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Data security

Data Security Challenges in Cloud Computing

Businesses of all sizes are migrating to the cloud to take advantage of the increased data availability, substantial cost savings, and data redundancy that cloud computing offers versus a traditional data center-based physical infrastructure.

By removing data stores from storage closets, opting for the cloud can help data be managed and safeguarded per best practices and legal requirements.

For businesses, choosing the best cloud service and putting in place their own security measures present many difficulties. Since there are now more cloud platforms available, it’s critical to make sure the service you select supports data integrity, privacy, and availability.

When moving to the cloud or changing your cloud storage plan, keep the following factors in mind.

Top Data Security Challenges in Cloud Computing

Business apps can these days grow to sky-high levels and handle complex use cases thanks to the boundless potential of cloud services. However, the level of threats that data on the cloud poses also rises.

We’ll then look at some of the greatest hurdles to protecting your cloud data.

Insecure Access Control Points

Cloud services are by their very nature available from any location and on any device. The widespread usage of components like API endpoints, which can be accessed from anywhere, poses a serious threat to the cloud’s security standing.

By making API endpoints vulnerable, a cybercriminal can access data and possibly change it, jeopardizing its integrity.

Here are two often-used ways to secure yourself:

  • Penetration testing which simulates an external assault on a set of API endpoints to breach security and gain access to the company’s confidential data.
  • Audits of general system security

Some challenges are connected. And to that point, insecure API leads to misconfigured cloud storage.

‍Misconfigured Cloud Storage

Misconfigured storage is a follow-up to an API (Application Programming Interface) cloud security issue. In most cases, security risks arise in cloud computing due to human error and audit done in haste. Cloud misconfiguration is actually a setting for servers (used for computational or storage purposes) that leaves them susceptible to hacks.

The most often seen forms of misconfiguration are:

  • Default server settings for cloud security, including typical access control and data accessibility;
  • Inadequate access control: When an individual with limited access accidentally gains access to confidential information;
  • Mismanaged data access – leaving sensitive data without security measures for accessing it.

Here are some tips on avoiding such a scenario:

  • When setting up a specific cloud server, double-check the settings for cloud security. Even though this seems like an obvious tip, people tend to gloss over it in favor of supposedly more pressing matters like putting goods in storage without thoroughly dealing with cybersecurity.
  • Check security settings using specialized tools. Third-party tools from trusted providers can be used to periodically monitor the condition of security settings and spot potential issues before they become serious.
Data Security

Data Loss

Since it is simple to lose track of how much data you are storing, constant monitoring is necessary to ensure data security.

Data loss may occur in some situations where users don’t have adequate controls. In the cloud, data loss does not always equate to data being forever lost. The user just might not have access to this sensitive information for a variety of reasons. Lack of data backups, automatic data loss controls, and even audits and risk assessments can all result in data loss in the cloud.

Data Breaches

A data breach poses a cause-and-effect risk to data security. If a data breach occurs, it signifies that the business failed to address some cloud security issues, which then had a causal effect.

An incident where information is accessed and retrieved without authority is called a data breach. Typically, this incident causes a data leak.

Although classified info can be made available to the public, it is typically sold illegally or kept hostage by cybercriminals.

The event itself is a stain on a company’s reputation, even though the severity of the effects depends on the particular company’s crisis management capabilities.

Final Word

Proper data security in the cloud has typically proven difficult and ineffective. However, there are ways to simplify your approach to cloud security, particularly if you select a reliable managed service provider.

Businesses will continue to move to cloud infrastructure as remote working becomes more common. Because of this, it is more important than ever for enterprises to have a solid, trustworthy, and comprehensive cloud security policy in place to host a safe and secure cloud infrastructure. Having a plan in place can help businesses in avoiding overspending or underspending on cloud security measures.

Data Collection And Data Selling Technology Webinar

Data Collection And Data Selling Technology Webinar

Hello, everyone! Welcome to new webinar at “Tea Time With Demakis”. In this webinar we will be talking about Data Collection And Data Selling Technology.

In this webinar, we turn to big data. Specifically, we’ll give you a brief overview of data collection and data selling technology today. We’ll address the following questions:

  • What is big data?
  • How does it affect your personal information?
  • Why and how do companies collect big data?

So if you want to know why companies like Amazon, Google, and Facebook think data is more valuable than oil, you’ll find the answer here.

If you’d like to learn more about innovative and emerging technology, please follow Demakis Technologies and continue reading about it on our blog.

