Brittany Kaiser, former Director of Business Development for Cambridge Analytica, stated in Netflix’s The Great Hack that data is now more valuable than oil.
And just like oil, gold, ore, and other natural resources, there’s hidden value in data that needs to be mined and extracted. This process is referred to as data mining.
What is data mining?
Data mining is commonly referred to as knowledge discovery within databases. It’s about sifting through massive datasets to uncover patterns, trends, and other truths about data that aren’t initially visible using machine learning, statistics, and database systems.
While this term is relatively new (first coined in the 1990s), it’s becoming more common as organizations across all industries are using it to gain further insight about how they can better their businesses.
Need to know something specific about data mining? Jump ahead to:
How data mining works
Why data mining is important
How data mining is used
Data mining examples
Understanding text mining
Challenges of data mining
The future of data mining
Introduction to data mining
A lot goes into understanding a complex topic like data mining, and even more goes into how each industry is able to use it to increase revenue, cut costs, improve relationships with customers, and so much more.
The results of data mining are analyzed, tested, and applied to reach a solution in the form of data analytics. In short, data mining is akin to finding a needle in a haystack. Data mining is conducted using machine learning software that discovers algorithms and statistics. These methods help reduce ‘noise’ in databases to extract useful information.
The overall foundation that makes up data mining is three scientific disciplines. First, there’s statistics, which is the study of numeric data relationships. Then there’s artificial intelligence, the human-like intelligence that software and some machines display. Finally, data mining also uses machine learning, which is the algorithms used to learn from the data and make accurate predictions.
It’s in the best interest of your company to check out the types of machine learning software on the market that you can utilize to improve process efficiency and effectiveness, speed up analysis, and embed artificial intelligence within the application.
Just because data mining allows for more useful information, that doesn’t necessarily coincide with more knowledge. To ensure that you’re making the most out of this new information, data mining needs to:
- Be able to sift through and organize all of the chaotic and repetitive noise that your data can hold
- Be able to distinguish between what is relevant and then take the steps to use that data to assess the likely outcomes
- Speed up the pace of informed decision making
How does data mining work?
The process of data mining consists of exploring and analyzing large sums of information with the intention of discovering meaningful patterns and trends. Doing so is essentially broken down into a five step process.
- An organization will collect data and load it into a data warehouse.
- This data will be stored and managed either on in-house servers or the cloud. Data visualization tools use this step to explore the properties of the data to ensure it will help achieve the goals of the business.
- Gather the business analysts, management teams, and information technology professionals at your organization to access the data and determine the ways they’d like to organize it.
- Application software tools will sort the data based on the results and will use data modeling and mathematical models to find patterns in the data.
- Data will be presented in a readable and shareable format, such as a graph or table, created using business intelligence platform, and shared across everyday business operations as a single source of truth.
Going through this process doesn’t help anyone if the data you collect goes untouched. The right business intelligence (BI) platform breaks down the data to a granular level, allowing your team to dig into the data to create forecasts, strategies, and actionable insights.
If your business isn’t already utilizing business intelligence platforms, there’s no better time than the present. Unsure of which platform is right for your company and its needs? Check out real user reviews from those who use this software every day.
Why is data mining important?
Data mining explores a business’s historical data during the data analysis process to look at past performances or future forecasts. This leads to faster, more efficient decision making.
For example, through data mining, a business may be able to see which customers are buying specific products at certain times of the year. This information can then be used to segment those customers. Customer segmentation is important for targeting sales and marketing campaigns – which may lead to higher profits, but also point toward a potential trend or two.
In addition to automated decision-making, data mining is also an important tool because it can accurately predict and forecast trends for your business based on historical information and current conditions. It also has the capability to allow for more efficient use and allocation of resources so that businesses can plan and make automated decisions to maximize cost reduction.
Everything from business intelligence to big data analytics tools utilize some form of data mining. It’s only a matter of time until businesses have even more use cases for data mining and the insights it can provide.
How is data mining used?
Data mining is used by business professionals across various industries to turn raw data into useful information. This is done by using software to look at patterns and sequences in large batches of data.
For instance, as long as you have effective data collection, warehousing, and computer processing, your business can use data mining to develop effective marketing strategies, decrease costs, and even increase sales among other things.
These programs work to analyze the relationship and patterns in the collected data based on what the user is requesting. Say you own a salon and are interested in using data mining to decide when certain discounts should be offered. Data mining programs would analyze the information it collected based on when customers visit and what service(s) they ask for. You may find that you do more haircuts in the spring and more hair coloring services in the fall, which will help you schedule out appropriate offers during the year.
Warehousing is another element of how data mining is used. Warehousing is when companies consolidate their data into one database or program. Organizations may choose to use a data warehouse to segment their data based on which specific users are going to analyze and use the data in the future. For instance, you may want to segment some data specifically for your sales team and others for your marketing team.
Data mining examples
Businesses across a variety of industries are turning to data mining to gain insights in ways that were once impossible. Below are some examples of how data mining is changing businesses for the better.
Data mining in marketing
Businesses within the marketing industry use data mining to analyze large sums of data to improve marketing segmentation. For instance, when looking at parameters like customer age, gender, location, or other demographic information, data mining makes it possible to guess their customers’ behavior as a direct correlation of these parameters.
It’s also possible to use data mining in marketing to predict which of your users are going to unsubscribe from your email campaigns or services, what interests them based on their site searches , and what your mailing list should include to achieve a higher response rate.
