Even as we grow in our ability to extract vital information from big data, the scientific community still faces roadblocks that pose major data mining challenges. In this article, we will discuss 10 key issues that we face in modern data mining and their possible solutions.
1. Heterogeneous Data
Data can be of low quality, adulterated, and incomplete. That’s why, apart from the complexity of gathering data from different data warehouses, heterogeneous data types (HDT) are one of the major data mining challenges. This is mostly because big data comes from different sources, may be automatically accumulated or manual, and can be subject to various handlers.
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This often leads to high redundancy and degrees of falsified data. A very common example can be that of a customer survey, where people may not submit or incorrectly submit certain information such as age, date of birth, or email addresses.
Solution: There are two aspects to a solution for this problem. One, we take the traditional approach and process each HDT individually as per the classical homogeneous data mining process and then stitch the results together. Alternatively, we combine the HDT during the pre-processing stage and then conduct the data mining process, treating them as a single entity. This is, of course, simpler than the first option.
Secondly, we approach the solution from the business angle as well, where marketing and development teams ensure that accurate data is collected as much as possible. For example, businesses must ensure that survey questions are more representative of the objective, and data entry points, such as in retail, have a method of validating the data, such as email addresses. This way, when we analyze sentiment through emotion mining, it will lead to more accurate results.
2. Scattered Data
One of the most prominent data mining challenges is collecting data from platforms across numerous computing environments. Storing copious amounts of data on a single server is not feasible, which is why data is stored on local servers. This is the case with most large-scale organizations. In fact, it is something we ourselves faced while data munging for an international health care provider for sentiment analysis.
Scattered data could also mean that data is stored in different sources such as a CRM tool or a local file on a personal computer. This situation often presents itself when an organization may want to analyze data from multiple sources such as Hubspot, a .csv file, and an Oracle database. Companies are also looking at more non-traditional ways to bridge the gaps that their internal data may not fill by collecting data from external sources.
Solution: We need to create distributed versions of data mining algorithms so that we don’t have to bring all of the data to a single centralized repository as we are doing now. We also need the right protocols and languages to map this scattered data. For now, this can be achieved to quite an extent with the help of metadata.
One can use XML files to store metadata in a representation so that heterogeneous databases can be mined. Predictive mark-up language (PMML) can help with the exchange of models between the different data storage sites and thus support interoperability, which in turn can support distributed data mining.
3. Data Ethics
Data mining challenges involve the question of ethics in data collection to quite a degree. This is different from data privacy. For example, there may not be express permission from the original source of the data from where it is collected, even if it is on a public platform like a social media channel or a public comment on an online consumer review forum.
For example, an e-commerce website might access a consumer’s personal information such as location, address, age, buying preferences, etc., and use it for trend analysis without notifying the consumer. The question becomes whether or not it is OK to mine personal data even if for the seemingly straightforward purpose of building business intelligence.
Solution: This is a governance issue, more than anything else, and one of the prominent data mining challenges in an ethical AI environment. Much like a website informs the user to accept or reject cookies, or requires permission to run pop-ups, a business too must inform the consumer of what they may use their data for. This is a responsibility that businesses need to address for more transparency with their customers.
4. Data Privacy
Data privacy is a serious issue that arises in data collection, especially when it comes to social media listening and analysis. Social media organizations are under the spotlight even more so because of the Cambridge Analytica/Facebook fiasco, which ultimately led to the former filing for bankruptcy, and the latter paying a $5 billion fine to the U.S. government for data privacy violations.
Because of this ongoing scrutiny, many social media platforms including Facebook, Snapchat, and Instagram have tightened their data privacy regulations. And this has proven to pose data mining challenges for social sentiment analysis.
Solution: This again falls in the purview of the principles of ethics in data mining. Social media platforms as mentioned above, and even others like Twitter or Amazon Reviews, need to be transparent about their data privacy policies. Another important way to address this issue is to regulate third-party apps that can access data through either direct access to a user’s digital device or indirectly via one of the user’s social connections. And thirdly, data scientists need to follow proper protocol when requesting access to social media apps and platforms, such as Douyin, which have very stringent data protection rules and are difficult to access for the purposes of data mining. At no point should an organization use back channels to access such restricted information.
5. Data Security
Data security is a big one when it comes to data mining challenges. Not only is this an issue of whether the data comes from an ethical source or not, but also if it is protected on your servers when you are using it for data mining and munging. Data thefts through password data leaks, data tampering, weak encryption, data invisibility, and lack of control across endpoints are causes of major threats to data security. Not only industries but governments are becoming more stringent with data protection laws as well.
