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Six Strategies for Building Resilient Supply Chains with Multi-Domain MDM

Posted in: Master Data Management - Jun 25, 2021

Join this TDWI Webinar to learn how multi-domain MDM helps you reduce data chaos and gain faster and easier access to diverse but accurate and complete data.top

What Does Digital Transformation Mean for IT?

Posted in: business transformation, Digital Transformation, News & Trends - Jun 23, 2021

The business world has been enamored with the concept of digital transformation for some time. While the term first appeared in public use in the late ’40s, conversation about the concept skyrocketed in 2010 , promising early adopters a new era of availability, resilience, and performance. On the business side, the hope is that it will greatly enhance overall efficiency and give a substantial boost to marketing and sales efforts. But what does it mean for IT? 

The digitalization of IT has been going on for some time. Concepts like virtualization, Anything-as-a-Service, and software-defined storage/networking have digital transformation at their core – their underlying premise is to add a layer of software to simplify tasks such as provisioning, organization, and optimization of hardware and software elements. By doing so, management becomes easier, and automation can move forward. Consider how long it used to take to request, procure, deliver, and make operational new servers or endpoints. Instead of weeks or months, it can be done in days, and sometimes hours. 

More Digital Progress Needed

Yes, great strides have been made in automation and digitalization. But there is still a long way to go. Take the case of a server build, which can still take several hours.  

“It shouldn’t require a human to care and feed it and press ‘Next’ after each prompt,” said Penny Jones, an analyst at 451 Group. “We have to automate mundane tasks.” 

One technology that forwards the overall digital transformation of IT is Data Center Infrastructure Management (DCIM). DCIM helps IT in preventing outages, lowering maintenance, and in the extraction of data for machine learning and deep learning models to glean greater value from operational data.

DCIM Value

DCIM has gained a following in some areas of IT such as data center planning. A U.S. semiconductor manufacturer, for example, was faced with its main data centers reaching capacity. It drew up plans to add two new facilities.

Before commencing, the manufacturer used the Serverfarm InCommand managed DCIM service to review the existing state of its data center operations. Along with reviewing failover requirements and running several what-if capacity scenarios inventoried 700 cabinets and 10,000 devices to create accurate records to upload into the InCommand portal. By providing a digital record of all assets (make/model/owner, power, cable and location details), it became possible to query and search all infrastructure elements for the first time. Data center personnel could also dynamically generate rack configurations, as well as power loads and thresholds to determine the best course. 

Result: The semiconductor manufacturer changed its plans. It figured out how to extend the life of its current infrastructure via server refreshes, and the streamlining of existing operations. Doing so saved the manufacturer tens of millions in construction.

Long Transition

The digital transformation of the data center, however, will not happen overnight as it is far from a simple matter.

IT has to address a jumble of infrastructure often dating back many decades. These aging hardware and software systems have to be reconciled and integrated with modern systems, protocols, and platforms. The complexity involved may appear staggering, but the way ahead is to break it down into its key elements.

CIOs looking to show immediate return on the investment of time and resources should first identify where it makes sense to digitalize, and where acceptable ROI may be the most rapid. 

Regardless of how daunting it may seem, it is a task that probably can’t be avoided. According to a BMC Software study, 73% of IT managers and IT decision makers believe businesses that do not embrace IT automation and digital transformation will cease to exist within a decade. 

The post What Does Digital Transformation Mean for IT? appeared first on CIO Insight.

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SAP ERP Software: S/4HANA Cloud Review for 2021

Posted in: AI, artificial intelligence, Business Intelligence, cloud platform, Cloud Virtualization, Enterprise Apps, enterprise resource planning, Enterprise Resource Planning (ERP), ERP, globalization, Innovation, IT Management, machine learning, ML, Oracle ERP, product roadmap, SAP, SAP ERP, SAP HANA, SAP S/4HANA - Jun 21, 2021

SAP has long been a leader in the enterprise resource planning (ERP) software space, mostly providing enterprise-level integrated solutions for administrative processes. But in more recent years, they’ve started to transition away from their traditional on-premise, data center solutions like SAP ERP 6.0 and other Business Suite 7 tools, in favor of a cloud approach with SAP S/4HANA Cloud.

