Data Collection

Data collection with AI

Data collection is the systematic process of gathering and recording information or observations from various sources or methods. It involves the acquisition of raw data, typically for the purpose of analysis, research, decision-making, or other specific objectives. Data collection can be manual or automated using sensors.

Why is Data Collection Important?

Data collection provides an empirical method to learn about a subject matter. Without data collection, we would have to rely on uninformed decisions that can have dire consequences. Businesses can minimize these risks using data collection as a foundation for fact-based decisions. Second- and third-party data collection can supplement first-party data to help meet a wide variety of objectives.

An old management adage is that you cannot manage what you can’t measure. This is particularly true in scientific research that relies heavily on collecting data to learn about the realities of our world. Our medications would be unsafe unless they had been through rigorous clinical trials designed to minimize risk. High-quality data collection ensures the scientific validity and credibility of clinical trial results.

Data Collection Methods

The choice of data collection method depends on considerations such as research goals, types of data needed, resources available, and ethical considerations. Researchers often use a combination of methods to gain a comprehensive understanding of a subject. The following are some of the methods used to collect data:

Manual Surveys

Manual surveys can take many forms, including a question-and-answer discussion in person or over the phone and paper questionnaires with manual data entry of responses.

Electronic Forms

Creating a form using tools such as Survey Monkey, Google Forms, or Microsoft Forms is a less labor-intensive approach than manual surveys. A hyperlink to the form can be emailed or shared on social media for individuals to complete. Such self-service forms have the benefit of tabulating results in real time.

Automated Data Collection

Internet of Things (IoT) networks comprised of smart sensors can collect data as telemetry streams without intervention. Examples include counting finished goods on a production line, measuring temperature fluctuations in machines or patients, and reading speed limit signs using cameras in self-driving cars.

Business Data Collection

Business performance is usually tied to revenue. In a retail scenario, the point-of-sale device collects data about product sales, transaction amounts and sales volume. Most business functions use key performance indicators (KPIs) to measure their performance, such as cases closed by customer service, sales quota attainment,  and leads generated by marketing. IT systems that support these functions collect data to analyze the business.

Business Performance Management

Well-managed businesses are data driven. Shareholders rely on audited financial disclosures, PE ratios, and growth trends to assess investment risks. Company boards hold management teams accountable using key goals and performance metrics. An analytics dashboard provides shared views of these across business functions to improve visibility and increase collaboration and cohesion.

Systematizing Business Data Collection

Modern businesses need current data to function effectively. Decision making starts with collecting raw data from multiple systems internally and from external sources. This raw data flows into automated data pipelines, enriching and refining data as it flows to analytical and reporting systems. The intended result is abundant, reliable, trusted data to support operational decision making.

Data Integration technology can assist with data collection and preparation for analysis in the following ways:

  • Providing preconfigured data connectors to business and IT systems.
  • Profiling collected data to ease ingestion into data warehouses.
  • Transforming data.
  • Filtering data.
  • Scheduling data movement.
  • Enabling centralized management of data pipelines.

Analyzing Collected Data

Survey platforms often include reporting capabilities with the option to export to .CSV files for further analysis. These comma-separated flat files are ideally suited for loading into a data warehouse. The data warehouse can handle much larger datasets than spreadsheets and populate analytics dashboards to share survey insights.

Secondary Data Collection

Primary research is expensive and takes longer than repurposing existing research. Below are some common sources of research that businesses use:

  • Analyst reports from industry watchers like Gartner, Forrester, IDC, Enterprise Strategy Group, Ventana Research, 451 Research, and Omdia for the IT industry.
  • Social media feeds can provide valuable metrics such as the popularity of subjects and emerging trends.
  • Publishers run annual industry surveys to stay abreast of developments and license their reports and findings.
  • Universities often conduct research sponsored by industry partners that provides credible data points.
  • Newspapers and magazines are readily available.

Actian Data Collection Capabilities

The Actian Data Platform includes data integration technology to ease data collection and a high-speed data warehouse for rapid data analysis. The integrated Actian warehouse is designed to minimize the need to create indexes and tune queries. This is due to its naturally indexed columnar data storage of relational data. Queries run faster than alternatives thanks to support for MPP systems that can take advantage of massive parallelism.

The Actian Data Platform allows organizations to store and analyze data on-premises and popular cloud platforms from Amazon, Microsoft, and Google.

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