10 min read
Jan 31, 2020
Data

Convert Data to Dollars: The Business of Mining Data

Data is integral to business, and software can help you mine and make sense of the information you collect.

G.M.
Gitanjali MariaSpecialist Analyst

The world has been hollering for a while that data is important! But has anybody told you how important?

Ask Microsoft.

The tech giant paid an astronomical $26.2 billion, or $260 per active customer, to acquire professional networking site LinkedIn in 2016. In 2018, the U.S. data economy measured at $1.024 trillion, or 5% of the national output that year.

So, data is important! And so is data mining!

Data mining is integral to business intelligence and helps generate valuable insights by identifying patterns in the data. In this article, we'll walk you through the benefits of data mining, the different techniques involved, and the software tools that facilitate it.

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What is data mining?

Data mining is the technique of discovering correlations, patterns, or trends by analyzing large amounts of data stored in repositories such as databases and storage devices. It's a crucial part of advanced technologies such as machine learning, natural language processing (NLP), and artificial intelligence.

Data mining has to be done meticulously to get the best results. The broad steps discussed below can help you smoothly sail through the data mining process.

Data mining steps: 

  • Define your hypothesis or assumption.

  • Identify all data sources relevant to the hypothesis.

  • Discern data points from the data sources that need to be tested to validate or reject your hypothesis.

  • Use data mining techniques such as correlation analysis to test statistical models that best connect data points.

  • Interpret and report results and use gathered insights to frame your business decisions/actions.

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Commonly used data mining techniques

Statistical methods and pattern recognition technologies commonly use the following data mining techniques:

  • Pattern detection: Simple pattern tracking involves recognizing a deviation in your data at certain time intervals (e.g., website traffic peaking early in the evening or late at night). This can be represented using simple line graphs or bar charts.

  • Classification and clustering analysis: This technique helps discover groups and clusters within your datasets. For example, based on the average value of all purchases customers make with per month, you can group them as "low margin" or "high margin" customers, and then devise different marketing strategies for the different clusters.

  • Association: This technique helps you track patterns that show dependency (e.g., customers tend to buy headphones or phone cases when they purchase mobile phones).

  • Regression analysis: This technique helps identify variables and their effect on the metric you're looking at (e.g., ice cream sales having a direct correlation with the temperature).

  • Prediction: This technique involves using data mining to build forecasting models that predict how independent variables will change in the future. For example, eCommerce firms can use sales and customer data to build models that predict which products are likely to be returned after a seasonal sale.

  • Outlier detection: Data mining helps identify data values that fall outside a defined normal range. Removing such outliers is important for accurate data analysis results.

Microsoft Excel regression anaylsis screenshot

Regression analysis using MS-Excel

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Data mining benefits for businesses

There are many benefits of data mining, including some specific ones that add value to your business:

  • Optimize marketing campaigns: Data mining helps businesses understand which marketing campaigns will likely generate the most engagement, classify customers, display personalized advertisements, and optimize marketing spend.

  • Detect possible fraud: Data mining helps businesses detect fraudulent activity and anticipate potential fraud. For example, analysis of point of sale (POS) data can help retailers detect fraudulent transactions. Banks and insurance agencies use data mining techniques to identify customers likely to default on premium payments or make fraudulent claims.

  • Make better business decisions: Rather than solely relying on your intuition or experience, insights generated from your own business data can help you make better decisions. For example, intuition may tell you that your product is not selling because of its high price point while data analysis reveals that it's actually because of fewer distribution channels. Such insights allow your business to identify and dress the underlying issue.

  • Insight into employees and HR policies: Data mining not only helps improve external market performance but can also be used to understand employee behavior, predict attrition, and evaluate HR policies.

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Examples of data mining in real-world business scenarios

Giant corporations and small and midsize businesses (SMBs) in all industries can benefit from data mining. The right data helps companies increase revenue, cut costs, and add customers. 

Let's look at some real-world examples of how companies have converted data to dollars.

  • The right follow-up strategy helped increase conversions by 40%: Envelopes.com was seeing potential customers routinely leave its website without completing their purchase, and was unsure when to send follow-up emails regarding abandoned carts. An analysis of data patterns revealed that emails sent 48 hours after a prospect left the website returned a higher conversion rate than follow-up emails sent 24 hours later.

  • Improvements in product design and marketing drive market share: With most consumers preferring self-treatment for tooth sensitivity pain, a major CPG company wanted to improve the market share of its sensitivity products. The company hired a data analytics firm to mine data from multiple sources including social media and the company's own AWS database. They analyzed over 250,000 customer responses and identified three main factors directly affecting sales using text analytics, regression analysis, and other techniques.

  • Market basket analysis: Market basket analysis uses association rules to identify what items will likely be purchased by individual customers. Amazon's recommendation engine mines data from user history, purchased and abandoned carts, wish lists, referral sites, etc. to target customers with product advertisements they're most likely to click on and convert, thus driving sales.

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Data mining trends and latest developments

While simple pattern detection and regression techniques have been widely used by businesses for a long time now, the large volume of unstructured data, scattered data sources, and poor data quality have made data mining challenging. 

Here are some of the latest data mining trends and developments:

  • Big data and multimedia data mining: Data comes in many forms—text data, audio files, images, and videos. Gathering this data, cleaning it, and running models requires the latest tools such as text mining or speech analytics software.

  • Security and privacy concerns: Data mining by gathering sensitive client details—often without necessary obtaining the necessary approval or sharing rights—has led to increased concerns about data security and privacy. Regulations such as GDPR have reduced the ways in which businesses can use and store consumer data.

  • Distributed data mining: As data is stored in multiple locations and devices, sophisticated algorithms are being developed and used to mine data from these locations and generate reports.

  • Geographic and spatial data mining: This type of data mining extracts geographic, environment, and astronomical data to reveal insights on topology and distance. This is especially useful for the travel, navigation, and government sectors.

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Data mining tools and BI software that'll help

While MS-Excel supports many data mining techniques, it's not powerful enough to handle large datasets or connect multiple data sources. There are many alternate data mining software options that offer extraction and visualization features.

Click on each of the data mining software applications below to see real users ratings and reviews.

Alteryx

  • Features: Data discovery, data preparation, visual analytics, and reporting

  • Pricing: Starts at $1,500/year

  • Primarily targeted at/for: Data scientists, data analysts, power users

Grepsr

  • Features: Data transformation, data visualization, and APIs

  • Pricing: Starts at $99/month

  • Primarily targeted at/for: Web scraping

Mozenda

  • Features: Data collection, data visualization, and data exporting

  • Pricing: Starts at $250/month

  • Primarily targeted at/for: Web scraping

Periscope by Sisense

  • Features: Data engine, data discovery, and data visualization

  • Pricing: Starts at $500/month

  • Primarily targeted at/for: Power users, data scientists

RapidMiner

  • Features: Data preparation, machine learning, and modelling

  • Pricing: Starts at $5,000/year

  • Primarily targeted at/for: Power users, data scientists

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Highest-rated data mining tools on GetApp

Not what you're looking for? Visit our data mining directory to find the right tool for your business.

Looking for a BI product offering data mining and other capabilities? Visit our BI software catalog, where you can filter for free software tools, SMB-focused BI tools, and more.

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