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. Data Extraction. 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 our 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 the process whereby large sets of data are analyzed 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 the analysis.
There are several properties that 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. 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 measure how confident the
incoming 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 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 helps
find out patterns and establish relationships within a set of data. It can also
be used for confirming and qualifying your observations based on the 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
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
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 the 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 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 goes by other different terms. They include web mining, data scraping, data extraction, web scraping, Data Crawling, 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 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, a 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 can’t 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 Hir Infotech can save your organization the time typically spent on data mining and data harvesting, helping you get the most out of your web data.
At Hir Infotech, we know that every dollar you spend on your business is an investment, and when you don’t get a return on that investment, it’s money down the drain. To ensure that we’re the right business with you before you spend a single dollar, and to make working with us as easy as possible, we offer free quotes for your project.