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Data mining isnt a new invention that came with the digital age. The concept has been around for over a century, but came into greater public focus in the 1930s. One of the first instances of data mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that could perform computations similar to those of modern-day computers.

Weve come a long way since then. Businesses are now harnessing data mining and machine learning to improve everything from their sales processes to interpreting financials for investment purposes. As a result, data scientists have become vital to organizations all over the world as companies seek to achieve bigger goals with data science than ever before.

Data mining is the process of analyzing massive volumes of data to discover business intelligence that helps companies solve problems, mitigate risks, and seize new opportunities. This branch of data science derives its name from the similarities between searching for valuable information in a large database and mining a mountain for ore. Both processes require sifting through tremendous amounts of material to find hidden value.

Data mining can answer business questions that traditionally were too time consuming to resolve manually. Using a range of statistical techniques to analyze data in different ways, users can identify patterns, trends and relationships they might otherwise miss. They can apply these findings to predict what is likely to happen in the future and take action to influence business outcomes.

Data mining is used in many areas of business and research, including sales and marketing, product development, healthcare, and education. When used correctly, data mining can provide a profound advantage over competitors by enabling you to learn more about customers, develop effective marketing strategies, increase revenue, and decrease costs.

Achieving the best results from data mining requires an array of tools and techniques. Some of the most commonly-used functions include:

Data is pouring into businesses in a multitude of formats at unprecedented speeds and volumes. Being a data-driven business is no longer an option; the business success depends on how quickly you can discover insights from big data and incorporate them into business decisions and processes, driving better actions across your enterprise. However, with so much data to manage, this can seem like an insurmountable task.

Data mining empowers businesses to optimize the future by understanding the past and present, and making accurate predictions about what is likely to happen next.

For example, data mining can tell you which prospects are likely to become profitable customers based on past customer profiles, and which are most likely to respond to a specific offer. With this knowledge, you can increase your return on investment (ROI) by making your offer to only those prospects likely to respond and become valuable customers.

You can use data mining to solve almost any business problem that involves data, including:

Through the application of data mining techniques, decisions can be based on real business intelligence rather than instinct or gut reactions and deliver consistent results that keep businesses ahead of the competition.

As large-scale data processing technologies such as machine learning and artificial intelligence become more readily accessible, companies are now able to dig through terabytes of data in minutes or hours, rather than days or weeks, helping them innovate and grow faster.

A typical data mining project starts with asking the right business question, collecting the right data to answer it, and preparing the data for analysis. Success in the later phases is dependent on what occurs in the earlier phases. Poor data quality will lead to poor results, which is why data miners must ensure the quality of the data they use as input for analysis.

Data mining practitioners typically achieve timely, reliable results by following a structured, repeatable process that involves these six steps:

Throughout this process, close collaboration between domain experts and data data miners is essential to understand the significance of data mining results to the business question being explored.

Organizations across industries are achieving transformative results from data mining:

These are just a few examples of how data mining capabilities can help data-driven organizations increase efficiency, streamline operations, reduce costs and improve profitability.

The future is bright for data mining and data science as the amount of data will only increase. By 2020, our accumulated digital universe of data will grow from 4.4 zettabytes to 44 zettabytes. Well also create 1.7 megabytes of new information every second for every human being on the planet.

Just like mining techniques have evolved and improved because of improvements in technology, so too have technologies to extract valuable insights out of data. Once upon a time, only organizations like NASA could use their supercomputers to analyze data the cost of storing and computing data was just too great. Now, companies are doing all sorts of interesting things with machine learning, artificial intelligence, and deep learning with cloud-based data lakes.

For example, Internet of Things and wearable technology have turned people and devices into data-generating machines that can yield unlimited insights about people and organizations if companies can collect, store, and analyze the data fast enough.

There will be about >20 billion connected devices on the Internet of Things (IoT) by 2020. The data generated by this activity will be available on the cloud, creating an urgent need for flexible, scalable analytics tools that can handle masses of information from disparate datasets.

