data mining definition steps and examples
when we think of mining it sounds manual tedious and unfruitful after all hacking
away at rock walls for hours on end hoping to find gold sounds like a lot of
work for a very small reward data mining however is quite the
opposite without doing much work at all you can reap rewarding results and
that's because we have modern solutions which do it for us
these softwares can sift through terabytes of data within minutes giving
us valuable insights on patterns journeys and relationships in the data
so let's dive into what data mining is how we do it and what its examples look like
what is data mining
data mining is a type of analytical process that identifies meaningful
trends and relationships in raw data and this is typically done to predict future data
data mining tools come through large
batches of data sets with a broad range of techniques to discover data
structures such as anomalies patterns journeys or correlations
though it's been around since the early 1900s the data mining we no one used
today comprises three disciplines the first is statistics the numerical
study of data relationships secondly we have artificial intelligence
the extreme human-like intelligence displayed by softwares or machines
last but not least we have machine learning the ability to automatically
learn from data with minimal human assistance
these three elements have helped us move beyond the tedious processes of the past
and onto simpler and better automations for today's complex data sets and in
fact the more complex and varied these data sets are the more relevant and
accurate their insights and predictions will be
by unveiling structures within the data data mining yields insights that can
then be used by companies to anticipate and solve problems plan for the future
make informed decisions mitigate risks and seize new opportunities to grow
what are the steps in data mining the overall process of data mining generally
consists of six steps the first is outlining your business goals
it's important to understand your business objectives thoroughly this will
allow you to set the most accurate project parameters which include the
time frame and scope of data the primary objective of the project in question and
the criteria needed to identify it as a success
the second is understanding your data sources with a deeper grasp of your
project parameters you'll be able to better understand which platforms and
databases are necessary to solve the problem whether it's from your crm or
excel spreadsheets identify which sources best provide the relevant data needed
the third is preparing your data in this
step you'll use the etl process which stands for extract transform and load
this prepares the data ensuring it is collected from the various selected
sources cleaned and then collated the fourth is analyzing your data at this
stage the organized data is fed into an advanced application and different
machine learning algorithms get to work on identifying relationships and
patterns that can help inform decisions and forecast future trends
this application organizes the elements of data also known as your data points
and standardize how they relate to one another
for instance one data model for a shoe product is composed of other elements
such as color size method of purchase location of purchase and buyer
personality type the fifth is reviewing the results here you'll be able to
determine if and how well the results and insights delivered by the model can
assist in confirming your predictions answering your questions and achieving
the business objective and last we have deployment or implementation
upon completion of the data mining project the results should then be made
available to the decision makers via a report they can then choose how they
would like to implement that information to achieve the business objective in
other words this is where insights from your analyses are applied in real life
without proper data management and preparation data mining could actually
work against you by providing inaccurate insights and forecasts however when done
correctly and by the right software data mining enables you to sift through
chaotic data noise to understand what is relevant from there you can make active
use of that information in your decision making
data mining examples people tend to assume that more data
equals more knowledge but in reality it's less about how much data you have
and more about what you do with it let's look at a few examples of
companies who've understood this and have done it right through their smart
use of data mining they've come out on top the first is groupon
groupon aligned their marketing efforts such as ad campaigns and sales offerings
closer to their customers preferences by data mining one terabyte of customer
data this data was analyzed in real time and helped the organization identify
emerging trends within their audience segment that they could leverage on
the second is domino's pizza from its point of sale systems and 26 supply
chain centers to text messages social media and amazon echo domino's pizza
improved its marketing and sales performances while enabling one-to-one
buying experiences across various touch points
it accomplished this by data mining 85 000 structured and unstructured data sources
third is air france klm
air france klm created personalized travel experiences for their flyers
through building a 360 degree customer view based on data mined from trip
searches bookings flight operations website cookies and social media
Gauthier Le Masne their chief customer data officer said each and every traveler is
unique with our big data and talent platform we offer made just for me
travel experiences from purchase planning through the post flight stage
well there you have it now that you understand what data mining is how it
works and the critical role it plays in transforming the way companies do things
perhaps you can start thinking about how these tools can empower you and your teams too