O'Reilly on Data

NOVEMBER 13, 2018

The Future of Privacy Forum and Immuta recently released a report with some great suggestions on how one might approach machine learning projects with risk management in mind: When you’re working on a machine learning project, you need to employ a mix of data engineers, data scientists, and domain experts. Continue reading Managing risk in machine learning Considerations for a world where ML models are becoming mission critical.

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John Wheeler

AUGUST 28, 2020

Digital risk continues to grow in importance for corporate boards as they recognize the critical nature of digital business transformation today. Not only that, but 49% of directors cite the need to reduce legal, compliance and reputation risk related to digital investments.

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DataFloq

OCTOBER 21, 2020

LONDON (Reuters) - Democracies around the world are under a constant barrage of cyberattacks that must be identified, called out and stopped, Microsoft President Brad Smith told a gathering of security and defence officials on Wednesday.

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John Wheeler

AUGUST 19, 2019

Gartner’s “Hype Cycle for Risk Management, 2019” report was published almost a month ago and reader response has been overwhelmingly positive. In this year’s report, we highlight the need for a “PRACtical” view of risk management technologies to fuel digital business growth. Hype Cycle for Risk Management, 2019 Source: Gartner. Risk management continues to mature and is offering a more practical approach to CEOs looking to improve.

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Advertiser: UserTesting

Agile has become the go-to methodology for companies that want to reduce the risk involved in shipping new products. But how do you prevent building items nobody wants? If you wait to get user feedback until after development, then you’ve waited too long.

Risk

Analytics Vidhya

MARCH 6, 2019

The post Hands-On Introduction to creditR: An Amazing R Package to Enhance Credit Risk Scoring and Validation appeared first on Analytics Vidhya. Data Science Machine Learning R Credit Risk data science machine learningIntroduction Machine learning is disrupting multiple and diverse industries right now. One of the biggest industries to be impacted – finance. Functions like fraud.

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John Wheeler

OCTOBER 21, 2019

Over the past month, I’ve been speaking to various groups to help them prepare for the onslaught of digital risks in their organizations. A common theme is the need for greater risk quantification beyond the realm of traditional, qualitative governance, risk and compliance (GRC) approaches. Digital Risk Management – Gartner. However, in the pursuit of techquilibrium, organizations must take their risk analysis a step further to the strategic view.

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Smart Data Collective

AUGUST 27, 2020

It’s important to know how to protect your own firm from spend risk, supply chain disruption while enhancing the company’s ability to thrive. It’s difficult to mitigate supply chain risk in the best of times. Mitigate spend risk in real time.

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DataFloq

AUGUST 19, 2020

The world's biggest social network, saying it was expanding its policy on perceived risks to public safety, also imposed restrictions on the remaining 1,950 public and private QAnon groups it could find. By Joseph Menn.

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John Wheeler

SEPTEMBER 15, 2020

As the recovery efforts fully take hold in 2021, a deep understanding of the integrated nature of risks associated with business operations will take center stage. A “PRACtical” Approach Provides a Balanced View of Risk.

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O'Reilly on Data

APRIL 12, 2019

Balancing risk and reward is a necessary tension we'll need to understand as we continue our journey into the age of data. But those opportunities were balanced against risks—risks that loom large as we discover more powerful ways to apply data using machine learning and artificial intelligence. What about the risks? The risks of a hostile, militarized network are real. Continue reading Strata San Francisco, 2019: Opportunities and Risks

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Advertiser: Data Robot

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. But times are changing. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

Machine Learning

DataFloq

OCTOBER 15, 2020

The sharper regulatory focus over Ant's cash cow and rapidly growing consumer lending business to curb financial sector risk has emerged as a key concern for potential investors ahead of its likely $35 billion float, the world's largest. By Cheng Leng and Julie Zhu.

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DataFloq

SEPTEMBER 26, 2020

SHANGHAI/WASHINGTON (Reuters) - The United States has imposed restrictions on exports to China's biggest chip maker SMIC after concluding there is an "unacceptable risk" equipment supplied to it could be used for military purposes.

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DataFloq

OCTOBER 7, 2020

(Reuters) - Tesla Inc Chief Executive Elon Musk said on Wednesday the company will produce Model Y with a new structural battery design and technology at its Berlin factory next year and that could result in a "significant production risk".

