Data Mining: Current Trends and Challenges

  • By Shaun
  • 05-03-2020
  • Technology
data mining

The New Year 2020 is expected to open a new door of improvement for the state of data mining. There’s no doubt that every business, howsoever different in size, type and ownership, stresses on improving its customer experience and future investments. Organized data is an indispensable part of this important business goal and to get that done, data mining is the one-stop solution. Data mining refers to a necessary knowledge extraction process, which involves the collection, refinement and organization of useful information.

Today, data mining is not only limited to the business-based environment, but is reaching out to other crucial fields like medicine, weather forecasts, insurance expectancy forecasts, healthcare data analysis and transportation data analysis. When all the industries are reaping the benefits of data mining, it becomes very important to keep abreast with the current trends in data mining.

The amount of data stored, observed and organized every day is beyond what you can calculate on a surface level. According to a recent study, this is what Google receives every minute – over 20 lakh search queries, more than 200 million e-mails, around 48 hours of YouTube videos, plus 7 lakh types of different Facebook content, and last but not the least, over 1 lakh tweets. It means that piles of data is being calculated over many different mediums in a duration of just 60 seconds.

As every second passes, this data adds up to other platforms (including news, stock trading and media sharing) and generates additional data. A few years back, all this data was collected by many organizations and now, companies across the globe are using it for research and analysis purposes. Let’s understand the common challenges that different industries face in the process of data mining, so that it becomes a smoother and easier experience for you.

Challenges in Data Mining

Noisy Data
It is impossible to spearhead the process of data mining without scanning large volumes of data to derive information. Being unstructured, noisy and diverse as per their own fields, large quantities of this collected real-world data are unreliable. This data might have measurement or quantification errors that can occur due to an instrument or a human’s mistake.

Imagine a case where you want to filter some data of people who have recently visited your retail store to send them some exclusive discount offers. But as soon as you start doing it, you find out that the data is severely flawed. This can be because a major section of clients have entered their e-mail IDs incorrectly (by mistake or even knowingly). Industry experts refer to it as ‘noisy data’.

Scattered or Distributed Data
The real-world data is stored in not one, but many different platforms like the internet or secured databases. If you go by the current trends in data mining, bringing all the data to a single structure is very important, but doing this can take a lot of time, planning and effort.

Sometimes, you might need to sort out the sub categories of a main category. But if the data of these sub categories is saved in several different locations on secured databases, accomplishing your business objective will not be as easy as it sounds. To bring together this scattered data, you’ll hence need the perfect combination of manpower, algorithms and the related tools.

Intricate Data Restructuring

Other than the hassle of been stored in different platforms, different types of structures of the real-world data is also one of the biggest challenges in data mining. The popular structures of data mainly include text, numeric, audio, video and graphical. Pulling out and compiling the required information from this complex data can be very tiring and therefore, business owners usually seek out for professional assistance to spearhead the process of data mining meticulously.

Algorithm Performance
How the process of data mining will work out directly depends on the algorithms and the mining methods used. This is why, algorithms are an inseparable part of the process of data mining. If you are not following the latest and safest mining methods and algorithms, the results of the task assigned will not be as satisfactory as you wanted. Not only will this ultimately disturb the end data, but will also hamper the complete project.

Incorporating Background Knowledge

Perfect, proper data mining is incomplete without background knowledge as it is responsible for delivering more accurate end data. Background knowledge incorporation is a great way to garner accurate results from the descriptive tasks and extract actual predictions out of the predictive tasks. All the experienced data entry experts consider the implementation of background knowledge an important part of the process. Even if it is time-consuming and difficult, it’s a step that must never be missed.

Data Privacy
Privacy of data is one of the biggest concerns for every organization and individual. Several fields and operations related to the process of data mining can cause a data protection threat. There’s a lot of data that can be beneficial for your competitors as it involves crucial information about your prospective clients and thus, they can try to get their hands on it unethically. This hacking has alarmed the companies across the globe and they have been investing in the latest data security measures, without fail.

Current Trends in Data Mining
Organizations all over the world today are executing the process of data mining, owing to its countless benefits in the business ecosphere. Be it identifying customers, increasing the revenue or cutting down the cost, data mining sorts everything for big and small organizations alike. Hiring data entry experts in India has been one of the most promising trends in the industry as its bringing about a big change by making available high-quality data mining services at budget-friendly prices. Some of the other popular trends in data mining are:

- Exploring applications
- Interactive and scalable data mining methods
- Integrating the process of data mining with web database systems, database systems and data warehouse systems
- Multi database data mining
- Standardization of data mining query language
- Web mining
- Visual data mining
- Biological data mining
- Data mining and software engineering
- Distributed data mining
- Finding out new methods for mining complex data
- Real time data mining
- Privacy protection and information security in data mining

Assisting companies worldwide in one way or another, data mining is a one-stop solution if you want to improve operations within your organization. Although there are some challenges at present, constant efforts are being made to curb them as soon as possible. Once solved, data mining can help organizations across the globe flourish.

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Shaun is the Director of Virtual Employee Private Limited, a remote staffing & tech MNC, and is responsible for leading a team of more than 1200 experts from domains like IT, Content Creation, Digital Marketing, Designing. A law graduate from Brunel University, Shaun has been instrumental in creating a business model which is increasingly being seen by industry peers as a model for new organizations in the tech outsourcing domain.