What is data collection?

Definition of data collection

From Wikipedia

Data collection is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes.

Executive view

Collecting data can have many benefits for a business. It is difficult to know how you are doing and what you are doing well or where you are going wrong if you are not looking at the data. Evaluating your data can help you make better business decisions to save money and time. It is a well-established fact that every business benefits from data, and ultimately, a data strategy.

Data collection helps businesses:

• evaluate and improve internal operations.

• evaluate performance.

• solve business problems.

• better understand customers and how to serve them.

• make better decisions.

Business function leader view

Data collection helps teams stay in tune with business objectives and recognise companywide or personal strengths, which helps to keep everyone motivated. When the whole team are involved with data, not only do you get a better view of the entire business, but the individual segments grow a better understanding and appreciation of each other.

If your company suffers from a lack of data practices, from collection to analytics, it’s worth reviewing your data strategy or building one. There are pros and cons to outsourcing data services versus building capability internally, but a business planning to use AI seriously over the long term generally benefits from developing at least some in-house understanding of its own data — outsourcing everything makes it harder to know what “good” data collection looks like for your specific business.

Technical view

Data collection is the key to better products as it provides insights as to how customers use your products that you can act on to increase engagement, sign-ups and revenue. Furthermore, data collection throughout the marketing funnel leads to multiple opportunities to identify areas for improved conversions. Your data will likely come from multiple sources in many different forms, so you should have a clear data strategy that will define how your data will be stored, accessed and analysed.

Data collection helps deliver:

• actionable feedback from your end-users.

• actionable insights to focus your marketing efforts.

• more efficient decision-making based on data.

• opportunities for AI initiatives built on data.

Get this service if you encounter:

• a lack of awareness of KPIs such as conversion rates.

• a lack of insight into how people use your products.

Key criteria to consider are:

• What is the structure of the data you are collecting?

• What tech stacks should be used to collect the data?

• How will you store the data securely?

• How will you store the data so that it can be accessed and analysed by relevant teams efficiently?

Data collection in metals manufacturing

For a steel or metals manufacturer, data collection is rarely a single event — it happens continuously, at several distinct points in the physical process:

• At goods-in: Every delivery of raw material arrives with a mill test certificate documenting the heat number, chemical composition, and mechanical properties of that batch. Capturing that certificate data accurately — rather than filing the PDF away unread — is the foundation of material traceability for everything made from it downstream.
• At the saw or production line: Cutting yield, scrap length, and cycle time data, captured automatically rather than logged manually on paper, is what makes it possible to measure and improve material efficiency over time instead of guessing at it.
• At despatch: Linking the certificate data collected at goods-in to the specific delivery a customer receives is what proves compliance — if that link isn’t captured and maintained, the traceability chain is broken even if the original certificate was fine.

The common failure mode in manufacturing data collection isn’t a lack of data — mills produce certificates for everything, and most factories track output in some form. It’s that the data is collected in a format that’s hard to use: a scanned PDF instead of structured fields, a paper log instead of a database record. Automated extraction — reading a mill certificate with AI rather than a person re-typing it — turns data that already exists into data that can actually be queried, reported on, and linked to the rest of the business.