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SCALING A SMALL DATA TEAM WITH THE POWER OF MACHINE LEARNING

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SCALING A SMALL DATA TEAM WITH THE POWER OF MACHINE LEARNING

How DAZN uses AWS and Dataiku to make Big Data with a Small Team and allow their non-technical staff to perform advanced customer segmentation, content attribution, churn prediction, and more.

In an effort to continue to grow their business in existing and new markets, DAZN – a live and on-demand sports streaming service – wanted a fast, low-maintenance way to enable their small data team to run predictive analytics and machine learning projects at scale.

The company wanted to find a way to allow data analysts who were not necessarily technical or experienced in machine learning to be able to contribute in meaningful ways to impactful data projects. Ultimately, they wanted to support an underlying data culture with advanced analytics and machine learning at the heart of the business.

The Situation

Until recently, the sports entertainment industry was dominated by cable or satellite TV systems and companies; if a customer wanted to watch a particular sporting event, he had little or no choice in how to do so. Now that consumers are breaking free from traditional TV, they are increasingly turning to specialized services streaming exactly the content they’re looking for, whether live or on-demand. And while they are willing to pay for these services, it means that entertainment companies – in the absence of the aforementioned virtual monopoly of TV broadcasts – are held to increasingly higher standards when it comes to quality and offerings.

In other words, because customers can turn elsewhere, entertainment companies have had to up their game, so to speak.  Today, that means bringing innovation by way of predictive analytics and machine learning to optimize every aspect of the business, from marketing to customer service to product offerings. To do this efficiently, they must also bring this innovation at scale, hiring fewer people to do more such that insights grow exponentially along with the amount of data being collected.

The Need for Big Data with a Small Staff

DAZN knew that in order to accomplish their goals quickly, they would need technologies that were simple and in the cloud. They turned to Amazon Web Services (AWS) and Dataiku in combination for their simplicity in setup, connection, integration, and usability, and they got up and running in under one hour.

With AWS and Dataiku, the small data team built and now manages more than 30 models in parallel, all without needing to do any coding so that the processes are completely accessible to non-technical team members. They use these models as the basis for a variety of critical processes throughout all areas of the business, specifically:

  • Content attribution to determine what fixtures are driving sales, enabling contextual information on key fixtures in each market.
  • Advanced customer segmentation to identify user behaviors, particularly regarding content and devices on which customers use the product.
  • Propensity modeling to identify customers that are likely to churn, enabling improved customer targeting for retention activities.
  • Survival analysis to understand customer stickiness, enabling calculation of expected revenues to understand customer return on investment.
  • Natural language processing on social networks for market research

Results of More Effective Team Members = More Data Science

AWS and Dataiku have noticeably shifted the data culture at DAZN and have brought innovations in advanced analytics and machine learning into the spotlight throughout the company. Thanks to Dataiku’s ease, simplicity, and huge efficiency gains, DAZN has hired two data analysts who have already gotten up to speed and are doing as much work as five analysts in the pre-Dataiku team. In addition, the company has found that each data team member 2.5x more efficient in putting models in production.

Overall, the biggest impact has been empowering a non-technical team to create more models than ever before and get them into the production environment quickly to bring real ROI to the business. DAZN plans to continue to grow the team to three data scientists and 6-10 analysts to exponentially increase the number of machine learning models in production.

To learn more about how Dataiku helps companies across all industries visit:www.dataiku.com

Technology

Track and Trace and Other Lost Data

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Track and Trace and Other Lost Data 1

By Ian Smith, General Manager and Finance Director at Invu 

You, like me, were probably amazed by the now infamous loss of the over 16,000 positive test results in the track and trace system due to an Excel spreadsheet error.

You, like me, probably wondered how the Government could get something so important so wrong?

But perhaps we should aks are standing in a greenhouse launching stones?

Data risks from software

Today we are spoilt with software offerings that help us with both our personal and our work lives.

