Show The concept of data mining has been with us since long before the digital age. The idea of applying data to knowledge discovery has been around for centuries, starting with manual formulas for statistical modeling and regression analysis. In the 1930s, Alan Turing introduced the idea of a universal computing machine that could perform complex computations. This marked the rise of the electromechanical computer — and with it, the ever-expanding explosion of digital information that continues to this very day. We’ve come a long way since then. Data has become a part of every facet of business and life. Companies today can harness data mining applications and machine learning for everything from improving 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 than ever before. Data mining is the process of analyzing massive volumes of data to discover business intelligence that can help companies solve problems, mitigate risks, and seize new opportunities. This branch of data science derives its name from the similarities between the process of searching through large datasets for valuable information and the process of mining a mountain for precious metals, stones, and ore. Both processes require sifting through tremendous amounts of raw material to find hidden value. Data mining can answer business questions that were traditionally impossible to answer because they were too time-consuming to resolve manually. Using powerful computers and algorithms to execute a range of statistical techniques that analyze data in different ways, users can identify patterns, trends, and relationships they might otherwise miss. Theycan then 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 give you an advantage over competitors by making it possible to learn more about customers, develop effective marketing strategies, increase revenue, and decrease costs. How data mining worksAny data mining project must start by establishing the business question you are trying to answer. Without a clear focus on a meaningful business outcome, you could find yourself poring over the same set of data over and over without turning up any useful information at all. Once you have clarity on the problem you are trying to solve, it’s time to collect the right data to answer it — usually by ingesting data from multiple sources into a central data lake or data warehouse — and preparing that 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. For a successful data mining process that delivers timely, reliable results, you should follow a structured, repeatable approach. Ideally, that process will include the following six steps:
Throughout this process, close collaboration between domain experts and data miners is essential to understand the significance of data mining results to the business question being explored. Learn how Talend runs its business on trusted data Get the ebookAdvantages of data miningData is pouring into your businesses every day from a dazzling array of sources, in a multitude of formats, and at unprecedented speed and volumes. Deciding whether or not to be a data-driven business is no longer an option; your business’ success depends on how quickly you can discover insights from big data and incorporate them into business decisions and processes to drive better actions across your enterprise. However, with so much data to manage, this can seem like an insurmountable task. Data mining gives businesses an opportunity to optimize operations for the most likely future by understanding the past and present, and making accurate predictions about what is likely to happen next. For example, sales and marketing teams can use data mining to predict which prospects are likely to become profitable customers. Based on past customer demographics, they can establish a profile of the type of prospect who would be most likely to respond to a specific offer. With this knowledge, they can increase return on investment (ROI) by targeting 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 automate these processes to dig through terabytes of data in minutes or hours, rather than days or weeks, helping them innovate and grow faster. Data mining use cases and examplesOrganizations 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. Key data mining conceptsAchieving the best results from data mining requires an array of tools and techniques. Some are probably already familiar, but others might be new to you. Here are a few of the most common terms and concepts in the field of data mining. Data processesThe first batch of concepts relate to the data itself, and how it is moved and managed.
Computer science conceptsNext, you should be familiar with some common computer science terms that describe how various programs and algorithms interact with the data to deliver meaningful insights.
Data mining techniquesThere are many techniques used by data mining technology to make sense of your business data. Here are a few of the most common:
The future of data miningWe are living in a world of data. The volume of data that we create, copy, use, and store is growing exponentially. We’ve already crossed the threshold of creating 1.7 megabytes of new information every second for every human being on the planet. That means that the future is bright for data mining and data science. With so much data to sort through, we are going to need ever more sophisticated methods and models to draw meaningful insights and fuel business decision making. 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, the Internet of Things (IoT) 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. By 2020, there were already more than 20 billion connected devices on the Internet of Things. 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. With data pouring in from sales, marketing, the web, production and inventory systems, and more, 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 accelerate data collection, compile, and prepare that data, then 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. Data mining software and toolsThere 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 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 meets the following criteria:
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 for a competitive advantage. Getting started with data miningAs 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 fraction of the cost of other solutions. Take a look at how to get started with Talend's Big Data tools. |