Data mining is the practice of drawing solutions from data-based insights in the form of patterns, models, or algorithms. Doing this is certainly not easy. It requires the joint effort of data scientists, researchers, translators, and analysts to make it possible.
Let’s get through what issues intercept data mining.
Data Mining Issues
These issues are mainly categorized into three in data mining, which are given below:
- Mining Methods & User Interaction Issues
- Performance Issues
- Different Data Types Issues
- Data Security & Privacy
Let’s catch up with the roundup of issues in every aforementioned category.
- Mining Methods & User Interaction Issues
The issues can be with picking up the best-fit methodologies of data mining, which are association, classification, clustering analysis, prediction, sequential patterns or pattern tracking, decision trees, outlier analysis or anomaly detection, and neural network.
The professionals with hands-on experience in this domain often struggle with these issues while using the aforesaid data mining methods.
- Hard to Derive Knowledge for Diverse Domains
The beneficiary can be related to different industries and domains. So do differ their requirement and knowledge discovery, which require niche-based data extraction, transformation, & loading of diverse forms like visuals, text, or numbers. This process covers a broad range of knowledge discovery processes, which is a challenge.
- Lack of Interactive Models
Interactive modelling makes pattern searches easier. For this purpose, datasets are extracted, refined, converted, and cleansed to ensure that they produce the intelligence that is required through the mining request.
- Lacking Professional Knowledge
For every level, from web data extraction to processing, & modelling, you need experienced matter experts or data miners. Their hands-on experience guides them through the entire discovery process, which is to figure out patterns. Their qualification plus working experience ensure putting patterns in a concise and comprehensive format. Finding such professionals with accountability is indeed an uphill battle.
- Ad hoc Data Mining is Not Easy
Ad-hoc mining answers to a specific business requirement or query, which insights can make it easier to abstract. The Structured Query Language is the query language that supports the ad hoc knowledge discovery process. This language guides users through mining tasks by mining or data research companies, which work on the optimized & flexible structure of records. This task is again not so easy.
- Comprehensiveness is a Challenge
The visual presentation should be impactful to let the analyst just see and extract insights in no time. Here again, the team should be competent to run different tools like Tableau, Sisence, Excel, etc. for an effective visual presentation using charts, graphs, etc. Simply put, the presentation of models should be understandable, which is not like a walkover.
- Noisy Data is Hard to Handle
Noisy data refer to redundancies or useless data in the database or big data. Dealing with the noises like duplicity, incomplete information, and errors can be a big challenge. But, going ahead with them can disturb the effectiveness of end result.
Result? The pattern will be poor and useless.
- Measuring Patterns
The patterns filtered, tested, & evaluated should be interesting because they represent either intelligence or feasible solutions or lack novelty.
2. Performance Issues
It is necessary that the modelling should be flexible and qualified through quality tests. These models are likely to be artificial intelligence via machine learning. Here, the performance issues must be acknowledged to sail across them with prior solutions.
- Inefficiency and Difficulty in Scalability
Certainly, a database is required, mostly colossal-sized niche-based records. Web data extraction & capturing make it way easier. Even if it is paper-bound, the OCR conversion and cleansing practices help in preparing a database. But, there are challenges like honeypot trap, captcha, and privacy settings that can hamper the supply of vital details. Sometimes, lacking tools can also prove a big barrier to defining the efficiency and scalability of data mining.
- Inability to Work with Parallel, Distributed, and Incremental Algorithms
The metrics like colossal size of databases, wide distribution, and complexity of data mining methods push to derive parallel and distributed data mining algorithms. These algorithms split records into parts, which are further processed in the same manner. Finally, the results of all parts are compiled together. This is how the incremental algorithms continue to update databases without mining the data again from scratch.
3. Diverse Data Types Issues
Data has many faces. You may find it in its visual, audio, text, and numeric forms. Processing these different types and then, mining may be difficult.
- Dealing with Relational and Complex Types of Data
Your source data may have PDFs, multimedia objects, spatial data, temporal or other types of datasets. This is indeed a bone-breaking experience to create such a standard tool that can ideally process all types of data in the same way.
So, you need to customize or access a specifically designed tool to mine from a particular type of dataset, which is an expensive deal. And also, you should have the ability to manage that tool.
- Modelling from Heterogeneous Databases
As there are a number of sources to access data like LAN or WAN, you may not expect to have records in an ideally similar form and format. It is simply because of their storage in a structured, semi-structured, or unstructured form. Therefore, mining knowledge from them is not easy.
4. Data Security & Privacy
Personally, identifiable data is sensitive and people don’t like to share it with anyone. Here, a threat to its privacy & security can be a reason to seriously think.
- Security
Mostly, data are shared over the internet, the cloud, and servers to ensure their access 24X7 remotely. This access can be dangerous if it is done through a public network, which is not secure. Vulnerability poses a big risk. So, the transferability of any record should be defined through encryption.
- Privacy Concerns
Dynamic techniques are adopted to collect information from diverse resources, especially from data subjects. This collection is not risk-free, as they carry personally identifiable information. Hackers tend to break in and take away these credentials. Here, privacy controls, authorization, and data compliance like GDPR appear in a major safeguarding role.
