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Data Analysis Techniques and Types of Data Analysis for Data Wrangling

Technology   |   Paul Warburg   |   Dec 22, 2021

Data is becoming increasingly important for businesses and organizations of every kind. In recent years, there has been an increase in the amount of data being gathered for all organizations. With the rapidly increasing quantities of data, data analysis and data analysis methods have become more crucial for analysts. The patterns found in the data can guide analysts to key insights to make key business decisions.

Types of Data Analysis Methods

There are many types of data analysis methods, but these are the most common and used the most frequently.

  • Data mining. This technique involves using tools to extract data and examine it for key patterns and insights. It takes numbers and transforms it into information.
  • Statistical analysis. This technique examines a sample to find information like the median and deviation that can help analysts find patterns.
  • Diagnostic analysis. This technique answers the questions of why something happened by identifying patterns in the data.
  • Predictive analysis. This technique uses existing data to look forward to what is likely to happen. It can be a key method for making decisions.

There are other types of data analysis that can help lead analysts to the important patterns in quantitative data sets. These techniques and types of data analysis are valuable to numerous industries because every industry is increasing in data usage.

The Importance of Data Analysis

Data analysis helps transform large quantities of data and information to patterns that can be used to guide organizations. Analysts pull crucial insights from the data to help businesses and organizations make informed and reliable decisions. For businesses, pulling insight from data can help guide business decisions and guide productions and customer outreach. Data analysis can also benefit other organizations like in the healthcare industry and retail. Without data analysis methods, it can be impossible to distill what is important from a batch of data. But data analysis techniques are often hindered without the appropriate data preparation.

The Limitations in Traditional Data Analysis Methods

When researching and naming data analysis techniques, data wrangling or data preparation for broader data analysis, is typically not included on this list. Even though most IT professionals, data analysts and business people that work with large volumes of data recognize it as an important first step in the data preparation process, too many times data wrangling is regarded as janitorial work, an unglamorous rite of passage before exploring “real” data analysis techniques. But of all the types of data analysis, data wrangling is crucial to finding the most important insights. In fact, we believe data wrangling should be included under “data analysis techniques” as much the final results.

Data wrangling, a core data analysis technique is not done in one fell swoop–it’s an iterative process that helps you get to the cleanest, most usable data possible prior to your analysis. Without data wrangling, the data set could be nearly impossible to sift through to find crucial insights. Each step in the data wrangling process exposes new potential ways that the data might be “re-wrangled,” all driving towards the ultimate goal of generating the most robust data for final analysis.

The Data Wrangling Process with Alteryx Designer Cloud

At Alteryx, we think about the data wrangling process as the most critical first step and complementary to the other data analysis techniques discussed above. To begin learning more about the process of data wrangling, these are the key steps. Our data wrangling process includes six core activities to prepare data for analysis and to get the most business value out of your data:

  1. Discovering – allows you to understand your data and how it’s useful for analytic exploration and analysis
  2. Structuring – gives you the ability to format data of all shapes and sizes to work with traditional applications
  3. Cleaning – lets you fix and standardize the data that might distort your analysis
  4. Enriching – allows you to take advantage of the wrangling you’ve already done
  5. Validating – identifies and surfaces data quality and consistency issues
  6. Publishing – provides you the ability to plan for and deliver data for downstream analysis

While each of these activities may sound labor-intensive and even tedious, remember that the data analysis techniques using Designer Cloud are heavily automated – from a single keystroke or mouse click, Designer Cloud uses a suite of algorithms that can identify patterns and leverage them to suggest data transformations. Incorporating Designer Cloud into your data preparation process and arsenal of data analysis techniques doesn’t require much time, but will yield huge results in finding key patterns and using data effectively.

See for yourself how Designer Cloud can help with your data aggregation needs – sign up for a free 30 day trial.