How to Cut Data Preparation Time for Visualization Tools - Pyramid Analytics

Specialized data preparation tools have emerged as powerful toolsets designed to sit alongside analytics and BI applications. They are intended to improve the quality of data models in the face of rapidly expanding data volumes and increased data complexity.

While these tools can handle many data types and sources, they are often expensive and difficult to learn. Not all standalone data prep platforms are end-user friendly. End-users may not need all the advanced functionality they offer, nor have the time to learn how to use them.

Standalone tools allow integration of broad and complex data sources, but they can add complexity. The solution is not to revert to relying exclusively on IT for data preparation, nor to depend solely on the basic data prep capabilities of desktop tools.

Despite these constraints, it is possible to implement data preparation processes that keep pace with today’s environment. Below are three ways to reduce the time and complexity of data preparation.

Data Preparation Must Balance Ease of Use with Data Governance

Organizations have largely moved beyond centralized BI implementations and associated data preparation techniques, but still require aspects of legacy systems such as data security and governance. Self-service tools offer no-code agility and modern interfaces, but often at the expense of governance and security.

Modern ETL processes must primarily suit end users, who are the predominant users of analytics and BI applications. These users expect data preparation to be intuitive and easy, as they are business experts rather than data experts. However, simplicity can be misleading; an easy-to-use solution (like Excel) is not always the right tool for the job.

A modern ETL process needs to balance power and simplicity. End users demand no-code, visual interfaces that allow them to connect, prepare, blend, and join data from databases, cloud and on-premises sources, structured and unstructured data, and spreadsheets using standard queries, formulas, filters, and joins—all without relying on others. At the same time, IT must maintain visibility into how and what data is being used due to concerns about data breaches.

Data Preparation Should be Embedded Directly in the Analytic Application

The use of standalone ETL software has become common, but this introduces several problems. It creates the need for a separate software application, increasing costs and administrative burden on IT departments. Separate products require separate licenses and administration.

It also increases the learning burden on users, who must become proficient with analytic applications that have their own proprietary interfaces and workflows. Users must also understand the functional relationships between their ETL tool and their analytics and BI tool, which can reintroduce data bottlenecks common to legacy tools.

Modern analytic and BI applications, such as Pyramid Analytics, include powerful ETL functionality directly in their platforms. Users can connect, prepare, blend, and join data from all sources with an intuitive, visually based user interface. Once models are built, users can create visualizations and dashboards from the same platform, without convoluted processes to migrate data between tools. Because Pyramid is server-based and centrally managed, all data models created during data preparation are available for others to use for their own analysis.

Data Preparation Must Practically Incorporate AI Capabilities

The explosive growth of data is making data preparation harder. Most organizations recognize the need to incorporate AI and ML technologies but struggle to integrate them meaningfully. While there is enthusiasm for AI, it is important to focus on aspects that can be practically incorporated into daily activities. Success depends on applying machine learning to data at the granular level and making it easy for end users.

Before integrating advanced AI, organizations must ensure they have the technology ecosystem to support it. Machine learning algorithms should be applied directly to data during data preparation, the first stage of the analytics process.

While analytics and BI applications now often include ML capabilities, these are frequently exposed to users only after data has been prepared and aggregated, limiting the effectiveness of machine learning algorithms, which work best on raw data. By incorporating machine learning at the data preparation stage—and including data preparation capabilities within a complete analytics platform—organizations can quickly surface all types of data for analysis, leading to more relevant correlations and insights.

It is also important that end users understand how to use the algorithms themselves. For example, Pyramid Analytics’ data preparation environment includes more than 25 machine learning algorithms (such as Outliers, Kmeans-Weka, Pam-R, Random Forest, Neural Net, and TensorFlow), exposed alongside typical blend, join, and column operations. Users can drag and drop these algorithms into the visual workflow and export the results into ready-to-analyze models. This allows ordinary business users to build models using traditional data prep tools and mix in machine learning algorithms using simple drag-and-drop actions, all without data science skills.

Conclusion: It’s Possible to Cut Data Preparation Time

Data preparation has become harder due to the explosion of data sources, forcing organizations to change how they prepare data for analysis. Organizations can no longer rely on legacy analytic systems or self-service tools with limited functionality. While standalone ETL tools offer deeper functionality for data scientists and experts, analytics are most effective when all analytics and BI are together on the same platform.

Organizations can reduce the time it takes to prepare data by adopting end-user-friendly ETL processes, integrating ETL processes on the same platform with other data analysis, visualization, dashboard, and reporting functionality, and exposing machine learning during data preparation so business users can apply algorithms to raw data instead of aggregated data.