Tag

collect data

Browsing

By Faith Leroux

Do you ever feel like your phone or computer is spying on you? You pop open YouTube, and seconds into the video, an ad about your favourite chocolate brand shows up out of the blue. And you feel a bit of a chill since you were just talking about your sudden craving for that exact brand and type of chocolate. Well, it turns out that it can be like that. Information you enter online and through apps can be used to collect data such as your interests, hobbies, and simple search queries. Google is notorious for this. Companies love giving you personalized ads, and to avoid them, you usually have to opt out through the settings.

What’s really creepy about ads, besides their relevance, is their timing. It almost feels like they come up right after you’ve had real conversations about their products. This can happen for various reasons, one being search queries. You could have talked to your family about a want or a craving, and they might have looked it up online to see if it was in stock, or you’ve subconsciously checked the topic through an app or browsed online. It can also happen on social media: your friends could spark a conversation about what they like after you’ve posted about it or clicked on a related link. Moreover, ad targeting can also be based on your connections, which means you might end up receiving ads because someone else in your inner circle has liked or interacted with a particular topic or product.

Your digital footprint might be more extensive than you think

Hengki Lestio/Getty Images

As an Android user, having a Google account is a must. Plus, if you’re using one of the many services that exist on Chromebooks, desktops, and even other smartphones, like Google Chrome, Sheets, or even YouTube, anything you do is tracked for ads unless you sign out of your account or tell Google to stop ad targeting.

But having all your information in one place, especially when apps love using the “Sign in with Google” tactic to link it, doesn’t help. That contributes to your digital footprint, which is what makes up your profile online. Advertisers can use that digital footprint to get more information about you, like your age, gender, and occupation. It can also learn about your interests, your product purchase history, and link you back to others you interact with. Marketing companies love to review this so they can show relevant ads. Google is only one example, but Apple, Meta, and Amazon are all major players in the advertising space. It’s just that Google is the most dominant, so much so that the U.S. Department of Justice (DOJ) has challenged Google’s monopoly over the ad technology stack.

Your online behaviour gets tracked, and your data is harvested

Bestforbest/Getty Images

Installed apps love tracking your behaviour. It is partly to help with ad targeting, and partly because algorithms need it to give you relevant suggestions. Apps with a For You feed, found in many social media apps like TikTok, use it to show videos and images based on artists, creators, or even people in a social group that a user interacts with. But sometimes when you opt out of personalized recommendations and ads, those feeds stop working correctly.At other times, apps might abuse their privileges and collect more information about your habits without your permission, claiming they’re doing so to improve the app. But that doesn’t mean they should always have extra diagnostic information, such as your device ID.

Another issue is on-device permissions. Some apps abuse access when it isn’t necessary to function. Why does a random utility app like a calculator or a flashlight require microphone permissions to function? On the other hand, enabling that makes sense for audio-detection settings in sleep-coaching apps. So the question becomes: Why are apps asking for more permissions than necessary? In truth, apps that don’t need it could be doing so to harvest and monetize your data through third-party agencies. And thanks to those privacy-invasive practices, some people become paranoid that their apps are eavesdropping or straight-up spying on contacts when they shouldn’t be.

By Faith Leroux

Sourced from BGR

By Ali Azhar

Data mining tools can collect and analyse data in much the same way a human can, but much faster. Learn what data mining is, how it works and how to use it effectively.

Data mining is an important big data management strategy that is gaining steam, especially as organizations realize how many patterns and problems data mining operations can detect across their data sets. In this guide, learn what data mining is, how it operates and why it might be the next data management strategy you need to incorporate into your business.

Jump to:

What is data mining?

Data mining is used to identify patterns, correlations and anomalies in large data sets for data analysis. This helps turn raw data into actionable information to make informed business decisions, predict outcomes and develop business strategies.

Although the term “data mining” wasn’t coined until the 1990s, data mining techniques were used long before that. As the quality and complexity of data increased, software applications were used for data mining. The potential of data mining continues to increase with technological advancements in computing power and the enormous potential of big data.