Are you a current SAP customer looking to transition to the S/4HANA platform? Are you an enterprise in search of the right ERP software to support your back office operations? This review offers a closer look at the features, benefits, pricing, and other common questions about SAP’s S/4HANA Cloud ERP software.

Read More on ERP: Three Key Advances in ERP for 2021

SAP Enterprise Resource Planning (ERP) Software Overview

• What are the Core Features of SAP S/4HANA Cloud?
• Benefits 
• Pricing
• What is the Difference Between SAP and ERP?
• Which Types of Businesses Use SAP?

What are the Core Features of SAP S/4HANA Cloud?

SAP S/4HANA was developed as a large enterprise cloud replacement for legacy systems like SAP R/3 and SAP ERP, and retains many of their strongest features. However, S/4HANA advances beyond these legacy systems in the areas of AI-powered analytics, intelligent process automation, and smoother data integration with the SAP HANA in-memory database. 

Some of the top features that S/4HANA Cloud offers its customers include the following:

• Cloud-native Design
• Integrated idea to design, procure to pay, plan to production, order to cash, offer to project, and core finance features
• Embedded AI and Machine Learning (ML)
• Advanced Analytics
• Predictive Accounting
• Intelligent Auditing
• Robotics Automation
• Image-Based Buying
• In-Memory Database
• Consumer-grade UX

Benefits 

Business Intelligence

End-to-end business scenarios, embedded intelligent automation, and dynamic resource allocation enable enterprises to quickly optimize and move toward new business models and processes.

Industry-Agnostic

SAP S/4HANA Cloud was not designed with any particular industry in mind, but rather with the scope of larger enterprises as its vision. Because it’s not particularly aligned with one industry, you can use the tool at its generic foundation or add on one of over 20 industry-specific solutions to your implementation.

Unified Data and Analytics Source

As a rule, ERP platforms unify your software and data sources into a single space, but some platforms complicate that process through a large number of external integrations. With S4/HANA, you’ll be working with analytics and data insights directly from the SAP HANA in-memory database, which makes predictive analytics and competitor analysis that much simpler because those key functions will run from a consistent, internal data set.

Globalization and Localization

One of SAP’s greatest strengths is its focus on globalization and its already significant global presence. The company’s existing global customer support network covers 34 languages and 64 countries, as well as several financial and linguistic localization features. So regardless of where your business is headquartered or where your employees are located, they’ll be able to work with an SAP team that understands their language as well as local regulations and requirements for business technology. 

(Data Source: IDC MarketScape 2020 Report)

Familiarity of SAP Software

SAP is a diverse family of business technology, with an ERP market share of approximately 6.8%. Many users are also already familiar with their tools for supply chain, procurement, database management, and HCM. With so many users already experienced in the SAP approach to business software, transitioning to SAP S/4HANA becomes an easy move for many enterprises that know how to use their other tools.

Visionary Product Planning

SAP is known for its forward-thinking approach to ERP and other business technology. Every few years, they release a new product roadmap, detailing what they expect to improve on and add to their existing tools. Most significantly, they release these roadmaps and reports to their existing customers ahead of the more widespread information release, which gives customers a transparent glimpse into how their tools will function and adjust to changing business needs in the future.

Read a Customer Review of SAP S/4HANA Cloud Here 

Pricing

SAP S/4HANA’s pricing model is not incredibly transparent unless you speak to their team about custom pricing. Their packages come in two different models: subscription by module or user type, or through a traditional licensing structure.

Regardless of which package you choose, or if you determine that the solution doesn’t align with your needs, SAP S/4HANA Cloud is available on a free trial basis for up to 14 days.

