What is Data Mining?
The concept of data mining has been with us since long before the digital age. The idaea of applying data to knowledge discovery has been around for centuries, starting with manual formulae for statistical modelling and regression analysis. In the 1930s, Alan Turing introduced the idaea 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 organisations all over the world as companies seek to achieve bigger goals than ever before.
Data mining is the process of analysing 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 analyse data in different ways, users can identify patterns, trends, and relationships they might otherwise miss. They
can 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 dependant 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.
- Modelling. 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.
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 optimise 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
Organisations 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, analysing 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 centre, and airport lounge interactions. They use this deep customer insight to create personalised 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 organisations 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.
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 modelling, 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 programmes 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 programme to identify meaningful trends in the data.
- Machine learning (ML). The earliest computers needed an explicit programme 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 programme 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 categorise 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 categorising 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 organisations like NASA could use their supercomputers to analyse 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 organisations — if companies can collect, store, and analyse 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 organisations to access massive data and computing resources. Cloud computing helps companies accelerate data collection, compile, and prepare that data, then analyse 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 organisation 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 practises for their industry or type of project — for example, healthcare organisations 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 visualisation 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 organisations 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.
Ready to get started with Talend?
More related articles
- What is MySQL? Everything You Need to Know
- What is Middleware? Technology’s Go-to Middleman
- What is Shadow IT? Definition, Risks, and Examples
- What is Serverless Architecture?
- What is SAP?
- What is ERP and Why Do You Need It?
- What is “The Data Vault” and why do we need it?
- What is a Data Lab?
- Understanding Cloud Storage
- What is a Legacy System?
- What is Data as a Service?
- What is a Data Mart?
- What is Data Processing?
- What is Apache Hive?
- Data Munging: A Process Overview in Python
- What is a Data Source?
- Data Transformation Defined
- SQL vs NoSQL: Differences, Databases, and Decisions
- Data Modeling: Ensuring Data You Can Trust
- How modern data architecture drives real business results
- Data Gravity: What it Means for Your Data
- CRM Database: What it is and How to Make the Most of Yours
- Data Conversion 101: Improving Database Accuracy