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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?

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?

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.

Early Tay Tweet with positive message about puppies.

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

10 Ways to Be More Human in the Age of AI

Posted in: AI, artificial intelligence, b2b marketing, IT Strategy - Jun 15, 2021

Collage of experts interviewed for how to be more human in the age of AI.

 

What is one way that brands can be more human in the age of artificial intelligence (AI)?

To help brands market themselves in the age of AI, we asked business owners and marketing managers this question for their best advice. From creating genuine connections to personalizing your customer service, there are several suggestions that may help you reach more customers for the next quarter. 

Here are ten tips to be more human in the age of AI: 

Create Genuine Connections

Having that personal touch is a quality that consumers look for in the brands that they choose to buy from or do business with. Although artificial intelligence can streamline operations, there is nothing like having a genuine human connection. Brands can be more human by communicating with their customers through having real people answering calls and messages. Having that connection resonates with your customers and builds a relationship causing them to remain loyal.

Henry Babicheknko, Stomadent

Resist Automating Everything

That’s easy – don’t automate everything! I know lots of businesses want to automate as much as possible to increase their profit margins, but the truth is that people enjoy working with and buying from other people. Knowing that there is a real person processing your request versus a robot makes a huge difference to the end users. That is not to say that you shouldn’t automate anything, you should simply be very picky about what you chose to replace with AI.

Dale Gillespie, Tic Watches

Forefront Improvements to Productivity  

Consumers generally approach artificial intelligence (AI) with apprehension. To help relieve that apprehension, brands need to position the benefits of what AI can help a consumer accomplish. For example, Lightkey highlights how our AI-powered grammar correction and text prediction software can assist users in writing quickly and confidently in email and word processing. Our software learns user typing patterns so that it can offer up spelling revisions and multi-word prediction that includes punctuation. By filling in the blanks and defining the limits of technologies, brands can better connect with consumers.

Guy Katabi, Lightkey

Post Employee and Customer Profiles on Social Media 

One core value we have is “You Are Unique” – which is defined by respecting the uniqueness of every human being. We aim to practice this value by posting employee and sometimes customer profiles on our Instagram account (in fact, these profiles are pretty much all we post!). Not only are these profiles a great way to showcase the people behind our “digital” business, but they are also great ways to attract other job seeking candidates and like-minded small businesses to our company. For anyone looking to humanize their business, consider featuring the humans who make up your company on social media. 

Brett Farmiloe, Markitors

Strategize Your AI Processes

Artificial intelligence is incredibly useful in some areas of business, but in others it is more important to have a human element. My advice would be to map out your business processes and decide which bucket each process belongs to. For example, social media management does not produce great results when controlled by bots – in fact, you are penalized for it if caught. Because of this, it makes sense to have a human behind the screen, creating content, featuring real people in your organization, and of course engaging with your followers. These informed decisions will ensure your audience is taken care of during the most important times in their customer journey.

Francesca Yardley, Threads

Personalize Your Customer Service 

In the age of artificial intelligence, the way to be more human as a brand is to focus on all your touchpoints with your consumers. This means personalizing your customer service responses, improving your various marketing tactics, and more. Even if there are automated responses to customers, there are ways to personalize these and create a more human-like experience. Focus on your brand’s tone of voice, and what writing you use in your communications efforts. This is, after all, where your audience is going to get that sense of a human brand. It’s also a good idea to have real human interaction where it’s needed most (e.g. customer service) to strengthen that bond between your brand and the consumers, as well as prevent frustration from inaccurate AI responses. 

Tarah Darge, Time to Reply

Show Your Followers Your Personality

If you want to come off more human to your customers then you have to work on speaking in a direct and casual way in your social media posts and marketing campaigns. The business world has become so automated that all business\’s voices are coming off all in the same. Your interactions online should always be as organic as possible. You don\’t want to sound automated or planned. Posts need to be light-hearted and quippy to show everyone that you can relate to them instead of coming off as a big business. The better you can keep a small business feeling with your marketing strategy then the more likely your followers and customers will see you as a human than an organization.

Chris Gadek, AdQuick

Emphasize the People Behind the Business

It depends on exactly how you combine AI with your brand. In our case, we use AI to create customized skincare products for our customers. We also make sure to emphasize the backstory on how our business came to be. I was frustrated with not being able to find workable skincare products and then I found out through a specialist that customized skincare formulas are the key to improving skin. Our Co-Founder Amy Yuan was able to build a huge database of skincare information in order to use AI for creating customized skincare formulas, and now our business has grown. We make sure to emphasize the human element to this story – That trying to find the right skincare can often be frustrating, and this is why we are care a lot about what we do. As long as there is a real sense of enthusiasm behind your business, whether or not you use AI, your customers will sense this energy and feel more of a sense of connection with your product or service.

Ming Zhao, Proven Skincare

Highlight Your Humanitarian Efforts 

Prioritize your humanitarian efforts. There are so many ways businesses can give back, and every business should look into charities and organizations they can help out at. A recent effort we made was the introduction of our first apparel item, the “Bread X Life Sweatshirt”. All proceeds of this item are going to the World Food Programme, an organization combating food insecurity globally. Not only does this get the word out about our company, but it goes towards our greater message of intuitive eating and humanitarianism.

William Schumacher, Uprising Food

Use Tech to Enhance Communication

After a year of isolation, the importance of human connection can’t be understated. Artificial intelligence can play a key role in helping you manage internal processes. It can even help you manage lead qualification. But when you greet customers with an automated reply, you\’re essentially saying “I don\’t have time for you. Leave a message and I\’ll get back to you at a time that suits me. In a 24/7 business landscape, that\’s just not accessible. Human connection is particularly vital in your customer support. Connection is about feeling heard. And even the most advanced AI in the world can’t offer your visitors that feeling. The very act of automating your interactions shows you’re not hearing your visitors. For true connection, you need real people ready to reach out and respond, whatever the channel. In short, use technology to enhance communication, not replace it.

Benjamin Graham, AnswerConnect

 

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The post 10 Ways to Be More Human in the Age of AI appeared first on CIO Insight.

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Business Intelligence Journal | Vol 26, No 1<br /><span class=’memberInlineSnipe’>TDWI Member Exclusive</span>

Posted in: Business Intelligence - Jun 15, 2021

Staying on top of tomorrow’s trends while meeting current challenges is a tough balancing act.top

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This showcases our company news and upcoming events. Please check back as this page will change frequently.