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DataFloq

OCTOBER 1, 2020

LONDON (Reuters) - China's Huawei Technologies has failed to convince British security officials that the security risks of using its products in UK national infrastructure can be adequately managed, according to a government report released on Thursday. By Jack Stubbs.

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KDnuggets

JANUARY 10, 2020

Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently. 2020 Jan Opinions AI Deep Fakes Security

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Advertiser: UserTesting

Up to 80% of new products fail. The reality is harsh and the reasons why are endless. Perhaps the new product couldn’t oust a customer favorite. Maybe it looked great but was too hard to use. Or, despite being a superior product, the go-to-market strategy failed. There’s always a risk when building a new product, but you can hedge your bets by understanding exactly what your customers' expectations truly are at every step of the development process.

Testing

Smart Data Collective

JUNE 19, 2019

The Relationship between Big Data and Risk Management. While the sophisticated Internet of Things can positively impact your business, it also carries a significant risk of data misuse. The application of big data to your organization’s risk management system ensures that you get a comprehensive system that will safeguard your data, revenue streams, and predict changes that may affect the growth of the company (either negatively or positively). Vendor Risk Management (VRM).

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DataFloq

APRIL 10, 2020

One thing we continue to hear again and again in regards to coming technological advancements relates to the benefits of connected and autonomous vehicles and how they will drastically improve our way of life. No matter what your thoughts or opinions concerning connected and autonomous vehicles, 2020 will see their continued rise and the imminent deployment of them is likely in the foreseeable future.

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DataFloq

MARCH 12, 2020

By defining the roles & rights within the data, organisations can also control and restrict the data and reports access for individuals belonging to different functions, departments and hierarchy.How can organisations rightly position themselves to address the opportunities that arise from the emergence of self-service analytics & what are the risks that organisations need to avoid?The Today, organisations are becoming increasingly data-driven.

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KDnuggets

SEPTEMBER 20, 2019

2019 Sep Opinions AI Big Data Causation Correlation Overfitting RiskWith recent advances in AI being enabled through access to so much “Big Data” and cheap computing power, there is incredible momentum in the field. Can big data really deliver on all this hype, and what can go wrong?

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Advertiser: Data Robot

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. But times are changing. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

Machine Learning

Cloudera

MARCH 28, 2019

The trouble is, mortgage lenders persist in relying on historical macro-economic assumptions in their models so they risk repeating the errors of a decade ago when banks – and their regulators – failed to recognize the warning signs from a far richer source: low-level micro-economic data. They also fail to model the effects of fear and the risk of contagion. Risk management 3.0. The post Managing mortgage risk in an uncertain world appeared first on Cloudera Blog.

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DataFloq

SEPTEMBER 1, 2020

(Reuters) - Wall Street analysts raised price targets for Zoom Video Communications on Tuesday as its shares hit a record high, while warning that a return to office work could hamper its success in luring small corporate customers.

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DataFloq

MAY 16, 2019

Despite the pros of open source projects, there are issues with security risks and code vulnerabilities when using components from such projects. This article informs you of some of the main open source security risks and vulnerabilities you should know about in 2019. One of the biggest security risks that still continues to plague commercial software development teams is the issue of remediating vulnerabilities too slowly after a fix has already been released. .

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Corinium

AUGUST 6, 2019

The Fourth Industrial Revolution is, ostensibly, upon us. The term was coined in 2016 by Klaus Schwab, the founder and executive chairman of the World Economic Form. Data Science Africa

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Speaker: Tolga Tarhan, Senior Vice President, GM AWS Services at Onica

When it comes to the modern tech stack, one of the fastest changing areas is around containers, serverless, and choosing the ideal path to cloud native computing. How do you as a technology leader point your team in the right direction, especially given how quickly this is evolving?

Technology

John Wheeler

OCTOBER 20, 2020

We continue our “20 for 20” theme this year by highlighting the integrated risk management (IRM) critical capabilities and top 20 software functions / features. Risk & Control Documentation /Assessment. Risk Mitigation Action Planning.

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Tamr

MAY 6, 2020

Warren Buffett, the world’s most famous investor, has a great quote about managing risk. “It’s This is prudent investment advice – but it’s also a great lesson for today’s supply chain leaders about risk exposure. The past few weeks have revealed that most companies actually have a fairly poor understanding about their exposure to risk. This requires an accurate and trusted pipeline of data that can help answer critical questions to identify supply chain risk.

