What is the process of extracting information to identify patterns trends and useful data that would allow the business to take the data-driven decision from huge sets of data?

Learn about data mining, which combines statistics and artificial intelligence to analyze large data sets to discover useful information.

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Data mining process
Data mining techniques
Data mining applications
Sales and marketing
Operational optimization
Fraud detection
Data mining and IBM
How data mining works
Advantages of data mining
Data mining use cases and examples
Key data mining concepts
Data processes
Computer science concepts
Data mining techniques
The future of data mining
Data mining software and tools
Getting started with data mining

Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets. Given the evolution of data warehousing technology and the growth of big data, adoption of data mining techniques has rapidly accelerated over the last couple of decades, assisting companies by transforming their raw data into useful knowledge. However, despite the fact that that technology continuously evolves to handle data at a large-scale, leaders still face challenges with scalability and automation.

Data mining has improved organizational decision-making through insightful data analyses. The data mining techniques that underpin these analyses can be divided into two main purposes; they can either describe the target dataset or they can predict outcomes through the use of machine learning algorithms. These methods are used to organize and filter data, surfacing the most interesting information, from fraud detection to user behaviors, bottlenecks, and even security breaches.

When combined with data analytics and visualization tools, like Apache Spark, delving into the world of data mining has never been easier and extracting relevant insights has never been faster. Advances within artificial intelligence only continue to expedite adoption across industries.

Data mining process

The data mining process involves a number of steps from data collection to visualization to extract valuable information from large data sets. As mentioned above, data mining techniques are used to generate descriptions and predictions about a target data set. Data scientists describe data through their observations of patterns, associations, and correlations. They also classify and cluster data through classification and regression methods, and identify outliers for use cases, like spam detection.

Data mining usually consists of four main steps: setting objectives, data gathering and preparation, applying data mining algorithms, and evaluating results.

1. Set the business objectives: This can be the hardest part of the data mining process, and many organizations spend too little time on this important step. Data scientists and business stakeholders need to work together to define the business problem, which helps inform the data questions and parameters for a given project. Analysts may also need to do additional research to understand the business context appropriately.

2. Data preparation: Once the scope of the problem is defined, it is easier for data scientists to identify which set of data will help answer the pertinent questions to the business. Once they collect the relevant data, the data will be cleaned, removing any noise, such as duplicates, missing values, and outliers. Depending on the dataset, an additional step may be taken to reduce the number of dimensions as too many features can slow down any subsequent computation. Data scientists will look to retain the most important predictors to ensure optimal accuracy within any models.

3. Model building and pattern mining: Depending on the type of analysis, data scientists may investigate any interesting data relationships, such as sequential patterns, association rules, or correlations. While high frequency patterns have broader applications, sometimes the deviations in the data can be more interesting, highlighting areas of potential fraud.

Deep learning algorithms may also be applied to classify or cluster a data set depending on the available data. If the input data is labelled (i.e. supervised learning), a classification model may be used to categorize data, or alternatively, a regression may be applied to predict the likelihood of a particular assignment. If the dataset isn’t labelled (i.e. unsupervised learning), the individual data points in the training set are compared with one another to discover underlying similarities, clustering them based on those characteristics.

4. Evaluation of results and implementation of knowledge: Once the data is aggregated, the results need to be evaluated and interpreted. When finalizing results, they should be valid, novel, useful, and understandable. When this criteria is met, organizations can use this knowledge to implement new strategies, achieving their intended objectives.

Data mining techniques

Data mining works by using various algorithms and techniques to turn large volumes of data into useful information. Here are some of the most common ones:

Association rules: An association rule is a rule-based method for finding relationships between variables in a given dataset. These methods are frequently used for market basket analysis, allowing companies to better understand relationships between different products. Understanding consumption habits of customers enables businesses to develop better cross-selling strategies and recommendation engines.

Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold), and an output. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent. When the cost function is at or near zero, we can be confident in the model’s accuracy to yield the correct answer.