Microsoft Excel is a powerful application and offers many functions now that required moderately complex macro writing in the past, seducing all of us into submitting more data for it to analyse. In finance, we tend to solve all those problems our applications cannot address using Excel.

In finance, we also know the risks of formula errors, and if we have relied on it enough, we will have our own war stories to go with these risks. Yet, we often continue to use the tool for operations that make those folks with an information technology background shake their heads.

These Excel files nowadays may find themselves resident on a local file server or one of the many file servers in the cloud (like those from the big three, DropBox, Google Drive and Microsoft OneDrive or other less well-known file sharing applications). Many of us use these in multiple ways.

Vulnerable programmes

Beyond finance and Excel, there are now many applications that we run our data through and leave data stored in the form of documents, comments and notes.

The long-standing example is email. We today receive many documents via email, with content in the body often providing context. Email systems then become the store for that data. While this works from a personal point of view, for a business working at scale, the information stored this way can be lost to the rest of the business. Just like data falling off a spreadsheet when there are not enough rows to capture the results.

More recently, we have seen easy to consume applications develop in many areas like chat and productivity. Take for example task management apps, my own preference being Monday.com (I am sparing you the long list of these). The result of the task and how we got there, in the form of attachments or comments, are often stored in the application. Each application we touch encourages us to leave a bit of data behind in its store.

Data proliferation

Many of these applications can have a personal use and an initial personal dalliance is what sparks up the motivation to apply the application to a business purpose. Just like the “Track and Trace System”, they can often find themselves being used in an environment where the scale of the operation overwhelms their intended use.

In our business lives, combining the use of applications in this way by liberally sprinkling our data across multiple systems often stored in documents (be they Microsoft Word, email, scans or comments and notes) puts us on the pathway to trouble.

Imagine how Matt Hancock felt explaining to Parliament that the world-class track and trace system depended on a spreadsheet.

Can you imagine a similar situation in your business life? Say, for example, that documents or data in some form was lost because of the use of disparate systems and/or applications that were not really designed for the task you assigned to them.

Who would be your Parliament?

Now you can see yourself in the greenhouse, you may not want to reach for that metaphorical stone.

If these observations create some concerns for you, you may want to consider the information management strategy at your business. You have a strategy, even if it is not addressed specifically in documents, plans or thought processes.

Action plan

These steps may help figure out where you are and where you want to go.

  1. Assess your current environment.

Are you a centraliser, with all the information collected in one place? Or is all your data spread across multiple stores, as identified above? Are you storing your key business information on paper documents, or digitally or a mix of both.

  1. Assess your current processes.

Do your processes run on a limited number of software applications? Or do you enable staff to pick their own tools to get things done? The answer to this question is often a mix of both where staff bridge the gaps in those applications using tools like MS excel. A key application to think about is how the data in email, particularly the attachments, is made available to the business.

  1. Design a pathway for change and implement it.

Start with the end in mind. I suggest the goal is to enable the right people to have the right access to the information they require to do their job in real-time. I believe the way to effectively do this is to go digital. The fork in the road is then whether to centralise your information store or adopt a decentralised approach.

My own preferred route is to centralise using document management software that enables all your documents to be stored in one place. Applications like email can be integrated with it, significantly reducing the workload required to file and store the data. The data can then be used in business applications using workflows. Thinking these workflows through will help you assess the gaps between your key business applications and consider whether tools like excel are being stretched too far.

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NICE Unveils ENLIGHTEN Fraud Prevention Powered by AI and Voice Biometrics to Empower Contact Centers in Safeguarding Consumers

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NICE Unveils ENLIGHTEN Fraud Prevention Powered by AI and Voice Biometrics to Empower Contact Centers in Safeguarding Consumers 2

Using AI-enabled interpretive and predictive models and advanced voice biometrics, the new solution continuously scans millions of calls to proactively identify fraudulent behavior and protect brand reputation

NICE (Nasdaq: NICE) today unveiled ENLIGHTEN Fraud Prevention, an innovative new solution for automatic and continuous fraudster detection and exposure. Bringing together NICE ENLIGHTEN’s comprehensive Customer Engagement AI platform with the company’s voice biometrics capabilities, the solution continuously scans millions of calls to accurately pinpoint suspicious behavior and uncover previously unidentified fraudsters. Adopting a proactive approach, NICE ENLIGHTEN Fraud Prevention significantly reduces fraud losses and handling time while protecting consumers and improving their experience.