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IBM Big Data Hub

MAY 4, 2020

But some AI models can add complexity and risk. You can minimize that risk and also streamline the process of model validation by using IBM Cloud Pak for Data , a data and AI platform that includes IBM Watson Studio, Watson Machine Learning, Watson OpenScale and other services Many financial firms are increasing their use of AI models because they can represent the real world more accurately, and they can deliver better projections than traditional, rule-based models.

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bridgei2i

SEPTEMBER 24, 2020

However, risk management is no way lagging. ERM or Enterprise Risk Management is being used to identify crises long before it blows up into a huge problem. AI is being used to assess, prioritize, and mitigate risks in the enterprise so that the business operations do not take a hit.

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Analytics

Smart Data Collective

AUGUST 2, 2019

Now, a new benefit of AI is joining the list: avoiding the risk of website accessibility lawsuits. The post AI Advances Minimize Risk of Site Accessibility Lawsuits in eCommerce appeared first on SmartData Collective. Artificial Intelligence Risk Management SmartData Collective Exclusive accessiblity AI artificial intelligence ecommerce lawsuitsAI has become an important factor in the ecommerce industry.

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Corinium

NOVEMBER 19, 2019

How are you creating a better-integrated security ecosystem in your organisation? My recipe to create an integrated ecosystem is to merge technology (operational and infrastructure) with business value(process) and culture(people). The internal ecosystem must be complimented with external partnerships and we need to start working together to redefine our end goal and purpose as a security professional.

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Smart Data Collective

MAY 15, 2019

On the other hand, big data has created a number of security risks that they need to be aware of, especially with brands leveraging Hadoop technology. Big data has created a number of security risks for Bluetooth users. Bluetooth Security Risks in the Age of Big Data. Today, the company has discovered that data has created a number of risks. This means that users’ personal information is at risk every time they use a Bluetooth device.

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DataFloq

DECEMBER 15, 2019

In the banking industry, trust is the largest bankable asset for any institution and its risk profiling happens to be the most volatile, complex, and regressional one among the non-systematic risks. We will explore the role of Big Data Analytics in catering a support mechanism to the industry. I will also share hypothetical scenarios for the audience and the potential of this advanced technology in harnessing the best practices. Current Challenges.

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that you’re at risk for an analytics emergency. teams risk falling straight off it. requests, you risk unhappy customers and skyrocketing. And ignoring the warning signs means you risk losing. Updating your application’s dashboards and reporting features.

Analytics

John Wheeler

MAY 2, 2019

Gartner clients are consistently searching for ways to improve their risk management programs to deliver greater value to the enterprise. Whether it is to bolster performance in support of new digital transformation efforts or to reduce spend associated with bloated governance, risk and compliance (GRC) bureaucracies, the need for better solutions is real. That’s why Gartner has been promoting integrated risk management (IRM) solutions for the past 4 years.

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John Wheeler

DECEMBER 18, 2018

Last week, I had the distinct privilege to join my Gartner colleagues from our Risk Management Leadership Council in presenting the Q4 2018 Emerging Risk Report. We hosted more than 500 risk leaders across the globe in our exploration of the most critical risks. The Q4 2018 Emerging Risks Survey, designed by Gartner, captures and analyzes senior executives’ opinions on emerging risks and provides actionable insight on identifying and mitigating these risks.

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Smart Data Collective

APRIL 18, 2019

The post Can AI Remove The Risk From EHRs? When the Obama administration authorized the law that would fund the expansion of electronic health records (EHRs), the idea was that this would transform American healthcare. Records would become readily portable and individuals and their doctors would have easy access to this information when it’s needed most.

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Sisense

SEPTEMBER 5, 2019

For modern organizations, data is the ultimate building block. High profile companies are using data to build profitable new products, new lines of business, even entirely new industries. It’s the starting point and the finish line for every new business creation. In an age where every company is making moves to be more data-driven, those that figure out how to efficiently monetize their data insights will be the biggest winners. The process of translating data into new revenue is not easy.

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for all your users, you risk upsetting customers who don’t care about and may never. unnecessary risk and results in incorrect (and sometimes dangerous) decisions. for an add-on product or capability and risk leaving potential revenue on the table.

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