What is data acquisition?

Data acquisition is the process of obtaining data from various sources using different methods. It represents a crucial step in the data ingestion pipeline, followed by data validation, transformation and loading.
 

Modern business fundamentals—such as data-driven decision-making, data analysis and artificial intelligence (AI)—all depend on the availability of large amounts of quality data. Data acquisition retrieves the data that makes these informed decisions and technologies possible. While the concept may seem straightforward, acquiring data can be complex, especially in the era of big data.

Today’s datasets are massive and intricate. They can span terabytes or petabytes, come in structured or unstructured formats and live across diverse sources. These complexities introduce challenges around managing data volumes, governance and security throughout the acquisition process.

However, when done effectively, the data acquisition process can be a pipeline of high-quality fuel for strategic initiatives. In fact, a study by Harvard Business Review found that organizations successfully leveraging big data and AI outperformed their peers in key business metrics, including operational efficiency, revenue growth and customer experience.¹

The term “data acquisition” can also refer specifically to the collection of the physical or electrical signals that measure real-world conditions—typically sensor data. Examples include temperature measurements, pressure and other physical phenomena.

These signals are processed and converted into usable digital values using data acquisition devices, or DAQ devices. This usage is common in fields such as environmental monitoring, industrial automation and scientific research.

According to the US Geological Survey, there are four methods of acquiring data:²

• Collecting new data
• Converting or transforming legacy data
• Sharing or exchanging data
• Purchasing data

Organizations can gather data through a seemingly unlimited number of sources. Data may be both structured and unstructured, and either internal or external. Some of the most common data sources are:

• Business applications: Data from enterprise resource planning (ERP), customer relationship management (CRM) and other systems
  
  
• Social media: Real-time interaction data from social media platforms
  
  
• Open data: Datasets from academic institutions and governments used for research and policymaking
  
  
• Public data: Data from governments and organizations, such as census and economic data
  
  
• Transactional data: Sales records, invoices and payment information
  
  
• Surveys: Data collected through customer feedback or research questionnaires
  
  
• Web analytics: Data from website interactions, such as page views and conversions
  
  
• IoT devices: Real-time data from connected devices, such as smart meters or appliances

Organizations acquiring data have several considerations to keep top of mind throughout the acquisition process:

• Data privacy and security
• Data quality
• Data compatibility
• Business needs vs. costs

Data privacy—also known as information privacy—is the idea that people should have control over how organizations collect, store and use their personal data. During acquisition, organizations might collect user information such as email addresses or biometric authentication data. It’s critical that they obtain user consent before processing this data, protect it from misuse and provide users with tools to actively manage it.

Many companies are legally obligated to follow these practices under regulations like the General Data Protection Regulation (GDPR). However, even without formal data privacy laws, there are benefits to implementing data privacy measures. Often, the practices and tools that protect user privacy also help secure digital information from unauthorized access, corruption or theft.

Ensuring data quality should be a top priority for organizations acquiring data from a wide range of sources. Data quality refers to how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness and relevance to its intended purpose. High-quality data supports accurate, fair and effective decision-making that aligns with business goals.

The importance of data quality control goes beyond daily operations. High-quality training data is key to effective adoption of artificial intelligence and automation. However, the well-known AI adage "garbage in, garbage out" applies broadly—poor-quality data in any use case leads to poor-quality outputs.

When organizations acquire datasets from diverse sources they will need to address any compatibility issues before loading them into their systems. Data cleaning practices and standardization can ensure that data adheres to a consistent format and structure, making it easier to understand and analyze down the pipeline. For example, street names commonly contain directions, like North or West. Standardization would format these values to “N” or “W.”

Organizations in heavily regulated industries (such as finance or healthcare) might face additional data standards rules and regulations. The Health Insurance Portability and Accountability Act (HIPAA), for instance, established standard code sets for diagnoses and procedures, creating a common language for healthcare data.

Before acquiring data, organizations should determine their data needs and whether the acquisition cost is justified. In addition to any costs related to data cleaning and standardization, businesses should consider pricing, licensing fees (if applicable) and any additional costs outlined in purchasing agreements.

Efficient data acquisition also requires robust data infrastructure that can handle, manage and store data. Organizations might need to invest in areas such as data storage, analytics, security and governance to help ensure that acquired data is properly stored, governed and used.

While often used interchangeably, data acquisition and data collection have distinct meanings.

Data collection is the process of gathering raw information directly from various sources, typically performed by data scientists and analysts. In contrast, data acquisition is a broader term that includes data collection. However, it also involves obtaining data through additional methods such as partnerships, licensing agreements, data purchases and the transformation of legacy data.

According to 72% of top-performing CEOs, gaining a competitive advantage depends on having the most advanced generative AI. But even the most sophisticated machine learning algorithms are only as effective as the data they are trained on. High-quality data is essential for AI systems to learn, adapt and deliver real value.

In practice, however, acquiring enough relevant data to train AI models can be challenging. Privacy concerns, high costs and legal or regulatory constraints can limit access to valuable data acquisition methods and sources such as web scraping or public datasets. In some cases, regulations may prohibit collecting specific types of data for AI use cases altogether.

To alleviate these obstacles, many organizations are turning to synthetic data—artificially generated data that mimics real-world data. Created using statistical methodologies or advanced artificial intelligence technologies like deep learning and generative AI, synthetic data offers several advantages: greater customization, more efficient acquisition, increased data privacy and overall richer data.