Solution: When gathering data for analysis, data mining companies need to offer clients the option to choose between a public/c
loud environment and an on-premise platform that is safe behind the client’s firewall. On an organizational front, businesses need to govern data privacy at scale instead of looking at piecemeal solutions. They need to invest in AI-enabled intelligent software that can track sensitive data and automatically catalog it in order to meet data privacy regulations.
You need to do a continuous risk analysis of all sensitive data as well as personal information and index identities. Doing so can make data inventory more coherent and makes data access transparent so that you can monitor unauthorized activity. With a tight-knit privacy mandate as this is set, it becomes easier to employ automated data protection and security compliance.
6. Data Complexity
When data is mined to analyze sentiment for a customer experience (CX) use case, for example, it is usually in the form of a very heterogeneous mix of data types that includes spatial data, user-generated videos, social media videos, images, memes, emojis, natural language text, and such.
Most tools that offer CX analysis are not able to analyze all these different types of data because the algorithms are not developed to extract information from such data types. In such a scenario, they neglect any data that they are not programmed for, such as emojis or videos, and treat them as special characters. This is one of the leading data mining challenges, especially in social listening analytics.
Solution: This problem can be solved if a platform has the capability to recognize and extract information from non-text content in the same manner as it can from textual data. Through the application of video content analysis, such data can be mined and processed for security and surveillance, sentiment analysis, healthcare delivery, market research, and numerous other areas.
7. Methodology
What methodology you use for data mining and munging is very important because it affects how the data mining platform will perform. Sometimes this becomes an issue of personal choice, as data scientists often differ as to what they deem is the right language – whether it is R, Golang, or Python – for perfect data mining results. How this presents itself in data mining challenges is when different business situations arise, such as when a company needs to scale and has to lean heavily on virtualized environments.
Solution: The solution here lies not in looking at each computing language individually but at the bigger picture of what your machine learning platform is meant for. If you are looking at a model that is built for websites, Python works well. If you are looking at data and security, Java should be preferred for obvious reasons. Yet again, if you’re looking for speed, scalability, and cloud-based environments, Go offers you this capability.
8. Data Context
Contextual information ensures that data mining is more effective and the results more accurate. However, the lack of background knowledge acts as one of the many common data mining challenges that hinder semantic understanding.
Solution: Metadata can help with this to a great degree. Because it gives information about other data, metadata helps in data extraction and in cleaning the data. It is also because of the summarizations it provides that we get more contextual information between current detailed data and highly summarized data. For example, it allows you to scour through terabytes of data to tell you who the singer of a particular song is, or the author of a research paper. That’s why an organization needs to pay attention to the quality of its metadata.
9. Data Visualization
Data mining challenges abound in the actual visualization of the natural language processing (NLP) output itself. Even if one were to overcome all the aforementioned issues in data mining, there is still the difficulty of expressing the complex outcome in a simplified manner. It is important to consider the fact that most end-users are not from the technical community and this is the main reason why many data visualization tools do not hit the mark.
Solution: Successful data visualization can be achieved if we make sure that the output data is provided in the form of easily understandable charts, graphs, color-codes, or other graphical representations. Word clouds are a great example of how complex algorithms can showcase the results of a query in an efficient manner that a non-technical user in a marketing department can follow.
10. Response Time
Last but not least is the issue of the response time of the prediction model. Precision and accuracy are of utmost importance in a business setting but a highly efficient response time is necessary too. Think stock exchanges: In such an industry where split-second stock trading decisions are heavily dependent on almost real-time market analysis and predictions, response time becomes absolutely critical.
Solution: When planning for a machine learning solution, data scientists need to decide on the pros and cons of such algorithms while keeping in mind the business application for which a solution is being built. Some algorithms are simple to build – for example, non-parametric classification methods such as the k-nearest neighbors (K-NN) algorithm, which is commonly used in classification and regression. They are, however, not time-efficient while predicting target variables.
On the other hand, other algorithms like non-parametric supervised learning methods involving decision trees (DTs) are time-consuming to develop but can be coded into almost any application. That’s why foresight and proper planning are very important.
Conclusion
Data mining has helped us make sense of big data in a way that has changed the course of the way businesses and industries function. It has helped us come a long way in understanding bioinformatics, numerical weather prediction, fraud protection in banks and financial institutions, as well as letting us choose a favorite movie on a video streaming channel. We must continue to develop solutions to data mining challenges so that we build more efficient AI and machine learning solutions.
