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  • A $3.3 trillion resource drain is caused by 85% of ROT data stored by businesses.

    What is ROT data? ROT refers to (redundant, obsolete, or trivial) as a term for digital information that companies keep even when the data it contains has no practical or ethical value. Employees generate ROT by storing duplicates of similar documents, out-of-date information, and unnecessary data that hinders the achievement of organizational goals. ROT is harmful in five crucial ways. Expenditures for storage, infrastructure, and maintenance are high. It makes it harder for staff members to prove that they are following regulations or to react to discovery requests. It hinders employees' capacity for quick access to pertinent facts and swift, data-driven judgment. ROT is frequently poorly managed, which leaves it open to data breaches. Information that is kept after its legally mandated time frame also puts the organization at risk of responsibility. Causes of developing ROT data It has been recognized that employees use business IT systems as a repository for their personal data. Files, such as images, games with music including legal documents and personal identification. All of it ultimately coexists alongside sensitive and important business data. Sink and sharing services frequently transfer these files, however, they may not always be the ones approved by management. And not just employees are in possession of data. Still in use is the conventional method of storing everything. In what part of a data Berg does all this data go? Consider this as a mountain of accumulating data that is always increasing and permeates every Organisation. Only 14% of this data, according to research, is crucial for business.32% is of little or no commercial value. This indicates that the majority of data stored by the company is dark or unidentified. Organizations that have amassed data without procedures in place to classify and evaluate what they are holding on to are finding that the ROT data, which has an unknown value, and redundant, outmoded, or inconsequential data are being considered as an enormous burden according to senior IT decision makers. The issue has been exacerbated in part by the quick expansion of data collection methods. Businesses can now gather M2M data, log files, analytics, surveillance, and location information to use it to improve various aspects of their operations. However, the problem is that there are massive volumes of data being saved that have no value or worth that are unknown since they have not been analyzed because there are no procedures in place for the storage and management of such data. Practices to avoid ROT burden A straightforward victory might be achieved through proper data management created by a well-thought-out big data plan, especially because IT is expected to do more with less as a result of decreasing funds. Most people struggle with not knowing what data to start with, what risks it might hold, or where the value is found. They may involve other company stakeholders and go on with a well-thought-out plan faster and with greater confidence if they have visibility into that environment. Given the growing number of data rules, like the EU's General Data Protection Regulation, which will take effect in the next two years, not knowing what is being held can be extremely risky. Businesses will be required by the regulation to conduct mandatory breach notifications and also have to keep in mind the type of data they store. The corporation will have to disclose what data has been breached and inform customers if their data has been exposed in any manner. Full breaches will result in a penalty fee of either €100 million or 4% of the company's yearly revenue. The issue is whether corporations can accomplish that given that 52% of the data is regarded as dark. Even while certain data must be maintained for legal or compliance requirements, a successful data management strategy that integrates business and IT eliminates a lot of superfluous data. "It's an age-old challenge; you have IT saying keep nothing and legal and compliance saying preserve everything so you have to find that balance," said Joe Garber, VP, of Marketing, HPE Software, Big Data Solutions, to CBR. There are two practices to approach the issue: one is to examine historically across time to create the best possible policies, and the other is to get close to those policies and properly analyze them. This entails analyzing the policies to determine what is required and what is not. According to IT leaders, the survey concluded that just 15% of all stored data may be categorized as business-critical data. It is anticipated that storing non-critical information will cost an average midsize company with 1000 TB of data more than $650,000 per year. Financial losses for companies can be seen as a result of these practices and, sure enough, using data that isn't compliant could cost them much more. How therefore can businesses approach the data? Make a move, to archives, make better business decisions Securely erase or anonymize your data. Take command, Improve the information management policies, and influence employee conduct Illuminate ROT data to draw attention to value and reveal risk. Make a taxonomy for your data that is practical. Establish a consistent set of definitions, labels, and groupings with the help of important stakeholders so that you can comprehend the data you have. Establishing best practices and policies to control ROT data. Create routines, for instance, for deleting unnecessary data and old records. For every category of information, establish a single source of truth (SSOT). Preventing the development of ROT Being one step ahead of the development of ROT data requires constant effort rather than a one-time action. Investing in a powerful file analysis system or tools available in the market will help businesses automate important information management processes, assure proper data tagging, and promote strong information management based on smart data evaluation. Following are some steps businesses can follow to prevent ROT development. Recognize ROT data throughout the whole IT infrastructure. Facilitate activities related to legal and regulatory compliance Making data more accessible will boost productivity. Cut back on data management and storage expenses. Lessen the likelihood of security issues and lessen the financial impact of a data breach Accurate search results can help you make better decisions. Conclusion It can be concluded that employees are contributing to generating ROT by storing duplicates of similar documents, out-of-date information, and unnecessary data that hinders the achievement of organizational goals. ROT is detrimental in five key ways. Storage, infrastructure, and maintenance costs are significant. Staff members find it more difficult to respond to discovery demands or to demonstrate that they are adhering to regulations. Employees' ability to quickly access important facts and make timely, data-driven decisions is hampered. ROT typically exhibits inadequate management, which makes it vulnerable to data intrusions. The organisation runs the risk of liability if the information is maintained for longer than the minimum amount of time required by law. By 2020, managing (ROT) Redundant, Obsolete, Trivial, and dark business data might cost corporations $3.3 trillion. Thus, companies are advised to take strict regulatory action to prevent the development of ROT data. Businesses will be required by the regulation to conduct mandatory breach notifications and also have to keep in mind the type of data they store.