Cloud-based analytics solutions are making it more practical and cost-effective for organizations to access massive data and computing resources. Cloud computing helps companies quickly gather data from sales, marketing, the web, production and inventory systems, and other sources; compile and prepare it; analyze it; and act on it to improve outcomes.

Open source data mining tools also afford users new levels of power and agility, meeting analytical demands in ways many traditional solutions cannot and offering extensive analyst and developer communities where users can share and collaborate on projects. In addition, advanced technologies such as machine learning and AI are now within reach for just about any organization with the right people, data, and tools.

There is no doubt that data mining has the power to transform enterprises; however, implementing a solution that meets the needs of all stakeholders can frequently stall platform selection. The wide range of options available to analysts, including open source languages such as R and Python and with familiar tools like Excel, combined with the diversity and complexity of tools and algorithms, can further complicate the process.

Businesses that gain the most value from data mining typically select a platform that:

The Talend Big Data Platform provides a complete suite of data management and data integration capabilities to help data mining teams respond more quickly to the needs of their business.

Based on an open, scalable architecture and with tools for relational databases, flat files, cloud apps, and platforms, this solution complements your data mining platform by putting more data to work in less time which translates into faster time to insight and competitive advantage.

As organizations continue to be inundated with massive amounts of internal and external data, they need the ability to distill that raw material down to actionable insights at the speed their business requires.

Businesses in every industry rely on Talend to help them accelerate insights from data mining. Our modern data integration platform empowers users to work smarter and faster across teams, enabling them to develop and deploy end-to-end data integration jobs ten times faster than hand coding, at 1/5th the cost of other solutions.

Take a look at how to get started with Talend's Big Data tools.

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What is Data Mining? Definition and Examples | Talend

Learning data mining can be a complicated process, and its not easy to know where to start. As a result, our editors have compiled this list of the best data mining tutorials on YouTube to help you learn about the topic and hone your skills before you move on to mastering it. All of the videos here are free to access and feature guidance from some of the top minds and biggest brands in the online learning community. All of the best deep learning tutorials listed tout a minimum of 20,000 views.

Note: Dont forget to subscribe to Solutions Review on YouTube!

Author: Edureka

Description: This Edureka R tutorial on data mining using R will help you understand the core concepts of data mining. This tutorial will is also comprised of a case study using R, where youll apply data mining operations on a real-life data-set and extract information from it.

Author:Red Team Cyber Security

Description: With nearly 140,000 views, this brief (its only five-and-a-half-minutes long) tutorial will help you come up to speed on a complex topic quickly. Though its shorter than what wed normally aim to share in a resource like this, Red Team Cyber Security does a good job of explaining the topic in a way that the novice will understand.

Author: Geeks Lesson

Description: In this data mining course, you will learn how to do data mining tasks with Weka. This data mining tutorial has been designed for beginners. It will walk you through the data mining process with Weka, one of the most popular and widely used data mining tools available. With more than 60,000 lifetime YouTube views, this is one of the most popular videos on the topic on the web.

Author: Great Learning

Description: This is a 10-plus-hour data mining training tutorial from the folks at Great Learning. It will take you from the starting point through the finishing point of everything you need to know about this domain and getting started on the journey to master it. This video starts off with an introduction to Python, followed by understanding a variety of Python libraries.

Author: Great Learning

Description: This data mining tutorial from the folks at Great Learning covers a broad definition of the topic, an outline of the kdd process, different data mining algorithms (like regression, classification, and clustering), and offers a demo of data mining using R and Python.

Author: Geeks Lesson

Description: This Data Mining with Orange tutorial helps you understand the process of uncovering data-based insights using an open-source data visualization and machine learning toolkit. The video showcases a visual programming front-end for explorative data analysis and interactive data visualization.

Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.

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The 6 Best Data Mining Tutorials on YouTube to Watch Right Now - Solutions Review