In today‘s data-driven world, companies rely on analyzing large datasets to uncover valuable insights that inform business strategy and decisions. But before data can be mined for patterns, it first needs to be collected from various sources through a process known as data harvesting.
Though these terms are sometimes used interchangeably, data harvesting and data mining refer to different disciplines with distinct goals. In this guide, we‘ll compare and contrast these two integral phases of the knowledge discovery process.
Contents
An Introduction to Data Harvesting
Data harvesting involves gathering data from multiple online and offline sources and aggregating it into one centralized location. Think of it like a process of systematically harvesting crops from fields – except in this case, data is the crop being collected and consolidated.
Some common data harvesting techniques include:
- Web scraping – Extracting data from websites by parsing HTML code and copying relevant text, tables, images etc. Popular scraping tools include ParseHub, Octoparse, ScraperAPI. Here‘s an example Python snippet for scraping a table from Wikipedia:
import requests
import pandas as pd
from bs4 import BeautifulSoup
page = requests.get("https://en.wikipedia.org/wiki/List_of_largest_technology_companies_by_revenue")
soup = BeautifulSoup(page.content, ‘html.parser‘)
table = soup.find(‘table‘, {‘class‘:‘wikitable sortable‘})
df = pd.read_html(str(table))
print(df[0]) -
Web crawling – Using bots to systematically browse the web, index pages, and collect data. Search engines like Google use web crawlers to discover and catalog webpages.
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Data extraction – Pulling structured data from databases, APIs, CSVs, spreadsheets etc. For example, a Python script extracting data from a REST API:
import requests
import json
api_url = "https://api.data.gov/restaurants/inspections/v1/restaurants/dc/inspections"
response = requests.get(api_url)
data = json.loads(response.content)
print(data[0]) - Copying data – Manually gathering data from physical documents, PDFs, scans and other offline sources.
The main goal of data harvesting is to aggregate data from disparate sources into a single, unified dataset that can be easily accessed and analyzed. This raw data might be unstructured or messy, so data harvesting also involves steps like cleansing, reformatting and storing the data, such as in a data warehouse.
An Overview of Data Mining Techniques
Once data has been harvested and prepared, the next step is data mining – using computational methods to analyze large batches of data in order to discover meaningful patterns, trends and associations.
According to IBM, common data mining applications include:
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Classification – Sorting data into predefined categories or classes. For example, an algorithm can analyze customer attributes like age, location, spending habits etc. and classify them into groups like low-value, medium-value and high-value customers.
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Clustering – Identifying similarities in data and segmenting records into distinct groups based on shared characteristics. This is useful for market segmentation.
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Anomaly detection – Spotting anomalies or outliers that deviate from expected patterns in a dataset. This can indicate fraud, system failures or other issues.
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Association rules – Discovering interesting relationships between variables, like products customers frequently purchase together. Market basket analysis is a common example.
Data mining relies on statistical modeling, machine learning and other AI techniques to comb through data and deliver insights. Some popular techniques include:
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Regression analysis – Predicting numerical variables like sales, prices or inventory levels.
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Decision trees – Mapping out decisions in a tree-like model that generates rules for classification.
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Neural networks – Modeling complex patterns inspired by the human brain‘s network of neurons.
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Cluster analysis – Grouping data based on similarity measures between records.
According to Forbes, the global data mining market size was valued at USD $8.2 billion in 2020. This is expected to grow at a CAGR of 13.7% until 2028 as data analytics becomes increasingly critical for companies across industries.
Key Differences Between Data Harvesting vs Data Mining
While data harvesting and data mining are complementary disciplines, there are some notable differences:
| Data Harvesting | Data Mining |
|---|---|
| Focuses on collecting and aggregating data from multiple sources | Focuses on analyzing data to find patterns and derive insights |
| Relies on methods like web scraping, crawling, extracting | Relies on statistical modeling, machine learning, AI techniques |
| Emphasis on consolidating data into one place | Emphasis on extracting meaning from the data |
| Output is a raw, unstructured dataset | Output is knowledge, predictive models and actionable insights |
| Comes before data mining in the analytics workflow | Follows data harvesting in the analytics workflow |
Best Practices for Mining Data Effectively
For effective data mining, it‘s important to prepare and clean data properly before applying analytical models. Here are some best practices:
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Handle missing values – data gaps can skew results. Fill gaps or remove records with too many missing values.
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Remove outliers – anomalies can disproportionately impact models. Detect and discard true outliers.
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Normalize data – use z-scores or min-max scaling to normalize data to a standard range.
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Encode variables – convert categorical data to numeric variables.
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Split data – use 60-80% of data for training models, 20-40% for testing accuracy.
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Optimize models – tune parameters, evaluate performance, avoid overfitting to the training data.
Real-World Examples of Data Mining
Here are some examples of data mining delivering value across different industries:
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Recommendation engines – eCommerce sites analyze customer data and purchase history to provide personalized product recommendations.
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Predictive maintenance – Airlines analyze sensor data from jet engines to identify parts needing repair before failure.
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Healthcare – Identifying patients at risk of disease by mining records to uncover health patterns and risk factors.
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Fraud detection – Banks analyze transactions and spending patterns to flag anomalies indicative of fraudulent activity.
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Churn prediction – Telcos mine customer data to predict the risk of customers cancelling service so retention tactics can be targeted.
Ethical Considerations for Data Practices
While data mining can deliver powerful insights, the rise of "big data" has also raised concerns around responsible data practices. Companies must be transparent in how they collect and use consumer data. Algorithms also carry risks of amplifying biases if not designed carefully. Maintaining high privacy standards and avoiding broad generalizations is important.
Conclusion
In today‘s data-driven environment, organizations must leverage data intelligently to gain competitive advantage. This requires both harvesting data from various sources, as well as mining this aggregated data for trends and insights that inform strategy and innovation. Mastering both disciplines is key to unlocking the full value of data analytics.
