Data mining isn’t 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.
We’ve 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.
Key Data Mining Concepts
Achieving the best results from data mining requires an array of tools and techniques. Some of the most commonly-used functions include:
Artificial intelligence (AI) — These systems perform analytical activities associated with human intelligence such as planning, learning, reasoning, and problem solving.
Association rule learning — These tools, also known as market basket analysis, search for relationships among variables in a dataset, such as determining which products are typically purchased together.
Clustering — A process of partitioning a dataset into a set of meaningful sub-classes, called clusters, to help users understand the natural grouping or structure in the data.
Classification — This technique assigns items in a dataset to target categories or classes with the goal of accurately predicting the target class for each case in the data.
Data analytics — The process of evaluating digital information into useful business intelligence.
Data warehousing — A large collection of business data used to help an organization make decisions. It is the foundational component of most large-scale data mining efforts.
Machine learning — A computer programming technique that uses statistical probabilities to give computers the ability to “learn” without being explicitly programmed.
Regression — A technique used to predict a range of numeric values, such as sales, temperatures, or stock prices, based on a particular data set.
Advantages of Data Mining
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:
- 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 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 dig through terabytes of data in minutes or hours, rather than days or weeks, helping them innovate and grow faster.
Fundamentals of Machine Learning now.
How Data Mining Works
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:
- Business understanding — Developing 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 — Determining the data that will be needed to solve the problem and gathering it from all available sources.
- Data preparation — Preparing the data in the appropriate format to answer the business question, fixing any data quality problems such as missing or duplicate data.
- Modeling — Using algorithms to identify patterns within the data.
- Evaluation — Determining whether and how well the results delivered by a given model will help achieve the business goal. There is often an iterative phase to find the best algorithm in order to achieve the best result.
- Deployment — Making the results of the project available to decision makers.
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.
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 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.
- Bayer helps farmers with sustainable food production — Weeds that damage crops have been a problem for farmers since farming began. A proper solution is to apply a narrow spectrum herbicide that effectively kills the exact species of weed in the field while having as few undesirable side effects as possible. But to do that, farmers first need to accurately identify the weeds in their fields. Using Talend Real-time Big Data, Bayer Digital Farming developed WEEDSCOUT, a new application farmers can download free. The app uses machine learning and artificial intelligence to match photos of weeds in a Bayer database with weed photos farmers send in. It gives the grower the opportunity to more precisely predict the impact of his or her actions such as, choice of seed variety, application rate of crop protection products, or harvest timing.
- 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.
The Future of Data Mining
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. We’ll 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.
O’Reilly Report: The Internet of Things Market now.
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.
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 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:
- Incorporates best practices for their industry or type of project. Healthcare organizations, for example, have different needs than e-commerce companies.
- Manages the entire data mining lifecycle, from data exploration to production.
- Aligns with the enterprise applications, including BI systems, CRM, ERP, financial, and other enterprise software it must interoperate with for maximum return on investment.
- Integrates with leading open source languages, providing developers and data scientists with the flexibility and collaboration tools to create innovative applications.
- 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 and 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 1/5th the cost of other solutions.
Take a look at how to get started with Talend’s Big Data tools.