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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
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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.