that mainstream adoption will appear faster than projected in the report, because businesses that implement it will enjoy clear competitive advantage, driving competitors in their segment to move quickly to add Advanced Anomaly Detection capabilities. There are many systems that try to find events and data that is not normal. Yet, often these systems fail, identifying too many anomalies (false positives) or not enough (false negatives). Applying machine learning to many rapidly changing environments means constantly updating and retraining models, to prevent false positives. Our AI analytics solution offers autonomous analytics. Instead of having to ask the many questions and carry out complex data analysis, Anodot does the work, and provides the answers to understand why an incident happened. Next Step Click here to download our white paper that helps you explore and weigh the issues around the Build or Buy Dilemma for Advanced Anomaly Detection. All statements in this report attributable to Gartner represent Anodot’s interpretation of data, research opinion or viewpoints published as part of a syndicated subscription service by Gartner, Inc., and have not been reviewed by Gartner. Each Gartner publication speaks as of its original publication date (and not as of the date of this report). The opinions expressed in Gartner publications are not representations of fact, and are subject to change without notice. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Gartner, Hype Cycle for Data Science and Machine Learning, 2017, 28 July 2017

"Anodot is a game-changer. It connects the dots all together quickly, in a single email. We've reduced the number of false positives to almost zero."

In this first post of our new series on overlooked incidents and AI analytics, we’ll discuss how intelligent monitoring of your metrics can open the door when opportunity knocks…before it walks away. “The main thing is to keep the main thing the main thing” - Stephen Covey Or at least that’s the intent when starting off. While it’s technically true that revenue and cost are only two key performance indicators (KPIs), it’s also true that both of those metrics are in fact aggregates - or more accurately, summaries. At such a high level of aggregation, there’s zero actionable insights because your team can’t directly alter either of those two metrics, but rather the myriad of little decisions which can tune the performance of the complex engine which is your business. Without access to much more granular data, you’re forced to make specific actions with incredibly vague information. The solution, of course, is to monitor a much higher number of much more specific metrics. This approach, however, can turn into an embarrassment of riches if your monitoring solution can’t keep up all the signals that will now need monitoring. Metrics monitoring can easily become an overwhelming task There’s an example we like to use which really drives home this problem of scale: Let’s say you’re an analyst at an ecommerce company and at the moment you’re monitoring only two KPIs - the number of purchases, and revenue. You’re a smaller e-tailer, with only 50 products total spread out over 10 categories, and you’re focused exclusively on the United States and want state-level data. As a company that does business on the web, you’ve already been bitten by platform and version-specific problems, and thus you want statistics from eight operating systems (four major Windows versions, 2 desktop Mac OSs, and one version each for both iOS and Android) to help you identify and fix future problems much faster. So how many total metrics will you be monitoring? The answer is: 2 X 50 X 10 X 50 X 8 = 400,000 As you get more granular, the number of permutations increases rapidly; in the off chance that Puerto Rico becomes the 51st state - that number will increase by 8,000. 400,000 metrics is far too many for manual metrics monitoring via traditional BI dashboards, alerts and teams of data scientists and analysts to be practical. Fortunately, AI-powered automated machine learning solutions are able to take human eyeballs off the dashboards because these real-time analytics solutions are able to accurately and automatically detect the anomalies in all that time series data. Those anomalies are the real signals in your data upon which you need to act. Accurate and real-time anomaly detection, coupled with the ability to correlate related anomalies across multiple data sources, is ushering in a new time in business intelligence when no data-driven organization should be surprised by an unexpected business incident ever again. The anomalies in the data which point to the opportunities in the market can now be found. Think of it this way: if you monitor everything, you can detect anything, especially events you didn’t know you had to look for. This is the real power of AI analytics. Absolutely no metric is overlooked Perhaps for a better perspective, let’s discuss a real-life example of how machine learning-based anomaly detection can help a business gain actionable business insights. When a celebrity endorses a product on Instagram, the free positive buzz can really drive up sales, but only if the reaction is in time. A large apparel conglomerate learned that the hard way when their BI team discovered the endorsement…two days later. If they discovered the sharp uptick in sales for that product and the rapidly dwindling inventory of that product in one of their regional warehouses in real time, they could have capitalized on the opportunity by increasing the price or replenishing the inventory to keep the customer demand fed. Now, that same Fortune 500 heavyweight is an Anodot customer and hot opportunities like that don’t slip by anymore. According to the Data Analytics Director, “With Anodot, we get real time alerts for sales spikes…or when an impending snow storm causes a decline in in-store purchases in the Midwest.” Switching to much more effective metrics monitoring has obviously added to their bottom line. When you have a data scientist in the cloud, any user can easily and automatically gain actionable business insights. Whether it’s a celebrity endorsement or signs of premature equipment failure from a swarm of IoT sensor devices, AI analytics tools are data agnostic and can find the signal hidden in your time series data. With these tools, you can extract not only actionable insights, but ultimately increased revenue from the business incidents discovered in your data. And isn’t that supposed to be the main thing?   Read our next post about "KPI Analysis with AI Anomaly Detection"