Should data analysts and other "knowledge sector" employees feel threatened by AI? Here's what industry experts have to say on the subject.

Read the case study to see how Uprise used Anodot to avoid major problems such as server outages.

Whether you’re in it for the game or the commercials, the excitement around the Super Bowl draws people of all ages, all around the world to their screens - many different screens. All that excitement produces a mountain of data. Just as there are few subjects in our world where stats and data are discussed any more than in sports, one of the main sporting events to talk about is  the Super Bowl. When the Eagles and Patriots squared off on Sunday in the most-watched sporting event of the year, nearly 188.5 million Americans will be tuned in, according to a survey by the National Retail Federation – about the same as last year. Collecting Reams of Data On and Off the Field While data has been collected on and around football games since the very beginning of the game, today’s collection techniques are smarter, real time data is the new standard. With new sensors around the stadium and even on a players' pads and helmet collecting real-time data about what is happening on the field, for brands, Super Bowl Sunday means collecting data across many platforms, including  through social media listening tools. The real data driven aspect of the Super Bowl comes from the advertisers. These companies try to get a picture of what people talk about most before, during, and after the game. Advertisers are paying over $5 million on average for a 30-second shot at those eyeballs, says research group Kantar Media. That’s just one side of the game, the second screen has also become a powerful opportunity to reach a highly targeted audiences, whether it’s a big-name brand looking to optimize their high-profile TV ad or a smaller brand simply looking to enter the fray. According to Rubicon Project’s survey of “more than 800 self-described “die-hard football” fans, more than a quarter of NFL fans regularly engage in second-screen content while watching their favorite team on television.” Data-driven Companies Need Real-time Business Insights Today’s environment allows very little margin for error. With Super Bowl ads costing millions of dollars, and their influence able to spur online shopping spikes, lack of detailed awareness of possible glitches can lead to larger losses, both monetarily and reputationally. It’s impossible to manually track the millions of metrics that are generated across today’s digital businesses. Static thresholds for seasonal data are becoming either meaningless or generate overwhelming alert-storms. Dashboards can’t keep up with these sudden spikes, where the data ends up being yesterday’s news. Analytics Makes Sense of the Data It’s in these areas that analytics comes into play. Just as on the field, the data may indicate the likelihood of the success of a play, data can’t reveal all the variables alone, like what a coach may know about a certain opposing player or Tom Brady’s coolness under pressure. While the  Patriots may have believed that the Eagles would run the ball wide, their coach could have decided to run the ball inside. Businesses can draw from this as well when it comes to data analytics. The data can point to  particular anomaly, but the analytics needs to put all the pieces together and make that data into data-driven decisions for the best action to take. Data analysts can’t rely upon what happened in the past will occur again in the future.  Today’s organizations need to leverage data to their advantage – to win a game, to drive revenues, to get better performance results, etc. This data is not just about what is being watched on millions of screens during the game, but what happens daily for piles of streaming data across many different industries. AI Analytics Spots Business Opportunities Before, during or even after the big game, the data gathered about browsing history and purchases, help advertisers to identify who is interested in what and what is likely to have the biggest impact on them. Hence, by applying AI analytics tools, companies can keep their finger on top of developments in advertising campaigns to drive more engagement, allowing not just their Super Bowl ad to have a better return on investment, but their ongoing business operations. The Super Bowl is an exciting game that tens of millions of people around the world will enjoy, and big data is changing the game. Whether it be in terms of trying to predict the most likely winner of the game or how advertising is handled. AI-powered analytics can ensure that advertising platforms, retail customers’ sites and online businesses interact with consumers perfectly and consistently across platforms. Effectively tracking exact customer activity and delivering concise data that is dependable, AI analytics allows you to ensure that  your customers have a best-in-class experience.

In this session at White Hall's BDA conference, AppNexus VP of Engineering Travis Johnson discusses how the company uses Anodot’s AI Analytics for insights about unknown unknowns, to confidently and proactively address incidents quickly.

