A Complete Data Mining Guide and How to Use It

You are currently viewing A Complete Data Mining Guide and How to Use It

Many organisations would like to take greater use of knowledge in order to make greater, more educated choices, but it is far easier said than done.

Big data is a veritable gold mine with what it has to deliver, but it also poses a lot of challenges to handle, evaluate, and extract information from it. And you come across all this technical jargon and abstract concepts when you start learning about data management, which seems to make it all the more difficult.

Data mining is one of the ways that companies aim to make the best of the data at their disposal. In order to streamline activities, create precise business forecasts, increase marketing and sales ROI, provide useful customer insights, and much more, this technique can be extremely valuable.

Let ‘s talk about what data mining is, some important concepts to bear in mind, common challenges, and how your organisation can securely and ethically exploit its potential.

What is the Mining of Data?

The process of analyzing vast quantities of data to identify trends and patterns is data mining. It enables you to translate raw, unstructured information into comprehensible information about different industry and market areas.

This research will provide useful insights that help solve and detect issues before they happen again, decrease risks and costs, detect business opportunities, enhance customer service, and anticipate customer behaviours and preferences.

Data Mining Benefits

Data mining, when done well, can offer a huge gain by providing market information that you would not otherwise have access to and providing insights in a far more important and timely manner. Some of the data mining advantages include:

  • Find the most relevant data quickly. There is some very useful information in Big Data, but there is also a lot that you don’t need and that will impede research rather than support. Data mining enables you to separate useful information automatically and build it into actionable reports.
  • Get a deeper picture of your clients and their journeys. Data mining can help you collect and collate customer information from various sources to form detailed and comprehensive profiles. This will provide you with useful knowledge of patterns, tastes, attitudes, similarities, and differences among clients. That’s the form of data that allows you to generally provide a better customer experience and enhance connectivity at all touchpoints.
  • Automated, faster decision-making. You can automate certain decisions instead of needing a person to review everything and decide on a course of action. Banks may use apps, for example, to detect data patterns that look like suspicious activity and block accounts automatically within seconds, alert a responsible person, or request additional user verification.
  • More successful and personalised strategies for marketing. Marketing teams can develop even more personalised campaigns, customise content and product suggestions based on known tastes and habits with the insights you get from data mining, anticipate patterns in how consumers purchase or browse your website, find out what keeps them from purchasing or what causes them to churn, develop accurate marketing segments, and deliver targeted promotions. It goes without saying that these marketing strategies powered by data produce a substantially greater ROI.

Data Mining vs. harvesting of data

Data mining has its advantages, but for a novice in the industry, it may sound like a lot to handle. In terms of discrepancies between data mining and data harvesting, one common point of misunderstanding is.

If performed correctly, data mining and data harvesting can be complementary methods. Although mining refers to the study of large data sets in order to derive patterns, the process of extracting data from online sources to then create analyses is data harvesting. So, while mining focuses more on data processing, the collection focuses on harvesting.

Data collection requires the crawling of a website, which is then structured into intelligible information to extract the data. And while this can be accomplished safely and ethically, without the permission or knowledge of users, there are also malicious actors that use data collection techniques to capture data online, such as email addresses, contact lists, images , videos, text, or code.

The Cambridge Analytica and Facebook controversy was a prominent instance of data collection you may have heard of. The British political consultancy firm, as revealed by The New York Times, began collecting data from millions of Facebook users in 2014 in order to build psychological profiles of voters and try to sell them to political campaigns.

Although the Cambridge Analytica scandal was large-scale and had tremendous consequences, any form of organisation , regardless of size, can pursue unethical data harvesting practises.

Let’s say , for example, a small media start-up hopes to create more tailored content suggestions for its audience, which consists mostly of women aged 18-24. This company then decides to crawl similar websites that are also visited by the same target audience in order to get more information to create these strategies and figure out what kind of content they most consume there, and then generates personalised content suggestions from that. However, this data was acquired without the permission of the consumers, which already constitutes a malpractice of data collection.

Another instance is when a business tries to expand the scope of its email newsletters, but still does not have a large number of subscribers. This company therefore prefers to purchase a contact list from a third-party vendor to reach more people, but under some data privacy regulations, purchasing and selling contact lists is forbidden, as well as sending unsolicited emails when users have not specifically given their personal data.

Avoiding Data Mining Issues

Great examples of what not to do when deploying data mining and harvesting are the situations mentioned above. For example , in the case of Facebook-Cambridge Analytica, data was collected without the permission or knowledge of users , Facebook failed to protect user data against external entities, and the data was then used for purposes in which users did not specifically agree or even necessarily know about.

That is why it is paramount to be mindful of the possible data mining and data collection pitfalls and to ensure that you ethically and transparently carry out these activities.

Ensuring data security and confidentiality is crucial

Your number one priority should be to ensure that all data you collect and use has been given with express consent and in full accordance with all relevant privacy laws, like any process that deals with sensitive data , including personal data. This also involves ensuring that the information, from collection , storage, analysis, all the way to data deletion, is safe in all phases of the process.

In order to specify what the data can be used for and how it can be processed and applied, companies will need to enforce internal guidelines to ensure that the insights taken from data mining itself do not infringe on privacy policies. Being open, truthful, and ethical with data should be your top priority, as a rule of thumb.

Some organisations will choose to employ data science and security personnel to oversee all data collection and review processes, which can be a huge support in the whole process to ensure data safety and user privacy. To produce the best performance, they can also deploy advanced equipment.

All of these specific know-how and tools, however, can end up being very costly, which may make data mining cost-prohibitive for smaller or more budget-conscious companies. As your business expands and the sophistication of your data increases, this expense will also intensify.

Integrating before mining the data

When implementing data processes, including data mining, an often overlooked phase is data integration. In a nutshell, data integration means merging data into a single database from many different sources for a more coherent view of the data.

Integrating the knowledge will make data mining much more powerful and precise. Because the knowledge after integration will be consolidated, enhanced, and up-to – date, it will be much simpler and quicker to recognise trends and patterns, enabling more agile decision-making based on real and precise results.

Your customer databases are also updated in real time if you use a syncing solution such as PieSync to integrate your data, so any analysis you obtain from this data will be based on real-time observations and allow you to construct more precise profiles and compile accurate reports.

PieSync will also synchronise the contact preferences of customers between your applications , making it much simpler for you to imagine opt-ins and opt-outs of customers in all applications to comply with data security and privacy regulations.

Not only can you gather accurate , reliable, and meaningful insights from your knowledge, but you can do so securely and legitimately, putting the privacy and security of users at the forefront.

Please share this