Decision tree: This data mining technique uses classification or regression methods to classify or predict potential outcomes based on a set of decisions. As the name suggests, it uses a tree-like visualization to represent the potential outcomes of these decisions.

K- nearest neighbor (KNN): K-nearest neighbor, also known as the KNN algorithm, is a non-parametric algorithm that classifies data points based on their proximity and association to other available data. This algorithm assumes that similar data points can be found near each other. As a result, it seeks to calculate the distance between data points, usually through Euclidean distance, and then it assigns a category based on the most frequent category or average.

Data mining applications

Data mining techniques are widely adopted among business intelligence and data analytics teams, helping them extract knowledge for their organization and industry. Some data mining use cases include:

Sales and marketing

Companies collect a massive amount of data about their customers and prospects. By observing consumer demographics and online user behavior, companies can use data to optimize their marketing campaigns, improving segmentation, cross-sell offers, and customer loyalty programs, yielding higher ROI on marketing efforts. Predictive analyses can also help teams to set expectations with their stakeholders, providing yield estimates from any increases or decreases in marketing investment.

Education

Educational institutions have started to collect data to understand their student populations as well as which environments are conducive to success. As courses continue to transfer to online platforms, they can use a variety of dimensions and metrics to observe and evaluate performance, such as keystroke, student profiles, classes, universities, time spent, etc.

Operational optimization

Process mining leverages data mining techniques to reduce costs across operational functions, enabling organizations to run more efficiently. This practice has helped to identify costly bottlenecks and improve decision-making among business leaders.

Fraud detection

While frequently occurring patterns in data can provide teams with valuable insight, observing data anomalies is also beneficial, assisting companies in detecting fraud. While this is a well-known use case within banking and other financial institutions, SaaS-based companies have also started to adopt these practices to eliminate fake user accounts from their datasets.

Data mining and IBM

Partner with IBM to get started on your latest data mining project. IBM Watson Discovery digs through your data in real-time to reveal hidden patterns, trends and relationships between different pieces of content. Use data mining techniques to gain insights into customer and user behavior, analyze trends in social media and e-commerce, find the root causes of problems and more. There is untapped business value in your hidden insights. Get started with IBM Watson Discovery today.

Sign up for a free Watson Discovery account on IBM Cloud, where you gain access to apps, AI and analytics and can build with 40+ Lite plan services.

To learn more about how IBM’s data warehouse solution, sign up for an IBMid and create your free IBM Cloud account today.

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 works

Any 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:

Business understanding. Develop a thorough understanding of the project parameters, including the current business situation, the primary business objective of the project, and the criteria for success.
Data understanding. Determine the data that will be needed to solve the problem and gather it from all available sources.
Data preparation. Get the data ready for analysis. This includes ensuring that the data is in the appropriate format to answer the business question, and fixing any data quality problems such as missing or duplicate data.
Modeling. Use algorithms to identify patterns within the data and apply those patterns to a predictive model.
Evaluation. Determine whether and how well the results delivered by a given model will help achieve the business goal. There is often an iterative phase in which the algorithm is fine-tuned in order to achieve the best result.
Deployment. Run the analysis and make the results of the project available to decision makers.

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.

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Advantages of data mining

Data 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:

Increasing revenue
Understanding customer segments and preferences
Acquiring new customers
Improving cross-selling and up-selling
Retaining customers and increasing loyalty
Increasing ROI from marketing campaigns
Detecting and preventing fraud
Identifying credit risks
Monitoring operational performance

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 examples

Organizations across industries are achieving transformative results from data mining:

Groupon aligns marketing activities — One of Groupon’s key challenges is processing the massive volume of data it uses to provide its shopping service. Every day, the company processes more than a terabyte of raw data in real time and stores this information in various database systems. Data mining allows Groupon to align marketing activities more closely with customer preferences, analyzing that 1 terabyte of customer data in real time and helping the company identify trends as they emerge.
Air France KLM caters to customer travel preferences — The airline uses data mining techniques to create a 360-degree customer view by integrating data from trip searches, bookings, and flight operations with web, social media, call center, and airport lounge interactions. They use this deep customer insight to create personalized travel experiences.
Domino’s helps customers build the perfect pizza — The largest pizza company in the world collects 85,000 structured and unstructured data sources, including point of sales systems and 26 supply chain centers, and through all its channels, including text messages, social media, and Amazon Echo. This level of insight has improved business performance while enabling one-to-one buying experiences across touchpoints.

