There are lots of terms involving data that are being tossed around these days. Data analytics. Data mining. Data warehousing. Big data. Data harvesting. Data science. Data scraping. And that’s just scratching the surface. It can become a confusing mess for those unfamiliar with the major changes surrounding data in the past decade or so. It’s no exaggeration to say that the explosion of data has transformed the world as more information is available for collection and analysis than ever before. Understanding these terms then becomes crucial if one hopes to effectively use data for their respective organizations.
Rather than looking at each term individually, let’s instead focus on two of them and do a proper comparison. The two terms we’ll look at are data mining and data harvesting. They come up quite often when talking about data, and they’re even sometimes used interchangeably. A thorough examination of each term reveals that the two, while similar, are different enough that they shouldn’t be confused with each other. Let’s go further and explore the differences in data mining vs. data harvesting.
What is Data Mining?
We’ll begin with a look at data mining. So what is data mining in the first place? Data mining is basically the process whereby large sets of data are analyzed in order to find patterns, relationships, and trends that otherwise might be missed through more traditional analysis methods. It is used to uncover shared similarities or groupings in web data that help gain insights for business decisions.
This process is sometimes referred to as Knowledge Discovery in Data (KDD), though that term isn’t used as often as it once was. Data mining largely makes use of complicated mathematical algorithms to achieve these goals. It’s useful for predicting events before they happen, though like any analysis technique, there’s never 100% certainty with the outcomes. Data mining merely increases the accuracy of analysis.
There are several properties which data mining is known for. The first is its automatic nature as it discovers patterns hidden within the data sets. Once the algorithm is programmed, the process goes on without much human intervention. The models have to be built, of course, which is where data experts will focus a lot of their time and attention. Many data mining models are built for specific data sets. So a retail company might build a data model specifically for sales data. However, other data models can be used for new data as it comes in.
Another key property in data mining is its ability to group pieces of data together. These groups should have a natural relationship to each other. When dealing with a large data set, it’s helpful to break down the data and create these groups so more effective analysis can be conducted.
A third property is making predictions with a probability attached to each one. These probabilities are often referred to as confidence, so they basically measure how confident the prediction is in coming true in the future. Predictive data mining can also state the conditions under which the outcome will happen. For example, a predictive data mining process would use machine learning to go through a customer database to look at past transactions in order to support theories about possible future volumes of transactions.
The last data mining property is delivering information that can be acted upon. Going through huge amounts of data and discovering new patterns and insights is simply not something that can be done with human abilities all the time. Data mining can do that, but it must also give results that can lead to action. If the data mining process only results in conclusions that have little meaning, then it has little use.
Data mining is helpful in finding out patterns and establishing relationships within a set of data. It can also be used for confirming and qualifying your own observations based on data you’ve received. As useful as that is, data mining can’t do everything. It can’t determine how valuable the data is, nor does it truly understand data sets. Data mining is simply doing what it’s been programmed to do. Knowing these limitations can help organizations employ data mining effectively.
The overall data mining process should follow a specific path with the following steps: It starts with identifying a problem or issue that needs to be solved within your business. This helps set expectations and objectives. You should research to understand current business objectives to assess business needs. Upon making those observations, create data mining goals to achieve your business objectives. A good data mining plan is essential to achieve both your business and data mining goals. Your data mining process must be reliable and repeatable by people who may have little or no knowledge of data mining in their background.
Once you understand business needs and have created a plan based on business objectives, you may move on to the data gathering and data preparation phase, where data is collected and prepared for further analysis. The next step is the model building and evaluation phase where data mining models are built and tested to find which one will work best with the data set. Last is knowledge deployment, where data mining leads to discovery of hidden insights and information that can be used for further results. The deployment phase can be as simple as creating a report of new insights uncovered during the data mining process in order to make business decisions based on those insights.
What is Data Harvesting?
The wide use of the term data harvesting is relatively new, at least when compared to data mining. Data harvesting is similar to data mining, but one of the key differences is that data harvesting uses a process that extracts and analyzes data collected from online sources.
The term data harvesting actually goes by other different terms. They include web mining, data scraping, data extraction, web scraping, and many other names. Data harvesting has grown in popularity in part because the term is so descriptive. It derives from the agricultural process of harvesting, wherein a good is collected from a renewable resource. Data found on the internet certainly qualifies as a renewable resource as more is generated every day.
To engage in data harvesting, a website is targeted, and the data from that site is extracted. That data can be pretty much anything the harvester wants. It might be simple text found on the page or within the page’s code. It could be directory information from a retail site. It might even be a series of images and videos. Or it could be all of those items at once.
There is no single method that data harvesting follows. Some methods involve harvesting data through the use of an automated bot, but that’s not always the case. Complicating the matter is the fact that some websites will place certain restrictions intended to fight this automated process. This is largely done through Application Programming Interfaces, or APIs. Many social media sites like Twitter and Facebook use APIs to ensure automated programs don’t harvest their data, at least not without their permission.
Data harvesting can be very beneficial, especially when using a third-party service. The data gathered from websites can provide organizations with helpful information and insights that can inform their business practices and help them reach out to prospective consumers. With so much data available on the web, data harvesting has become a popular and at times necessary tool so companies have a more thorough knowledge of marketplaces, consumers, and competitors.
Data Mining and Data Harvesting
Both data mining and data harvesting can go hand in hand with an organization’s overall data analytics strategy. The tools available to companies make data more accessible than ever before. Between data extracting tools, data munging tools, and more; it’s time to put that available data to good use.
Some organizations may feel intimidated by the vast amount of data out there, and they may think they don’t have the ability to properly analyze and use it to solve problems. Luckily, through data mining and data harvesting advancements, it’s easier than ever to collect data and discover those key insights and trends that will improve a company. As you understand how the two terms differ, you’ll be able to use them to the best effect.
Contact a data expert to find out how Import.io can save your organization the time typically spent on data mining and data harvesting, helping you get the most out of your web data.