  • The Top Ethical Principles for Web Machine Learning

    As the world becomes more dependent on computers and algorithms, artificial intelligence (AI) and machine learning (ML) will play an increasingly important role in our lives, especially as they become smarter and increasingly complex. Because of this, it’s important to create ethical principles that guide the use of AI and ML so that we may avoid catastrophic consequences like those seen in Hollywood movies like Skynet or The Matrix. Here are the top five ethical principles for web machine learning to help guide both your development process and your business decisions. What is Web Machine Learning? Web machine learning is a process of using algorithms to automatically learn and improve from experience without being explicitly programmed. It is mainly used to make predictions or recommendations based on data. The two main types are supervised, where the algorithm tries to predict an outcome, and unsupervised, where the algorithm groups objects together in clusters. Supervised machine learning is most often applied in problems such as spam detection, language translation, autonomous driving systems, etc. Unsupervised machine learning has applications in pattern recognition (e.g., image compression), recommender systems (e.g., movie recommendations), etc. Fairness When it comes to web machine learning, fairness is one of the most important ethical principles to consider. Fairness means that individuals should be treated equally and fairly, without discrimination. Accuracy In machine learning, accuracy is a measure of how well a model predicts outcomes. The higher the accuracy, the better the predictions. However, accuracy is not the only important thing to consider when creating a machine learning model. There are also ethical principles that need to be taken into account. Transparency To maintain trust with users, machine learning systems must be transparent about how they work. This means providing information about the data that was used to train the system, the algorithms that were employed, and the results of any evaluations that have been conducted. Furthermore, it is important to give users control over their data and what happens to it. This includes letting them know when their data is being used to train a machine learning system and giving them the ability to opt-out if they so choose. Privacy One of the most important ethical principles when it comes to web machine learning is privacy. Any data that is collected should be done so with the explicit consent of the individual involved. Furthermore, this data should be anonymized as much as possible to protect the identity of the individual. The data should also be stored securely to prevent any unauthorized access. Finally, when the data is no longer needed, it should be destroyed securely. Security When it comes to web machine learning, security is of the utmost importance. After all, you’re dealing with sensitive data that could be used to exploit individuals or groups. Here are five ethical principles to keep in mind when working with web machine learning How can good ethics have a better future? There's no doubt that machine learning is revolutionizing the way we live and work. But as with any new technology, there are ethical considerations to be taken into account. And these considerations are often overlooked. It's not always easy to separate the good from the bad in this arena of ethics. Case Studies: Insurance Sectors There are a few case studies that show how the insurance sector has been evolving with the changes in technology. In one case, an insurance company started using predictive analytics to identify which customers were more likely to file a claim. The company then proactively reached out to these customers to offer them preventive care options, which helped reduce the number of claims filed. In another case, a different insurance company started using machine learning to automate the process of detecting fraud. This not only helped the company save money, but it also helped them improve customer satisfaction by catching fraudulent claims before they were paid out. Conclusion To improve the accuracy of their algorithms, many companies have begun using machine learning to personalize services and target users with advertising based on their browsing history and other data points like their location or gender. This has raised privacy concerns among consumers and has become a hot-button issue in Congress, but as long as people are willing to give up their personal information to receive tailored ads, this practice isn't likely to change any time soon.