“Contact center fraud is growing in frequency, breadth and sophistication,” observes Dan Miller, Lead Analyst at Opus Research. “NICE ENLIGHTEN Fraud Prevention stands out as an integrated, pre-emptive AI-based Fraud Prevention solution that actively prevents malicious activities with minimum additional effort from customers.”

Unlike most technologies that focus on a single call, NICE ENLIGHTEN Fraud Prevention includes powerful AI interpretive and predictive models that scan millions of voice interactions over time to detect abnormal, risky behavior including requests to change addresses or authentication methods without relying on agents to manually capture dispositions. NICE’s Proactive Fraudster Exposure voice biometrics capability included within the solution is then used to expose perpetrators and create a ranked and prioritized list of suspected fraudsters. Importantly, the solution is self-training, constantly learning from identified behaviors, continuously updating its AI models and thus consistently improving results. With this novel solution, organizations can protect customers from account takeover and prevent exposure of personally identifiable information, reduce fraud losses, optimize fraud analyst team efficiency and safeguard brand loyalty.

“We are proud to bring yet another market-first offering with NICE ENLIGHTEN Fraud Prevention,” Barry Cooper, President, NICE Enterprise Group, said. “NICE ENLIGHTEN is NICE’s AI platform with models specific to the Customer Engagement domain. A number of solutions across our portfolio are being infused with AI from NICE ENLIGHTEN including our Proactive Fraudster Exposure solution. NICE ENLIGHTEN Fraud Prevention ensures that fraudsters are rapidly and proactively stopped in their tracks so organizations can protect their customers and their brand. We believe that by bringing AI to Fraud Prevention we provide organizations with the agility that makes it even more difficult for the fraudsters to win.”

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Financial Services Sector Leads in Fixing Application Flaws, Lags in Time to Remediate

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Financial Services Sector Leads in Fixing Application Flaws, Lags in Time to Remediate 3

Veracode, the largest global provider of application security testing (AST) solutions, today released findings revealing that the financial services industry has the best flaw fix rate across six industries and leads a majority of industries in uncovering flaws within open source components. Fixing open source flaws is critical because the attack surface of applications is much larger than developers expect when open source libraries are included indirectly.

The findings came as a result of Veracode’s State of Software Security Volume 11, which analysed 130,000 applications from 2,500 companies. The research found that financial services organizations have the smallest proportion of applications with flaws and the second-lowest prevalence of severe flaws behind the manufacturing sector. It also has the highest fix rate among all industries, fixing 75% of flaws. Still, the research found that financial services firms require about six and a half months to resolve half of the flaws they find, indicating it is slower than other industries to remediate.

“Financial services firms have a median time to remediation of more than six months, despite having a high fix rate compared to other sectors,” said Chris Wysopal, Chief Technology Officer at Veracode. “However, developers in the financial services industry are often limited by the nature of the environments they are working in, as applications tend to be older, have a medium flaw density, and aren’t consistently following DevSecOps practices compared to other industries. With some additional training and sticking to best practices, they can quickly remediate issues and start to reduce security debt.”

Financial Services Specific Findings

Veracode’s research found compelling evidence that certain developer behaviours associated with DevSecOps yield substantial benefits to software security. The findings detail that financial services firms:

  • Are a leading industry when it comes to fixing flaws in their open source software and establishing strong scan cadences.
  • Fall to middle-of-the-road for scanning frequency and integrating security testing, and are not likely to be using dynamic analysis (DAST) scanning technology to uncover vulnerabilities.
  • Outperform averages across all industries in dealing with issues related to cryptography, input validation, Cross-Site Scripting, and credentials management – all things related to protecting users of financial applications.
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