Data Mining

Data Mining

We’ve recently discussed data collection and data-selling technology on our blog. But what happens to big data once you capture it? You have to process it somehow. And that analysis and extraction of information from big data is data mining.

But understanding data mining is also more complex than that. So if you want to know more about this topic, you’ll enjoy this article. Let’s begin.

What is data mining?

Data mining is the process of analyzing large volumes of raw data (data sets) to extract information from it.

Typically, this information includes patterns, irregularities, and connections within that data.

Based on the findings, individuals and organizations can extract value from big data.

In most cases, this means generating statistical forecasts that predict risks, opportunities, and outcomes within the context of that data.

In other words: data mining is the process of finding meaningful information in big data.

How to extract data patterns in statistics?

understanding data mining

Technology is critical if you want to extract meaningful information from data sets. The reason for this is the volume, complexity, and structure of big data.

Typically, the data sets you capture can be:

  • Structured
  • Unstructured
  • Semi-structured

Even with the simplest, structured model, manually analyzing large data sets requires a lot of time and resources.

So instead, researchers use software and innovative technology like artificial intelligence (AI) and machine learning.

These technologies can automatically process and analyze data sets to uncover patterns from them.

You can then use these statistical patterns in the data and apply them practically.

For example, when launching a new product, you’ll want to know what your target audience is, and whether they’ll welcome its arrival.

On the other hand, as huge as big data is, it’s never complete. It’s always provisional. So instead of applying it directly, you may first want to test it against more or other sample data.

In the product launch example, you could examine its effectiveness against existing products through a focus group.

What are some data mining techniques?

what are some data mining techniques

New technologies contributing to data mining are continuing to evolve. As they become more accessible, data miners can use them to adopt them and develop new techniques to extract information from big data.

And according to the International Journal of Computer Applications, there are 16 different data mining techniques in use today:

  1. Data cleaning and preparation
  2. Tracking patterns
  3. Classification
  4. Association
  5. Outlier detection
  6. Clustering
  7. Regression
  8. Prediction
  9. Sequential patterns
  10. Decision trees
  11. Statistical techniques
  12. Visualization
  13. Neural networks
  14. Data warehousing
  15. Long-term memory processing
  16. Machine learning and artificial intelligence

Who can use data mining?

While you’ll need the support of managed tech services, the importance of data mining can be felt across fields and industries.

A data mining example and its common use is science.

Researchers can collect data sets from across their field and use AI and machine learning to analyze and extract crucial results and findings for their research projects (regardless of their location).

But the addition of data mining techniques and algorithms isn’t limited to science alone. And there are many other uses for it in both the private and public sectors.

Here are a few types of data mining uses:

  • People search
  • Credit reporting
  • Market testing
  • Advertising effectiveness
  • Researching political outcomes
  • Risk evaluation
  • And many others
data science news

Successful data mining steps you can take

Now, let’s take a look at how you can effectively apply data mining techniques.

Here’s a quick step-by-step guide on how you can make the best use of data mining:

#1 Choose the project carefully.

If you want to extract maximum value from big data, align your data mining goals with your top business goal.

When you know which information you need out of big data, it’s easier to collect, process, and analyze the right data to acquire it.

#2 Collect a lot of data from multiple sources

This is straightforward. The more data sets you use, the more varied the data is, and the greater the accuracy you’ll achieve for your forecasts using that information.

This step has the biggest impact on user behavior analytics and predictive analytics.

#3 Simplify your sampling strategy

Even when you use powerful data mining platforms to process large data sets, try analyzing smaller subsets of data instead.

Simplifying samples to make them clear and concise is the key to generating the best outcome from your efforts.

#4 Always use holdout samples

A holdout sample is a benchmark. It’s a reference point that you can use to evaluate the validity of your predictive models.

This ensures that your predictions aren’t based on other predictive patterns from a defined set of data. But, instead, on actual estimates from the real world.

#5 Refresh your models frequently

Once you generate a forecast or data prediction, start applying it to your research, business, or operations. But don’t hold onto it forever.

These models are only as good as the relevance of the patterns that you find. And as the data changes, it will affect the validity of your forecasts.

That’s why it’s essential to feed new data to the models every week, day, or even hour.

If you’d like to learn more about how Demakis Technologies can help you manage your data, contact us.

best websites to collect data from

Data Collection and Data Selling Technology

In this post, we turn to big data. Specifically, we’ll give you a brief overview of data collection and data selling technology today. We’ll address the following questions:

  • What is big data?
  • How does it affect your personal information?
  • Why and how do companies collect big data?

