The Importance of Data Preparation

Data preparation is cleaning, structuring and enriching raw data into a desired output for analysis. It’s commonly referred to as “janitorial work,” but is enormously important and mission-critical to ensure robust, accurate downstream analytics. Properly conducted, data preparation gives you insights into the nature of your data that then allow you to ask better questions of it.

Breakdown of the Data Preparation Process

These are the main activities in the data prep process that can help companies utilize data to find key insights: 

  • Exploring. Before data can be prepared, analysts will need to know what is in the data. This is where the exploring step is crucial. Analysts can see the content of the data and let them assess column level distributions, anomalies in the data, patterns in the data structures, and many more areas. Once analysts are aware of the contents of the data, they can better determine how the data should be prepared.
  • Structuring. Raw data can come in many different structures and sometimes with no structure. It’s key to give the raw data structure by adding columns or rows in unstructured data sets, extracting key pieces of information, flattening arrays into rows, or separating raw data into columns.
  • Cleaning. To clean data, analysts remove null values, remediate anomalies and outliers, and replace unwanted values in columns. 
  • Enriching. The process of enriching data can help create a more complete picture of the data set. Analysts will join fields and determine which fields are the best for unions or joins.
  • Shaping. This final step in preparation is optimizing the raw data for analysis use. The optimization can consist of pivoting or unpivoting data, filtering, aggregating data, creating new fields, or encoding columns.

Once the raw data has been cleaned and prepared, it can then be analyzed and used to make decisions and find patterns. As straightforward as data preparation sounds, there are challenges that can cause some companies to neglect data prep or skip over crucial steps.

Data Preparation Challenges

One of the biggest challenges with preparing data is that, historically, it has been very time-consuming. It has been widely publicized that up to 80% of the overall analysis process is spent cleaning or preparing data. As data has increased in size and complexity in recent years, however, data preparation has only grown more demanding and is often relegated to the organization’s most technical employees.

But when data preparation lives behind technical barriers, this presents new challenges: first, the organization’s most costly resources are tied down with preparing data and are unable to solve more challenging data problems. Second, business analysts, or those who know the data best, are unable to get involved with the preparation process themselves. They lack the visibility into their raw data, which has the potential to transform their requirements and, ultimately, their analysis.

 

Self-Service Data Preparation

Self-service data preparation tools, such as Designer Cloud, are solving these problems and carving out a new market with huge demand. By empowering non-technical users with the ability to wrangle data themselves, organizations can unlock huge value from their big data investments. Business units and organizations can spearhead their own initiatives, leveraging new data sources while maintaining the same resources on their team. Simultaneously, Wrangler accelerates the process, allowing businesses and organizations to arrive at insights faster.

Benefits of These Self-service Tools

The technical employees only have so much time to work, and that can often lead to data preparation taking a back seat. Some businesses might also not have the funding to pay IT and data specialists to work on data prep. These shortages highlight why self-service data preparation is crucial for many organizations. 

Self-service data prep platforms like Alteryx are designed to be used by employees that don’t have an extremely technical background. Designer Cloud can be used with ease by those who have business backgrounds, or other less technical backgrounds, and it is specifically designed for ease of use. Self-service tools accelerate the data prep process because it expands who can work on the preparation, and the acceleration helps companies reach unexpected opportunities and new patterns that can drive future success. 

Benefits of These Tools on the Cloud

In the modern age of data, data is no longer stored solely on a server. Most organizations are utilizing the cloud to store data. To transition data in the cloud, businesses face the same challenges: shortages of qualified personnel and time to prepare the data. But using self-service tools such as Designer Cloud, companies are able to utilize the benefits of having data in the cloud. Unprepared data is hard to integrate with the cloud or machine learning because there are still anomalies and null values that make the data hard to use. But with self-service tools, companies are able to prepare the data efficiently and accelerate the process of putting the prepared data in the cloud. 

With Designer Cloud, businesses are able to accelerate and prepare data from data lakes and warehouses and multi-cloud environments for AI and machine learning initiatives. In the modern world of data, it’s key to be able to use the prepared data in multiple environments and have quality integration with the cloud. Alteryx’s data preparation helps organizations prepare raw data for the crucial next steps of integration and machine learning.

Our Solution

Alteryx has an innovative approach to self-service data preparation, which we call “data wrangling.” Designer Cloud’s intuitive interface combines interactive exploration, or the ability to visually profile data, with predictive exploration, or suggested transformations that gain intelligence with each new click. What’s more, every transformation step defined by the user in Designer Cloud is logged and at execution time automatically compiles down into the appropriate processing framework. With Designer Cloud, data preparation is accessible, intuitive and scalable across the organization. Sign up today!

 

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