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 concepts

Achieving 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 processes

The first batch of concepts relate to the data itself, and how it is moved and managed.

Data cleansing and preparation. Raw data flows in from any number of sources in a wild mix of formats and quality. Before it can be used in any meaningful way, that data must be transformed from its raw state into a format that’s more suitable for analysis and processing. This includes processes such as identifying and removing errors, calling out missing data, and flagging outliers.
Data warehousing. Unless you are working with only a small subset of data, you will probably need to collect data from a range of sources combine it into a single data repository before you can use data to make decisions. This repository is generally known as a data warehouse. It is the foundational component of most large-scale data mining efforts.
Data analytics. Once your data has been cleaned and collected, you can start examining it for past trends that could be applied to future decision-making. The process of evaluating historical digital information to provide useful business intelligence is known as data analytics.
Predictive analytics. Where data analytics looks to the past to identify trends, predictive analytics uses that data to anticipate future outcomes. Predictive analytics relies on data modeling, machine learning, and artificial intelligence to uncover patterns in big data.

Computer science concepts

Next, 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.

Artificial intelligence (AI). With modern technology, automated systems can perform analytical activities that used to be possible only by applying human intelligence. These activities can include things like planning, learning, reasoning, and problem solving. When it comes to data mining, this refers to using a computer program to identify meaningful trends in the data.
Machine learning (ML). The earliest computers needed an explicit program to instruct them through any process, step by step — but that assumes that the programmer is already aware of every possible scenario that may arise. More recently, programmers use statistical probabilities to write machine learning algorithms that give computers the ability to “learn” and adapt without being explicitly programmed.
Natural language processing (NLP). Many valuable data sources, such as social media, aren’t easily broken down into simple fields. Natural language processing is a feature of AI that gives a computer program the ability to “read” and understand casual or unstructured data sources.
Neural networks. Sometimes a single machine learning algorithm isn’t powerful enough to do the job alone. A neural network is a collection of algorithms that work together to solve more complex problems, thinking more like a human brain. Just like a simple machine learning algorithm, neural networks have the ability to learn and adapt.

Data mining techniques

There are many techniques used by data mining technology to make sense of your business data. Here are a few of the most common:

Association rule learning. Also known as market basket analysis, association rule learning looks for interesting relationships between variables in a dataset that might not be immediately apparent, such as determining which products are typically purchased together. This can be incredibly valuable for long-term planning.
Classification. This technique sorts items in a dataset into different target categories or classes based on common features. This allows the algorithm to neatly categorize even complex data cases.
Clustering. To help users understand the natural groupings or structure within the data, you can apply the process of partitioning a dataset into a set of meaningful sub-classes called clusters. This process looks at all the objects in the dataset and groups them together based on similarity to each other, rather than on predetermined features.
Decision trees. Another method for categorizing data is the decision tree. This method asks a series of cascading questions to sort items in the dataset into relevant classes.
Regression. This technique is used to predict a range of numeric values, such as sales, temperatures, or stock prices, based on a particular data set.

The future of data mining

We 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 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 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:

It incorporates best practices for their industry or type of project — for example, healthcare organizations have different needs than e-commerce companies.
It manages the entire data mining lifecycle, from data exploration to production.
It aligns with all enterprise applications, including BI systems, CRM, ERP, financial systems, and other enterprise software.
It integrates with leading open source languages, providing developers and data scientists with the flexibility and collaboration tools to create innovative applications.
It meets the needs of IT, data scientists, and analysts, while also serving the reporting and visualization needs of business users.

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 mining

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 fraction of the cost of other solutions.

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