12/17/2019 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 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 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 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 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
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 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 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 the 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 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, 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 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 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.