The state of data mining is eager to improve as we slowly step into the new year. Organizations of all shapes and sizes belonging to both the public and the governmental sector are focusing on digging deeper into organized data to help perfect future investments as well as the customer experience being served.
Data mining is an essential knowledge extraction process that includes both collecting, cleaning, and organizing useful information. Data mining can be efficiently applied not only in a completely business-based environment but additionally in several other fields. Some great and notable examples are healthcare data analysis, weather forecasts, medicine, transportation data analysis and forecasts, expectancy forecasts for insurance companies and the list goes on. There are many benefits of data mining when it is used in a specific industry.
In the current day and age, the data being stored, examined, and organized is ever-expanding. Per the statistics of a recent study, over 20,00,000 search queries are received by Google every minute, over 200 million emails are also sent over the same time period, 48 hours of video on YouTube is also uploaded in the same 60 seconds, around 700,000 types of different content is shared over Facebook in the very same minute, and a little over a 100,000 tweets are being tweeted in the same minute. It’s brilliant how much data is being calculated in the same 60 seconds over several different mediums.
All of this data when added with some other mediums such as news platforms, stock trading platforms and media sharing platforms generate additional data that’s being created as each second passes. It’s amazing to think and realize that only a few years ago several different organizations started collecting such data which is now becoming a game changer in terms of research and analysis for companies all around the world.
Challenges Faced By Data Mining
Though data mining is considered as a powerful information collection practice, it faces several different challenges for and during its implementation. Such challenges can be related to mining methods, data collection, performance etc. To enable different companies around the world in attaining perfectly calculated data for an even perfect and operational execution, these problems need to be addressed and solved. Some of the widely discussed challenges in the world of data mining are as follows
- Poor quality of data collection is one of most known challenges in data mining. Noisy data, dirty data, misplaced data values, inexact or incorrect values, insufficient data size and poor representation in data sampling.
- Redundant data integration from several unmarked sources is another great issue currently being faced by the data mining industry. This data may be in several forms such as: numeric data, media files, social interaction data, or even geo location data.
- Proliferation of security and privacy concerns is another great issue that is slowly increasing for data mining organizations around the world. Both private and government organizations and even individuals around the world are part of raising this genuine concern which is a huge obstacle for safe, privacy protected data mining.
- Another one of the challenges of data mining is dealing with data that exceeds the static boundaries, is cost-sensitive or simply unbalanced.
- One known data mining challenge is caused by consistent updates in data collection models to analyze data velocity or any updated incoming data.
- Difficulty to access different sorts of data and unavailability of certain types of data is another important issue being faced by different sectors. There are several different data factors that are difficult to calculate and organized merely because of the speed bumps in their data collection process.
- Some organizational data challenges come forth when enormous amounts of unstructured data is being structured. Often the data counts are so huge that several issues are faced when organizing them into a structured form. Challenges with manpower, time consumption, and even financial outputs arise with such situations.
- A similar issue arises when an enormous quantity of output from several diverse data mining methods is being collected.
- One of the oldest challenges being faced by the data mining industry is dealing with huge datasets. Analyzing huge data sets at certain times require several different distribution methods which can be a tricky challenge.
- A cost-based data mining challenge arises with the effectively high cost of data collection software and hardware used to accumulate and organize large amounts of data from different informational sectors. For an organization that collects data, this is one of the biggest financial challenge being faced.
While these are the some of the most frequently known problems being faced by the industry there are limits to how wide these challenges extend. While some of these challenges are not widely received, other are. Let us also have a look at some of the widely received challenges in the diverse field of data mining to understand and evaluate just how to derive solutions for these problems.
The process of data mining consists of deriving information from large volumes of data. The data we collect in the real-world is noisy, unstructured and fairly diverse in its fields. In such situations, data in large quantities will be fairly unreliable. These challenges are mostly due to errors in measurement and/or quantification by instruments or simply due to human errors.
Here’s an example to further elaborate the query. Let us suppose that a retail clothing brand decides to collect email ids of their customers for all of their purchases. At some point in time, the clothing brand wants to separate customers that may have made high purchases in the store to send in exclusive discount codes or offers, but to their surprise the data recorded can be severely flawed. Several of the customers would’ve made spelling mistakes or writing errors when entering their email IDs while others may have just entered a wrong email address simply because of privacy concerns. This is a prime example of noisy data.
Distributed or Scattered Data
The data existing in the real world is stored in several different mediums. It could be on the internet, or even protected databases. To bring all the data to a single structure while is a very beneficial data mining goal, but contains a lot of speed bumps in organizational terms. For example, several different geo-located offices owned by the same organization may have their data saved in hundreds of different locations on protected databases. Hence, data mining demands the manpower, the algorithms, and the tools regarding that specific area.
Complex Data Restructuring
The data that exists in the real world also has several different forms. There can be data in text form, numeric form, graphical form, audio form, video form and the list goes on. Extracting the required information from this data and compiling the required information from this diverse and heterogenous mediums of data can be complex.
One of the most essential areas of data mining are the algorithms. The performance of the data mining process ultimately depends on the mining methods and algorithms used. If these mining methods and algorithms are not up to the mark for the task assigned, the result will not be as required and will ultimately affect the end data. This additionally has an effect over the complete campaign.
Background Knowledge Incorporation
Background knowledge is one of the essentials for a proper and perfect data mining technique. Background Knowledge enables the end data of the data mining procedure to be more accurate which is why it plays such as essential role. With background knowledge, predictive tasks can become actual predictions and descriptive tasks can produce more accurate results. However, collecting and implementing background knowledge is a time consuming and difficult process for data mining organizations.
Data Protection and Privacy
One of the most common issues for individuals, and both private and governmental organizations is privacy of data. The field and operations of data mining normally leads to serious data security and protection issues. A great example would be a retail company noting down the grocery list of a customer. This data can be a clear indication of customers interest in several products. This is one of the many reasons hundreds of data mining companies around the world take the most security measures to secure the data being collected.
Latest Trends in Data Mining
As each day passes, Data mining is turning out to be the ultimate swiss army knife for organizations worldwide. Even more reason for us to move forward and solve all the challenges that this industry faces.
Some of the biggest organizations globally are using data mining to increase revenue, decrease costs, and identify customers. Organizations are also using newer more definitive guides to learn trends in Data Mining. Amazon sets a prime example of data mining with its Amazon Price Check Mobile Application. The app can be used to scan products, perform product searches, and even to find in-store prices via a text message. This enables the company to collect intelligence information regarding products from its competitors.
Another great example of cross platform data mining is set by Delta. The airline consistently keeps an eye for complaints on major platforms such as Twitter. Any tweets that contain an issue that concerns the airline are solved almost instantly. Several issues regarding baggage and much more get solved in minutes. Delta on the other hand gains customer satisfaction, customer intelligence data, and much more to further enhance user experience.
These are just a few examples of how data mining is assisting companies worldwide improve their operations. The challenges faced by this industry can take time and unique resources to solve; However, once solved can turn out to be a true game changer for the industry profiting organizations around the world.