Benefits of data mining

Data mining helps organizations analyse a large amount of data, deriving useful insights that allow an organization to become more efficient or profitable. With increases in data complexity and the volumes of data that are available to an organization, data mining provides a semi-automated way to process large data sets.

SEE: Data governance checklist for your organization (TechRepublic Premium)

An organization can make informed decisions and improve its strategic planning by uncovering data patterns, data anomalies and data correlations. Business executives can also use data mining to reduce legal, financial, cybersecurity and other types of risks to the organization.

How data mining operates

Data mining works by exploring and analysing large volumes of data to derive meaningful trends, relationships and patterns. Data mining software solutions are versatile tools that can be used for different objectives and functions like fraud detection, customer sentiment analysis and credit risk management.

Although data mining can be used in various ways, the process includes a few common steps. The first step is to gather and load the data. This step is followed by preparing the data through methods such as data cleansing or data transformation.

Once the data is prepared, it is ready to be mined. Computer applications with data mining algorithms are most frequently used to perform data mining. From there, data mining results are often translated into visual or statistical representations for further analysis.

Different types of data mining

There are several types of data mining techniques that businesses can apply to their big data. The right data mining technique to use depends on several factors, including the type of data and the objective of the data mining project. Here are some of the most common types of data mining:

Affinity grouping

Data elements that share the same characteristics are grouped. For example, customers that have the same buyer intent, interests or goals can be grouped. This type of data mining is also known as clustering.

Regression

Predicting data values based on a set of variables. This type of data mining is often used to find relationships between data sets.

Neural networks

Computing systems that are inspired by biological neural networks, such as the human brain. The algorithms in neural networks are useful for recognizing complex patterns in data.

Association rule

Association rules are established to determine the relationship between data elements. This includes determining co-occurrences and patterns in data.

Data mining examples

Telecommunications and media

Several industries use data mining, including the telecom and media industries, where it is often used to analyse consumer data. These companies use data mining to map customer behaviour and run highly targeted marketing campaigns.

Insurance

Similarly, data mining is commonly used in the insurance industry, where it helps companies solve complex problems related to compliance, customer attrition and risk management. Health insurance companies use data mining to map the patient’s medical history, examination results and treatment patterns. This helps them develop and execute an efficient health resource management strategy.

Manufacturing

Data mining is also used in the manufacturing industry to align supply chains with sales forecasts and for early detection of future problems. Through data mining, manufacturers are able to anticipate maintenance and predict the depreciation of production assets.

Banking

Finally, the banking industry uses data mining algorithms to detect fraud and other anomalies in their data. Data mining helps banks and other financial institutions achieve optimum ROI on marketing investments, meet compliance requirements and have a better view of market risks.

Top 3 GRC Solutions

1Domo

Visit website

Build a modern business, driven by data. Connect to any data source to bring your data together into one unified view, then make analytics available to drive insight-based actions—all while maintaining security and control. Domo serves enterprise customers in all industries looking to manage their entire organization from a single platform.

Learn more about Domo

2RSA

Visit website

RSA Archer removes silos from the risk management process so that all efforts are streamlined and the information is accurate, consolidated, and comprehensive. The platform’s configurability enables users to quickly make changes with no coding or database development required. Archer was named a Leader in Gartner’s 2020 Magic Quadrant for IT risk management and IT vendor risk management tools. Additionally, Forrester named it a Contender in its Q1 2020 GRC Wave.

Learn more about RSA

3LogicManager

Visit website

LogicManager’s GRC solution has specific use cases across financial services, education, government, healthcare, retail, and technology industries, among others. Like other competitive GRC solutions, it speeds the process of aggregating and mining data, building reports, and managing files. LogicManager is lauded for its user experience and technical training and was named a Challenger in Gartner’s 2020 Magic Quadrant for IT risk management. Forrester named it a Leader in its Q1 2020 GRC Wave.

Learn more about LogicManager

Feature Image Credit: ZinetroN/Adobe Stock

By Ali Azhar

Ali is a professional writer with diverse experience in content writing, technical writing, social media posts, SEO/SEM website optimization, and other types of projects. Ali has a background in engineering, allowing him to use his analytical skills and attention to detail for his writing projects.

Sourced from TechRepublic