In business, few surprises are good. When you learn that, for example, a business process error has been preventing customers from completing their orders for over a week, you probably won’t be happy. You’ve just tripped over an “unknown unknown - the ones we don’t know we don’t know.” The term, Unknown Unknowns, attributed to Donald Rumsfeld, refers to those situations where you don’t know you have a problem, so you don’t know that you need to apply resources to solve it. What is an Unknown Unknown in Business? The problem here isn’t that business processes fail – that may still happen. Rather, problems occur when you not only can’t see the failure, you don’t see the shadow in the data that the failure creates. In the example above, the shadow would come in the form of a decline in sales, and you might not see it because your BI platform mis-represents the data, making you feel like there is nothing to worry about. The error and subsequent sales drop are unknown unknowns. Its converse, a known unknown, might take the form of, ‘we know we’re seeing an unusual drop in sales, but we don’t know what’s causing it yet.’ In business intelligence, unknown unknowns are an unfortunate fact of doing business, especially in a world scaling up and dealing with ever large datasets. The problem is you don't know what you don't know, until it's too late. So how do you identify unknown unknowns in a timely way and avoid complex, revenue-losing problems that can persist right under your nose? Think of the Unknown Unknown Problem as an Opportunity In our example, the effect of a business process error was masked by seasonality. Seasonality is a data trend which occurs naturally based on customer data. Seasonality refers to the presence of cyclical patterns in time series data. Seasonal patterns are changes we expect; part of the normal behavior of a given metric and thus must be included in the model of that metric. For some metrics, however, there are no seasonal patterns. And sometimes, multiple seasonal patterns are present in a time series. Since this can be mis-identified as outliers that might deserve attention, seasonal variability must be identified, filtered out and ignored. Finding unknown unknowns in your data might sound like you are flying blind. Even though these are unknown unknowns, very often, your data will hold clues that can point you to the unseen drivers that are impacting your business. For example, as online commerce has grown exponentially, so has its complexity. Glitches have scaled down in size, but they’ve grown in number, sophistication, and—especially—difficulty to identify. The fact is that multiple micro-sized glitches may cut as deeply, if not more so, than the headline-grabbing failures we often hear about. Where we deal with such huge populations of statistical data, and where it becomes nearly impossible to take out a representative sample, no matter what the sampling technique is, big data has tremendous value to give us a sense of the bigger story that the data can tell us. Revenue-losing problems can be discovered early to preserve the revenue stream that would otherwise be lost. In other words, unknown unknowns aren’t necessarily a problem – they’re an opportunity to discover revenue that may otherwise be lost. Uncovering Unknown Unknowns in Business Take, AppNexus, a company that experienced unknown unknowns first hand. Their account management team might not see an issue or it would take then a couple of days to surface, like run a report and see that something changed within the business. Then they would start trying to figure out which metric had changed or which site had stopped showing ads. Handling automated online ad purchasing and processing about 10 billion transactions every day,  with each transaction taking just milliseconds, sometimes things were missed. AppNexus’ VP of Engineering, Travis Johnson, explained, "We wanted to be able to reach out to our clients to work with them to resolve these issues faster. Every minute counts, every minute can be a missed impression. However, sifting through 10 billion daily transactions to try and find these signals was a difficult problem for us. We didn't have the tools to do this. We weren't a team of data scientists. So, we needed to know really where to look and come up with a way to solve this problem." They found that with over 40 metric attributes per transaction, there was no realistic way for human analysts to keep track of the data. “We had too much data that we wanted to watch. We weren't able to just set up a simple dashboard and give it to our account management team and have them just say hey, watch these, watch these five metrics for your client and understand their health.” Shining a Light on Unknown Unknowns with AI-Powered Analytics AppNexus changed the story by applying machine learning to analyze their time series metrics and detect anomalies in real time. Speed was the issue, so that they could know when things were going wrong and reach out to their clients. Enabled with real-time analytics and accurate alerts, AppNexus was able to be aware of errors almost immediately as they happened, without having to rely on skilled data scientists to perform effort-intensive sleuthing. Instead of having to wade through dashboards to find unknown unknown revenue leaking issues, AI-powered analytics could point them out right way. For instance, where a client had an ad running and an anomaly showed that the ad had all of a sudden dropped to just a trickle of clicks coming in, from the expected pattern of clicks. There could be a lot of reasons why that happens. AI-powered analytics shows all the related metrics that are also warning, giving better insight. Data analysis is great for finding many things you want to know, but another big advantage is that it can discover what you perhaps don’t want to know – the glitches and the anomalies -- the things that just aren’t right and are absolutely crucial to discover and mitigate in near real time. Machine learning analytics disrupts and replaces traditional BI tools, turning the unknown unknowns turn into valuable solvable issues.

Learn how Anodot helped Eyeview's engineering team and executives achieve access to the system’s precise status at any given moment and to identify abnormal behaviors as quickly as possible.

In this session at O’Reilly's Artificial Intelligence Conference, Dr. Ira Cohen, Anodot's Chief Data Scientist and co-founder, presents how Anodot devised a way to intelligently monitor the performance of highly complex, unsupervised machine learning models with a “learning the learner” approach.

