Do you know why data preparation is important to your organization? Poor-quality or “dirty” data can result in unreliable analysis and ill-informed decision-making. This problem worsens when data flows into your system from multiple, unstandardized sources.
The only way to ensure accurate data analysis is to prepare all ingested data to meet specified data quality standards. That is why understanding the data preparation process is crucial.
Quick Takeaways
- Data preparation turns raw data into processed, reliable information for an organization.
- Without proper data preparation, inaccurate and incomplete data may enter the system, resulting in flawed reporting and analysis.
- Data preparation improves data accuracy, enhances operational efficiency, and reduces data processing costs.
- The data preparation process comprises six key stages: collection, discovery and profiling, cleansing, structuring, transformation and enrichment, and validation and publishing.
What is Data Preparation?
Data preparation is the process of transforming raw data into a format suitable for storage, processing, and analysis. It typically involves monitoring data quality, identifying and cleaning bad data, and reformatting and transforming data into a standard format. It can also involve combining multiple datasets to enrich the overall data.
Though the data preparation process can be long and involved, it’s essential in making data usable. Without proper preparation, there is no guarantee that the data your organization ingests will be accurate, complete, accessible, or reliable.
Why is Data Preparation Important?
The average business today faces a deluge of data from an ever-increasing number of sources, much of which is dirty. Cleaning this dirty data is a lot of work. According to Anaconda’s State of Data Science Survey, data specialists spend 39% of their time on data preparation and cleansing. That’s more than they spend on data model selection, training, and deployment combined.
Dealing with all this dirty data requires robust data preparation—especially when that data comes from multiple internal and external sources. Data from multiple sources often appears in different, incompatible formats. Some may be incomplete, inaccurate, or duplicated. Such data simply isn’t usable, at least not reliably.
That’s where data preparation comes in, preparing the data you collect for use across your organization.
Data preparation offers multiple benefits, especially for those working with large amounts of data. The most significant benefits include:
- Identifying and fixing obvious errors in ingested data
- Improving the accuracy of data flowing through the system
- Ensuring consistency of data across sources and applications
- Enabling data curation and cross-team collaboration
- Improving scalability and accessibility to valuable data across the organization
- Ensuring high-quality data for analysis
- Providing more reliable results
- Improving both operational and strategic decision-making
- Reducing data management costs
- Freeing up time for more important tasks
How Do You Prepare Data?
The data preparation process varies between industries and organizations within an industry. That said, there are five key components of data preparation that are common across most use cases.
1. Data Collection
The first step in data preparation is acquiring the data. You need to know what data you need, what data is available, and how to gather that data.
Data today can come from a variety of sources, including:
- Internal databases, data warehouses, and data lakes
- CRM and other systems
- External databases
- Internet of Things devices
- Social media
Data can be ingested in batches or streamed in real time. The preparation process must accommodate all these types of data, no matter how they enter the system
2. Data Discovery and Profiling
All ingested data must be examined to understand what it contains and how it can be used. This typically involves data profiling, which identifies key attributes and extracts common patterns and relationships in the data.
This stage marks the beginning of data quality management. You should thoroughly examine the data to identify any inconsistencies, missing values, and other potential issues.
3. Data Cleansing
Any data with issues identified in the discovery and profiling stage should be separated from the presumably higher-quality data ingested. At this point, attempts should be made to cleanse the dirty data, using a variety of techniques such as:
- Removing duplicate data
- Identifying and removing data outside an acceptable range of values
- Filling in missing values
- Synchronizing similar-but-inconsistent entries from multiple sources
- Correcting obvious errors
- Removing outdated data
Data that can be successfully cleansed can be returned to the data set. Data that cannot be reliably repaired should be deleted.
4. Data Structuring
Data from a variety of sources may come in numerous formats. Some will be structured, some unstructured. Standardizing data structure is important for future access. All ingested data must eventually conform to your organization’s standard data structure, which means analyzing that data’s original structure and mapping it to your standard structure.
5. Data Transformation and Enrichment
Beyond structuring, ingested data must be transformed into a format usable by others in your organization. This may involve something as simple as converting date formats or as complex as creating new data fields that aggregate information contained in multiple previously existing fields.
Transforming data enriches it, providing additional insights beyond that contained in the raw data itself and contributing to better decision-making.
6. Data Validation and Publishing
The final stage is data validation and publishing. Data validation involves running automated routines that verify the data’s accuracy, completeness, and consistency. The validated data can then be published to applications or stored in a data warehouse or lake for future use.
Improve Data Preparation FirstEigen’s DataBuck
Data quality monitoring and validation is essential in the data preparation process. DataBuck from FirstEigen monitors data throughout the process, from initial collection to publishing and analysis. It uses artificial intelligence and machine learning technologies to automate more than 70% of the data monitoring process. This reduces data management costs while identifying, isolating, and cleansing poor-quality data.
Contact FirstEigen today to learn more about improving data quality in the data ingestion process.
Check out these articles on Data Trustability, Observability, and Data Quality.
- 6 Key Data Quality Metrics You Should Be Tracking
- How to Scale Your Data Quality Operations with AI and ML
- 12 Things You Can Do to Improve Data Quality
- How to Ensure Data Integrity During Cloud Migrations
