Data Preparation

What is data preparation?

Data preparation is the process of transforming raw data into a format that is ready for analysis. This can involve cleaning up, standardizing, and formatting data to make it easy to analyze, typically with the support of an analytics tool.

In most cases, data preparation precedes the use of data in a business intelligence (BI) environment, where data scientists and other technical experts will leverage that data to deliver insights to colleagues. With the introduction of decision intelligence (DI)—the next evolutionary stage of BI—nontechnical personnel can prepare data through no-code or low-code interfaces. They can then generate visualizations and conduct analyses on a self-service basis.

How does data preparation work?

Most data preparation processes involve steps such as data cleansing, data standardization, and data formatting. In data cleansing, data scientists remove or resolve any incomplete or incorrect records from a dataset. Data standardization involves transforming the data into a consistent format that can be used in data analysis. Finally, data formatting involves rearranging data into a structure that is ready for analysis.

Modern analytics environments such as DI platforms employ artificial intelligence (AI) to automate key aspects so that anyone can prepare data for analysis without a data science background. This approach—augmented data preparation—can broaden access to data-driven insights to any number of decision-makers within an organization.

Data preparation capabilities

In its 2022 market guide for augmented analytics, Gartner identifies several capabilities in data preparation that can contribute to better results from analytics. Those include:

• Data profiling: analyzing data to assess data quality and consistency
• Data quality: identifying data issues and providing data cleansing recommendations
• Data harmonization: combining data from multiple data sources into a unified format
• Data modeling: data transformation that enables data to be analyzed
• Data manipulation: data transformation for data aggregation and data sorting
• Data enrichment or inference: data transformation that adds data values based on data analysis
• Metadata development: data transformation that supports data provenance, data lineage, data governance, data security, and data privacy
• Data cataloging: data transformation that supports data discovery and data reuse

Data preparation and decision intelligence (DI)

Decision intelligence platforms leverage AI and machine learning (ML) to support low-code or now-code tools that “automate tasks performed during the analytics workflow and augment the user experience,” as Gartner describes in its 2022 market guide. “This can span most areas of data and analytics—from data ingestion and data preparation to analytics and ML model development.” Through augmented data preparation, AI eliminates the need for technical skills. Virtually anyone can prepare data, access useful insights from data, and generate data visualizations and reports on a self-service basis.

How can Pyramid Analytics help with data preparation?

Pyramid Analytics is the world’s leader in decision intelligence. We provide a DI platform trusted by enterprise brands all over the world. The platform can deliver data-driven insights and other capabilities to various personnel in a governed, self-service way. In addition to augmented data preparation, these capabilities include interface personalization, self-service analysis, advanced visualizations, and both data sharing and collaboration, all within a single environment. This eliminates the need for disparate analytics tools.

Contact us today to learn more about how we can help your organization and your broader analytics initiatives.