Credit Karma shares the benefits of using Anodot to easily track data, get real-time alerts and altogether promote a fair and healthy business monitoring.

Big data brings endless opportunities for the travel industry, but this ever-changing field also brings with it many challenges. For direct booking travel businesses and travel aggregators, microscopic pricing and performance advantages will determine each sale in today’s competitive market. The key is the data. Here are three tips to help online travel booking companies as well as travel aggregators steer clear of glitches and dirty data, and raise the volume of sales. There’s no need to explain the Online Travel Agency (OTA) business model, how it has revolutionized the travel industry, or how these companies (and later their offshoot travel metasearch engines) have utterly dominated online travel for nearly two decades. But recently, we’ve seen a tectonic shift in online travel. The travel industry like many others have amassed an inordinate amount of data on their consumers, flights, hotels, experiences, loyalty programs, complaints etc. With customers creating so much valuable data at every stage of their journey, how can travel companies do more to collect and connect these data points to improve the customer experience? Airlines, hotels, travel agencies, aggregators and others in the travel business need to do what they do best – provide outstanding value and respond to rapidly-changing markets. To do this, they need to ensure that the data they’re getting from all parties is clean, that each of the APIs that nourish their business are glitch-free, and that they can respond agilely yet accurately. The thing is, when you’re running a travel business that is processing nearly 50,000 events per second and drawing on data from thousands of parallel services – it’s tough to tell if something goes wrong! Dirty Data + Glitches = Lost Business In general, research has shown that 40% of anticipated business value is lost owing to poor data quality. Gartner estimates that dirty data results in losses approaching $10 million a year for the average company. Online travel businesses operate in a space where handling an extremely high volume of data from diverse and disparate sources at tremendous velocity is all in a day’s work. This sheer scale of data magnifies the impact of glitches and dirty data –  potentially cutting deep into the bottom line. It’s also what makes it nearly impossible for data analytics teams to identify and rectify revenue-siphoning problems in real time. For example, a sudden drop in sales of travel packages during the pre-holiday rush period could be the result of a simple pricing error, a negative trend on social media, a server glitch causing slow load time, a coding error that makes checkout impossible, a handshake problem with one of the hundreds of travel provider APIs, an error in the payment gateway, an error affecting just one type of browser over one operating system...the list goes on. The Road to Regaining Sales More than ever, travel businesses and aggregators need to overcome the challenge of spotting dirty data and glitches before they hurt the bottom line, and maximizing the value derived from big data as a whole. This is a great first step on the road to regaining sales volume lost to direct travel providers. To start down this road, make sure that you’re: Watching customers to identify problems faster – Sound intuitive? It should be. But not all online businesses can yet track in aggregate exactly what customers are doing on the site or in their apps. This is data that tells a story. It can show you pages, page elements, or products that show sudden drops or spikes in traffic. By way of example, a sudden runup in sales offset by a drop-off in revenues for a given vacation destination could indicate page-level mis-pricing. A drop-off in sales for customers using a particular Android version might mean a version-specific glitch in your app. Closely monitoring secondary data sources – For travel businesses, nearly all data is external data. Yet beyond this mission-critical core data from hotels, airlines, car rental agencies, and others - there’s a world of relevant secondary-source data to monitor. Need examples? How about competitor advertising bid data, weather data (which can cause revenue-impacting power outages or discourage travel to certain destinations), fraud detection and security data, and more. This is data that can quickly and dramatically affect travel plans, and you need be able to analyze trends in real time to understand how to best respond to findings from it. Listening to social media buzz – Social media monitoring is not a vanity metric. Used correctly, this data has real value to sales and marketing operations. Working in real time, correlating social media data with changes in demand for travel packages or destinations, can offer valuable insights. For example, if a celeb promotes a certain destination via social media, you need to quickly identify the actual business impact to your specific business. That way, you can more effectively leverage the momentum to tailor inventory to meet expected demand, tweak pricing to further drive demand, tactically bundle products to grow overall package profitability, and more. The Bottom Line Identifying dirty data, catching glitches, and leveraging data assets in the hyper-complex, multifaceted travel ecosystem is a task that stretches the limits of human capability. Travel is a fiercely competitive industry where speed is always of the essence, but minimal advantages can reap great rewards. The key to unlocking those rewards will be the use of reliable data, enhancing customer experiences. To meet the challenge, direct booking players and aggregators need to seek out and adopt technological solutions that have the speed, accuracy and agility that will make difference in their bottom line and impact future operations.