Data mining in retail
Think about how Amazon shows you a selection of products based on what you have searched for or purchased in the past. This is data mining at work. Or think about a product team that is about to pitch an idea for a new pair of running shoes. They may say that men’s running shoes sell better with black packaging versus blue packaging. To prove this, they use a data mining tool to show the historical support of their theory.
We also see data mining being used in supermarkets. Thanks to joint purchasing patterns, supermarkets can identify product associations to gain insights on how to place certain items in the aisles and on the shelves (eye-level or top shelf, for example). They can also use data mining to understand which offers are most valued by their customers to increase sales at checkout.
Data mining in banking
Banks apply data mining techniques to credit ratings and intelligent anti-fraud systems as a way to analyze transactions, purchasing patterns, and the financial data of their customers. They also can use it to learn more about their customers’ online preferences or habits in order to optimize the return on marketing campaigns and study compliance obligations.
An example of this would be when a bank uses dating mining to see that a customer makes the majority of their purchases online. Because of this information, the bank may decide to increase their credit card limit before a major shopping holiday, like Black Friday or Memorial Day.
Data mining in healthcare
The medical industry is perhaps set to benefit the most from data mining as they use it to enable more accurate diagnostics. When a doctor or a medical practitioner has all of a patient’s information, like medical records, treatment patterns, and physical examinations, they can prescribe more effective treatment for diseases.
Data mining also allows those in the medical field a more effective and cost-efficient way to manage health resources as it can identify risks and better forecast the length of hospital admissions for their patients. This would allow better allocation of hospital beds and other vital resources during a patient’s hospital stay.
Data mining in insurance
With further insight into analytics, insurance companies are able to use data mining to solve complex problems that go hand-in-hand with fraud, compliance, risk management, and customer attrition. Insurance companies can also use data mining to better and more accurately price products across their business lines and their existing customer base.
Data mining in manufacturing
When data mining is used in manufacturing, supply plans can be better aligned with demand forecasts, and problem detection is used to their advantage, which are essential parts of the industry. Additionally, data mining in manufacturing can predict wear of production assets as well as predict maintenance, allowing businesses to maximize uptime and keep their production line on schedule.
Data mining in education
When it comes to the education and data mining, teachers can predict student performance before class even starts. It allows instructors to develop intervention strategies to ensure students keep on course. When educators can access student data, predict achievement levels, and pinpoint which students need extra attention, everyone is able to succeed.
Text mining, or text analysis software, is an extension of data mining using natural language processing (NLP) to extract information out of text-heavy unstructured data. This strategy within data mining is being used by airlines to find lost luggage, finance teams within the stock market to track breaking news stories, and allow healthcare professionals to categorize their patients’ medical records.
Here’s an example of how text mining works:
Text-heavy data will first need to be collected and formatted in a uniform way. Text is taken from everything to HTML and XML files to word documents and PDF files using text analysis software. Then embedded image files will be deleted as they serve no value in regards to text mining.
Next, all text that is considered “noise” will be eliminated. This consists of words like “of,” “a,” “the,” and so on.
Words that are synonyms will be unified. Numerical values and percentages will be pulled and formatted in their own ways. Phrases, key terms, sentence structures, and other nuances of the human language will be broken down as well. Now, everything should be as close to structured data as possible.
Challenges of data mining
It’s clear that data mining is a crucial technology in general business. Even though it has developed into an established process, there are still some challenges and hurdles you may experience during the process.
For example, you may experience poor quality of data collection based on data that is noisy, dirty, or contains misplaced or contains incorrect data values. This could be due in part to human error or software failure. Another common issue is redundant data integration from unmarked sources. Redundant data can come in many forms, including numeric data, media files, geolocation, and more.
Data mining is also susceptible to security and privacy concerns. Private and government organizations often run into the hurdle of safe, privacy-protected data mining, seeing as sensitive and private information is often collected for customer profiles and user behavior understanding.
Future of data mining
Text mining is the here and now, but the future of data mining will focus on other forms of unstructured data as well. For example, data from images and videos can be mined for knowledge discovery. There are some frameworks already in place that focus on image, video, and audio mining, but they’re still in very early stages. This is referred to as Multimedia Data Mining.
Semantic Web Mining will also be more prevalent, enabling researchers to find deeper meaning that’s hidden within data on the Web. The semantic Web is essentially an extension of the World Wide Web where data on websites are structured and tagged in a way that’s easier for machines to read.
There’s also Ubiquitous Data Mining, which involves mining data from mobile devices to get information about the user. While this method is still in the works, and will experience challenges regarding privacy and cost, it will open up many opportunities for a multitude of businesses to study how humans interact with computers.
Other elements of data mining we will see in the future are Geographical Data Mining, which involves analyzing information from images taken from outer space. This type of data mining is mainly used to show aspects like distance and topography for navigation applications. There’s also Time Series Data Mining, a strategy used to study cyclical and seasonal trends. It is also used by retail companies to take a better look at customers’ buying patterns and their behaviors.
No amount of data is too vast
From business intelligence to big data analytics, all of the data that companies gather would serve no purpose without knowledge discovery.
Data mining allows businesses to visualize patterns and trends of raw data that may not be initially visible. Whichever insights are revealed will lead to faster, more informed decision making. This is beneficial to both businesses and the customers they serve.
Only time will tell how we as a society find new ways to mine data and discover actionable insights that lead to new ways to conduct business.
Take your learning one step further when you discover the difference between structured versus unstructured data.