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