What is the Difference Between SAP and ERP?

SAP stands for “Systems Applications and Products in Data Processing” and is the name of the largest ERP vendor in the world. SAP offers a wide variety of other enterprise software solutions including CRM and customer experience, supply chain management, network and spend management, HR and people engagement, and business technology. 

The idea behind SAP’s ERP solutions, and ERP platforms as a whole, is to unify an enterprise’s core functionalities and software so that data and communications can be more easily transmitted across the enterprise. SAP’s key differentiators include strong native integrations and extensive AI/ML features across its portfolio.

Which Types of Businesses Use SAP?

SAP is known for being an industry-agnostic platform, working with customers across all verticals. They also offer industry-specific software solutions for 25+ industry-specific needs, so if you can’t find exactly what you need in the standard ERP, their add-ons and integrations may solve your problems. 

Given their robust range of tools, including AI and ML-powered solutions, as well as their global focus, SAP S/4HANA Cloud best fits larger enterprises that are looking to expand globally or improve their capabilities in intelligent data processing and insights.

If you’re interested in ERP solutions by SAP or are looking to learn more about other solutions in the ERP landscape, try this ERP Selection Tool from TechnologyAdvice to guide your selection process to the platform that best fits your needs.

Another ERP Solution to Consider: Oracle NetSuite ERP: The Pros and Cons

The post SAP ERP Software: S/4HANA Cloud Review for 2021 appeared first on CIO Insight.

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What is Adversarial Machine Learning?

Posted in: adversarial machine learning, adversaries, adversary, algorithm, artificial intelligence, attack prevention, attack simulation, Big Data, deep learning, endpoint security, evasion attacks, Infrastructure, IT Strategy, machine learning, ML, News & Trends, poisoning attacks, Security, Tay Twitter Bot, training data, training model - Jun 21, 2021

A human adversary stands in your way and stops at nothing to make your life more complicated, sometimes with dire consequences when they’re successful. Adversarial attacks parallel this approach, disrupting machine learning practices and resulting in dire consequences ranging from stalled business processes to serious human injury. 

Adversarial machine learning is a fairly new, but nonetheless burgeoning problem for AI innovation. A report from Gartner predicts that 30% of all cyberattacks will involve data poisoning or some other adversarial attack vector by 2022. With machine learning growing in popularity, it makes sense that more attacks are leveraged to disrupt machine learning and the systems innovations that they make possible.

Let’s take a look at the current landscape of adversarial machine learning, what experts believe could be possible for attacks in the future, and how you can defend against and mitigate the risk of these adversarial attacks.

More on Machine Learning: AI vs. Machine Learning: Their Differences and Impacts

A Closer Look at Adversarial Machine Learning

• How Do Adversarial Attacks Work?
• Examples of Adversarial Attacks in Machine Learning
• Risks of Adversarial Machine Learning
• How to Defend Against an Adversarial Attack in Machine Learning 

How Do Adversarial Attacks Work?

Adversarial machine learning (ML) attacks all focus on making small, malevolent changes to reference data to obstruct initial training and deep learning for ML or to interfere with ML that is already trained. The goal behind adversarial attacks is to circumvent existing parameters and data rules so that the ML confuses its instructions and makes a mistake.

Attackers invade and obstruct your machines through a mixture of poisoning/contaminating and evasion attacks:

Poisoning/Contaminating Attacks

Poisoning and contaminating attacks make small changes to training data, often in inscrutable ways over a long period of time, to slowly train ML systems to make bad decisions in the future. Adversaries who use poisoning attacks usually look for back doors into the system’s training data and disguise malicious data by mislabeling it to look like other training data, thus enabling it to pass through the classifier. It’s often difficult to detect these disguised bits of training data, especially since the mistaken inputs and actions are rarely caught until long after the ML training phase.

