Most businesses wish they could take better advantage of data to make better, more informed decisions – but that is much easier said than done.

Big data is a veritable gold mine in what it has to offer, but managing, analyzing, and deriving insights from it presents a lot of challenges, too. And when you start learning about data management, you come across all this technical jargon and complex definitions that seem to make it all the more complicated.

One of the ways organizations try to make the most of the data at their disposal is with data mining. This technique can be extremely valuable to streamline operations, build accurate business forecasts, increase marketing and sales ROI, provide valuable customer insights, and much more.

Let's talk about what data mining is, some key definitions to keep in mind, common challenges, and how your business can harness its potential safely and ethically.

This analysis can give important insights that help solve problems and identify them before they happen again, reduce risks and costs, identify market opportunities, improve customer experience, and predict customer behaviors and preferences.

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Benefits of Data Mining

When done well, data mining can bring a significant advantage by providing business intelligence you wouldn't otherwise have access to, and give insights in a much more relevant and timely manner. Some of the benefits of data mining include:

  • Easily find the most important data. Big data has some really useful information in it, but there's also a lot you don't need and that would hinder analyses rather than help. Data mining allows you to automatically tell the valuable information apart and construe it into actionable reports.
  • Better understand your customers and their journey. Data mining can help you gather customer data from multiple sources and collate it to form informative and thorough profiles. This can give you valuable knowledge about customer trends, preferences, behaviors, similarities, and differences. That's the type of information that helps you deliver a better customer experience overall and improve communication across all touchpoints.
  • Faster, automated decision making. Instead of needing a person to review everything and decide on a course of action, you can automate certain decisions. For example, banks can use software to identify data trends that look like fraudulent behavior and automatically block accounts within seconds, notify a responsible individual, or request additional verification from users.
  • More effective and tailored marketing campaigns. With the knowledge you get from data mining, marketing teams can build much more personalized campaigns, tailor content and product recommendations based on known preferences and behaviours, predict trends in how consumers purchase or navigate your website, figure out what stops them from buying or what leads them to churn, create accurate marketing segments, and offer tailored promotions – and that's just the beginning. It goes without saying that these data-driven marketing campaigns yield a significantly higher ROI.

Data Mining vs. Data Harvesting

Data mining has its benefits, but it can sound like a lot to tackle for a beginner in the subject. One common point of confusion is in regards to the differences between data mining and data harvesting.

Data mining and data harvesting can be complementary processes if done properly. While mining refers to the analysis of large sets of data in order to derive trends, data harvesting is the process of extracting data from online sources to then build analyses. So, while mining focuses more on the analysis of data, harvesting focuses on the collection.

Data harvesting involves crawling a website to extract its data, which is then organized into intelligible information. And while it is possible to do this safely and ethically, there are plenty of malicious actors who use data harvesting methods to collect information online – such as email addresses, contact lists, photos, videos, text, or code – without users' consent or knowledge.

One famous example of data harvesting you might have heard of was the Cambridge Analytica and Facebook scandal. As reported by The New York Times, the British political consulting firm started harvesting data of millions of Facebook users in 2014 in order to build psychological profiles of voters and try to sell them to political campaigns.

Though the Cambridge Analytica scandal was large-scale and had huge repercussions, unethical data harvesting practices can be conducted by any type of company, regardless of size.

For example, let's say a small media startup is hoping to build more personalized content recommendations for their audience, which is mainly composed of women aged 18-24. So, in order to get more data to build these campaigns, this company decides to crawl similar websites that are often visited by the same target audience and find out what type of content they most consume there, and therefore builds tailored content recommendations from that. However, this data was acquired without users' consent, which already constitutes a data harvesting malpractice.

Another example is when a company is seeking to broaden the reach of their email newsletters, but doesn't have a huge number of subscribers yet. So this company decides to buy a contact list from a third-party provider to reach more people – however, buying and selling contact lists may be prohibited under several data protection laws, as well as sending unsolicited emails when users didn't explicitly provide their personal data or consent to receive emails.

Avoiding Problems with Data Mining

The scenarios described above are perfect examples of what not to do when deploying data mining and harvesting. In the Facebook-Cambridge Analytica case, for instance, data was extracted without users' consent or knowledge, Facebook failed to safeguard user data against external actors, and the data was then used for purposes that the users didn't explicitly agree with – or even necessarily knew about.

That's why it's paramount to be aware of the potential pitfalls with data mining and data harvesting and ensure that you carry out these practices ethically and transparently.

Ensuring Data Protection and Privacy Is Key

Like any process that deals with sensitive data – including personal data – your number one concern should be to ensure that all data you're collecting and using has been provided with explicit consent and in full compliance with any applicable privacy laws. This also includes making sure the data is secure throughout all stages of the process, including collection, storage, analysis, all the way to data deletion.

Organizations also need to implement internal rules to specify what the data can be used for and how it can be analyzed and implemented – and make sure that the insights taken from data mining themselves don't infringe on privacy policies. As a rule of thumb, being transparent, honest, and ethical with data should be your top priority.

Some companies may want to hire staff specialized in data science and security to oversee all data management and analysis procedures, which can be a big help to ensure data protection and user privacy throughout the entire process. They can also deploy specialized tools to achieve the best results.

However, all these special know-how and tools can end up getting quite expensive, which could make data mining cost-prohibitive to smaller or more budget-conscious businesses. This cost may also scale as your company grows and the complexity of your data increases.

Integrating Your Data Before Mining

An often overlooked step when implementing data processes – including data mining – is data integration. In a nutshell, data integration means combining data from several disparate sources into a unified database for a more consistent view of the data.

Integrating your data can make data mining even more effective and accurate. Since your data would be unified, enriched, and up-to-date after integration, it would be much easier and faster to identify trends and patterns, allowing for more agile decision-making based on current and accurate results.

If you use a syncing solution like Operations Hub to integrate your data, your customer databases are also updated in real time, so any analysis you gather from this data will be based on real-time insights and enable you to build more accurate profiles and compile reliable reports.

This type of integration can also sync customers' communication preferences between your apps, making it much easier for you to visualize customers' opt-ins and opt-outs in all apps to comply with data protection and privacy laws.

With that, you can not only gather accurate, reliable, and relevant insights from your data, but you can do so safely and legitimately – putting users' privacy and protection front and center.

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Originally published Oct 8, 2020 7:16:00 AM, updated April 21 2021