  • Ethical Dilemmas of Digital Marketing

    When it comes to the world of online marketing, many potential ethical dilemmas can arise as you run your business. These issues can affect your clients, your employees, and even yourself – but they don't have to get in the way of being successful with your business if you keep them in mind and make smart decisions when they come up. Here are seven common issues that you should consider when you're starting up your company so that you can avoid any possible problems down the road. What is Digital Marketing? Digital marketing is the process of using online channels to promote and sell products or services. The main goal of digital marketing is to reach a wider audience than traditional marketing techniques and to do so more effectively. However, with increased reach and effectiveness comes increased responsibility. While some ethical dilemmas are relatively easy to solve, others are much harder. For example, when should content be promoted? When should it be taken down? What guidelines should be followed for sponsored posts? What guidelines should be followed for influencer campaigns? These questions have no right answer because they all come down to one's moral compass. A good rule of thumb is to not hurt people; don't intentionally take advantage of them. As marketers, we must always remember that our words and actions will have consequences that can have lasting effects on others. 1) Google Analytics Google Analytics is a powerful tool that can help you track and analyze your website traffic. However, it's important to use this tool ethically, to avoid violating the privacy of your visitors. Here are seven ethical dilemmas to consider when using Google Analytics 2) Tracking Page Visitors vs. Users You want to track who is visiting your website or landing page so that you can adjust your marketing accordingly. However, you also don't want to be accused of invading someone's privacy. This ethical dilemma is a common one in digital marketing. 3) User Experience In a world where companies are constantly vying for our attention, it's important to consider the ethical implications of digital marketing. From bombarding us with ads to collecting our data, there are a lot of ways companies can cross the line. 4) Misleading Ads Ads that are intentionally misleading are not only unethical, but they're also illegal. You could be fined or even sued if you're caught running a misleading ad campaign. Plus, you'll lose the trust of your customers and damage your reputation. 5) Fake Links Digital marketing is one of the most effective ways to reach consumers, but it's not without its ethical dilemmas. From privacy concerns to manipulation, here are ethical concerns you should consider before starting your next digital marketing campaign. 6) Customer Privacy In the digital age, consumer privacy is more important than ever. With so much information available online, it can be difficult to keep track of what data is being collected and how it's being used. As a marketer, it's important to be aware of the ethical implications of collecting and using customer data. 7) Clickbait Headlines Digital marketing is full of ethical dilemmas. From collecting data to using AI, there are many gray areas when it comes to what is considered ethical. How can good ethics have a better future? In a world where technology is constantly advancing, businesses need to be aware of the ethical implications of their marketing practices. By understanding and following the seven ethics of digital marketing, businesses can ensure they're making the best decisions for their customers, employees, and shareholders. Wholesaler Drives: A Case Study As a wholesaler, you're always looking for ways to increase sales and grow your business. You've been using traditional marketing methods for years, but with the advent of digital marketing, you're not sure if it's the right move for your company. After all, there are some ethical concerns associated with digital marketing. Is it worth the risk? Conclusion Digital marketing ethics represent the core values that guide ethical marketers to make decisions when planning, producing, delivering, and evaluating their marketing programs. In today's digital age, brands have more avenues than ever to connect with consumers. This also gives them more opportunities to engage in unethical practices that can harm the health of their organisations and brands over time. That's why marketers need to learn about digital marketing ethics and practice them regularly in their day-to-day work lives.

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  • Become a Com Olho Partner | Parnters Program | Com Olho

    Help Center How can we help? Developers Documentation Read our developer's reference documentation Publishers List Manual on how to upload the publisher's list on social platforms. Blogs Read more about our solutions and offerings. Com Olho's Help Center offers assistance and resources to help you achieve your goals. Get help storing and synchronising your database in real time, making data storage more convenient and accessible from any device: web or mobile. Experience behavioural analytics and break your data into data sets into related groups(cohorts) helping you to make more informed business decisions that will reduce churn and significantly increase revenue. The platform is based on patented technology for ad fraud detection, and transforms the data into second set of features for analysis and deterministic detection. Com Olho's Platform has the resources and expertise enterprises need, whether they're seeking for help with a single service or a long-term engagement. Get Support? Can't find what you need in our help documents ? Get in touch via email or schedule a demo with us. Email Support Schedule a Demo 2021-2022 COM OLHO

  • Com Olho | Building Technology Bridges of Truth

    Top of Page प्रसिद्ध भारतीय वैज्ञानिक सपना Patents Funding Research Mentions These 6 Indian origin cybersecurity startups are redefining digital security landscape

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