Tag

collect data

Browsing

By

If you’ve ever wondered what websites do with the data they collect, you’re not alone. Many people assume the worst, thinking websites just want to target or scam you. But the truth is far more straightforward and less spicy.

Website Owners Need Your Data for Analytics

The most basic reason websites collect data is for analytics purposes. Much like how you monitor your social media accounts to see how many people have followed/unfollowed you, liked your posts, watched your videos, etc., website owners rely on your data to analyse how people are interacting with their services.

For instance, analytics can show which pages or content are most popular. Then, website owners can create or provide more of what resonates best with their audience.

Whether it’s tracking how many people visit a certain page, how long they spend on different sections, or which website features people use the most, data collection helps owners continually enhance the overall user experience. These details can offer clues on how they can improve the navigation, make content more accessible, or bring more relevant information to the fore.

Although collected from individuals, much of this data is aggregated—used as a whole to make general statements instead of identifying individual traits. At its core, analytics offers owners high-level snapshots like “10,000 people subscribed to my blog, with more females unsubscribing after two weeks than males.”

This data isn’t used to profile you—as someone who reads articles every evening. Rather, it helps owners identify common usage patterns so they can better tailor their services to the needs and preferences of their target audience.

Most Sites Want to Offer You Personalized Services


While analytics focus on aggregate insights about large groups of people, many sites also track some level of individual user data to cater to your unique needs and interests. If you’re anything like me, you’d rather see relevant product recommendations when you open an e-commerce site. Or, you just love Spotify’s daylist or DJ features.

These personalized recommendations are provided because the website or application tracks your previous purchases, listening history, browsing behaviour, etc.

However, it’s not only about personalized recommendations. If you’re visiting a site for the second time, you’d most likely see a different landing page from the one you saw the first time around. Similarly, the site header, font, page span, etc., will differ based on whether you’re using a smartphone or a laptop.

Have you ever noticed how a site remembers your language preference or theme? That’s also your data at work.

Big tech companies like Google typically employ individual tracking to “tailor your experience,” but again, this is usually strictly based on the information you voluntarily provide. You’ll always be able to disable personalization cookies or opt out of sites tracking your searches or activity.

Google Turn Off Personalisation Settings Information Page

Websites Can Collect Your Data to Show You Relevant Ads

Have you ever wondered how so many websites manage to remain free without any paywalls blocking your way? The truth is that most rely on ads to keep the information, videos, etc., flowing.

Whether you want to or not, you’ll most likely endure a barrage of ads every day, even if you use ad blockers. But the good thing is that, instead of irrelevant ads, you can opt for ads that’ll allow you to find things you might need or like. I’ve found several helpful, unique productivity apps enduring unwanted ads.

So, how do sites show you relevant ads? Simple—by tracking things like what product categories you typically browse and your past purchases. Run a search for something you’re thinking of buying, and lo and behold, related ads will pop up wherever you surf next. You can read our guide if you’d love to learn more about how websites track your online activity.

7 Ways to Protect Your Privacy Online

While most sites aren’t out to scam you, you should still take extra steps to keep your data safe and private.

  1. Don’t accept cookies blindly. Read through cookie/privacy policies carefully to understand what data a site collects and how it’ll be used. Don’t assume. Verify.
  2. Periodically clear the cookies you’ve accepted for specific sites. Modern browsers make this easy, as well as blocking third parties and opting out of interest-based ads.
  3. Use a VPN to keep your browsing activities hidden.
  4. On sites like Google and Facebook, opt out of personalization if you’d rather not receive ads based on your interests.
  5. Confirm that data transfer on each site you visit is secure; check that there is HTTPS in the URL field.
  6. Use a separate, complex password for each of your accounts. This way, if one of your accounts suffers from a data breach, it won’t affect all your accounts. There are tons of password managers you can take advantage of.
  7. Install privacy tools like Brave or Signal on any of your devices to block trackers that may be lurking in the background of sites.

While malicious sites exist, most collect data for legitimate reasons, not to scam you. It’s important not to attribute malicious intent at first glance. Remember, people run websites—and people make mistakes, which is why there are so many instances of data breaches and the like.

By

Sourced from MUO

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