Evasion Attacks

Evasion attacks typically happen after an ML system has been trained. Adversaries who attempt evasion attacks are looking to poke holes in a system’s existing training parameters. If they find a hole or vulnerability, they will use that discovery to “evade” security safeguards and gain access to the algorithms and codes that guide the ML system’s actions. These types of attacks can damage everything from intended outputs to data quality to system confidentiality.

Examples of Adversarial Attacks in Machine Learning

Only a small handful of adversarial machine learning attacks have been successfully launched in the real world but considering Amazon, Google, Tesla, and Microsoft are among the known victims, companies of any size and sophistication could suffer from adversarial consequences in the future.

Data and IT professionals are currently practicing adversarial attacks in the lab, experimenting with potential attacks to see how different ML scripts and ML-enabled technologies respond to those attacks. These are some of the theoretical attacks that they’ve attempted and believe could be launched successfully in the near future:

• 3D printing human facial features to fool facial recognition technology
• Adding new markers to roads or road signs to misdirect self-driving cars
• Inserting additional text in command scripts for military drones, changing their travel or attack vectors
• Changing command recognition for home assistant IoT technology so that it will perform the same action (or no action) for very different command sets.

A Real-Life Example of Adversarial Machine Learning

One of the most famous examples of a real-life adversarial machine learning attack happened with Microsoft’s Tay Twitter bot in 2016. Microsoft released Tay as a Twitter bot for conversational understanding, or an AI meant to improve its conversational skills the more that Twitter users engaged with it.

Several Twitter users decided to overrun Tay with offensive remarks, which over the course of fewer than 24 hours, completely changed the tone of Tay and made the bot misogynistic, racist, and utterly hateful.

Because of this unsophisticated, but nonetheless adversarial, attack against the tool, Microsoft shut down the bot to prevent it from making further offensive statements. The Twitterverse took control of a machine learning innovation with little to no effort, which is why so many tech experts fear the potential of coordinated adversarial attacks in the future.

Read Next: 10 Ways to Be More Human in the Age of AI

Risks of Adversarial Machine Learning

Although some adversarial attacks can result in alarming but ultimately negligible consequences like in the case of the Tay Twitter bot, adversarial machine learning could have the capacity to cause considerable damage to human life and business processes in the future. Some possible repercussions of adversarial machine learning attacks include:

• Physical danger and death, particularly if self-driving cars miss streetside indicators or if military drones are fed incorrect attack information. 
• Private training data getting stolen by competitors and used for their own competing innovations.
• Training algorithms being altered beyond your team’s recognition or ability to fix them, leaving machines virtually unusable.
• Supply chain and/or other business processes being disrupted, leading to delayed order deliveries and frustrated customers.
• Violation of personal data privacy, especially after membership inference attacks, leading to identity theft for customers.

Network Security Innovations: Are Air Gapped Networks Secure?

How to Defend Against an Adversarial Attack in Machine Learning 

Adversarial attacks seem like an unavoidable, looming problem, but many organizations are already discovering ways to combat these malicious attacks. Enterprises should take these proactive steps in order to protect their machine learning tools and algorithms:

• Strengthen your endpoint security and audit existing security measures regularly (Learn more about how endpoint security can protect your ML initiatives here).
• Take your ML systems through adversarial training and attack simulations. It’s a good idea to run practice trojan attacks on both your training and seasoned systems.  
• Change up your classification model algorithms so that malicious actors can’t as easily predict and learn your training models.
• Sharpen your knowledge of attacks and defense methods with an adversarial example library.

With the right research, training, and preparation in place, your team can predict and counteract many of the most likely adversarial attacks on your machine learning systems.

A Controversial, Effective Security Solution: End-to-End Encryption: Important Pros and Cons

The post What is Adversarial Machine Learning? appeared first on CIO Insight.

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Data Digest: Master Data Management, Cloud Data Management, Basic Data Strategies

Posted in: Master Data Management - Jun 17, 2021

Read these articles for the basics on master data management, managing big data across multiple clouds, and planning your enterprise’s long-term data strategy.top