So if you want to know why companies like Amazon, Google, and Facebook think data is more valuable than oil, you’ll find the answer here. Let’s begin.

What is big data?

data collection services

Big data describes large volumes of data (or data sets) that are so huge and complex (and continue growing exponentially over time) it’s impossible to manage or process them using traditional software.

Typically, these data sets contain publicly available or privately permitted information about human behaviors and interactions online.

When the data is processed, it can generate statistics which identify patterns and trends among those activities.

What is publicly available personal information?

Publicly available data refers to information about a person that is disclosed to the public. But this isn’t always the case.

Data privacy remains an open topic since many .com companies regularly capture information without consent.

For example, Facebook had to pay a record $5 billion to settle a privacy concerns case in 2019.

These events and others like it have prompted nations and international organizations to create laws that protect personal data.

One such legislature is the EU General Data Protection Regulation. In this document, we can find the answer to the main question of this paragraph.

Data Collection and Data Selling Technology

And according to the GDPR, personal information is publicly available:

  • If it’s contained in official documents of public interest, or related to public officials;
  • If it contains the source of the personal data with permission for public disclosure.

What kind of data collection is there?

Currently, there are three types of big data:

  1. Structured: formatted data that can be stored, accessed, and processed.
  2. Unstructured: complex (and usually huge) data sets without form or structure.
  3. Semi-structured: data sets with a structured form that is unintelligible through that structure.

Why do companies collect data?

As a consumer, you may ask yourself: what are companies doing with my data?

Usually, companies capture data for one of two reasons.

The first has to do with user behavior analytics. Businesses want to get a deeper level of insight into how consumers interact with their brand, marketing, products, and services.

Companies will use a statistical representation of this behavior to align their sales and marketing strategies. The goal here is to use big data to persuade consumers to interact with this company instead of its competitors.

how to collect big data

The second reason companies use data is to create future forecasts, so they can uncover risks, trends, and new market opportunities. This is called predictive analytics.

Predictive analytics relies on several statistical techniques such as predictive modelling and machine learning. Companies use these solutions to extract value from present data and align it with their future business goals.

How to collect big data?

Companies can collect data in many ways and from many sources. Some capturing methods are technical, for example, website cookies. Others are more deductive, like Google Analytics.

That said, there are three ways companies can collect data:

  • By directly asking users to provide data
  • By indirectly tracking user behavior
  • By sourcing data from third parties

The most obvious way businesses collect data is through interaction with their websites.

Here, companies typically deploy all three strategies that we’ve listed.

For example, companies can use gated content to capture email addresses with user permission, or third-party software to create website heatmaps that track cursor movement on a web page.

Here are a few other big data collection methods:

  • Loyalty cards (retail and e-commerce websites)
  • Browser games (World of Tanks, Words with Friends)
  • Online gameplay (Fortnite, League of Legends)
  • Satellite imagery (Google Earth, Google Maps)
  • Employer databases (HR and headhunter databanks)
  • Popular email services (Gmail, Yahoo Mail)
  • Social media platforms (Facebook, LinkedIn, Instagram)
  • Ratings and feedback (online surveys, Google reviews)

Note: Companies tend to use managed services to protect their technology systems when capturing data.

Besides collecting information for business use, it’s common to see companies trade data either via data marketplaces or consumer data vendors.

Data Selling Technology

Personal data and big data are routinely bought and sold by companies. Data brokers are those who facilitate these deals.

The brokerage of data includes:

  • People search (Spokeo, ZoomInfo, White Pages, PeopleSmart)
  • Credit reporting (Equifax, Experian, TransUnion)
  • Advertising and marketing (Acxiom, Oracle, Innovis, KBM)
  • Political consultancy (Cambridge Analytica)
  • Risk mitigation

Before monetizing the data, data brokers use advanced technology to acquire, store, access, and process big data sets.

For example, data brokers typically use large private clouds to store these data sets. They can then use a combination of AI and machine learning to process the data to extract value and meaning for their customers.

What is the future of data collection?

Big data is here to stay. Companies will continue capturing data and using it to understand consumers and make predictions about future markets.

We’re still unsure how new privacy laws will affect big data. Or which new technologies will emerge to simplify data processing. All you can really do is stay informed.

If you’d like to learn more about innovative and emerging technology, please follow Demakis Technologies and continue reading about it on our blog.