Data capture is the process of collecting, ingesting, or otherwise acquiring structured and unstructured data and either converting it into a data format usable by a computer or merely storing it, for the purpose of using that data to gain some form of insight.
Data capture covers both physical data sources – paper documents of all kinds, primarily – and digital sources. Optical character recognition has been around for decades but the technology has advanced, as has the technology for storing and analyzing the content. Automated physical data capture methods include but are not limited to documents, mail, fax, and receipts, where it is converted into a readable digital format
Digital sources range from database applications, and applications in general, to data streams like RSS feeds, to sensors and other input devices. The data is then either processed for storage (in a data warehouse) or simply stored for later use (in a data lake).
The Data Capture Process
Data capture is the process that allows you to collect information, either manually on your part, manually on the part of third party, or automatically.
Manually, on your part: scanning documents, reading in data files to save in a database.
Manually, on someone else’s part: an e-commerce customer filling in their relevant information (name, address, etc.)
Automatic: Logging customer sales, logging Website visits, storing security video, taking in sensor data.
Automatic data capture is clearly the preferred method for multiple reasons. By utilizing an enterprise information platform like a database with data capture features, businesses can:
- Greatly increase data volume then doing it manually
- Reduce costs
- Accelerate processes
- Eliminate input errors from tedium
- Maintain and support a single system
- Set rules and policies for what is to be ingested and what not to
- Set rules and policies for who can access what data
As a general rule of thumb, a business could spend $1 to prevent errors in data capture. Correcting that error could cost up to $10, in comparison. Not catching that error could result in up to $100 in lost revenue.
See more: What is Data Scraping?
Data Capture Methods
You need to use a variety of data capture methods to handle digital and physical data. Scanning a document is different from creating a PDF or filling out a Web form.
Hence, there are many data capture methods, including:
- Manual data capture
- Automated data capture
- OCR (Optical Character Recognition)
- ICR (Intelligent Character Recognition)
- Barcode/ QR Сode Recognition
- Voice Capture
- IDR (Intelligent Document Recognition)
- Digital forms (Both Web and App)
- Digital signatures
- Image & video capture
- Paperless Forms
- Double Blind Data Entry
- Smart Cards
- Magnetic Stripe Cards
See more: Structured vs. Unstructured Data
Benefits of Automated Capture
- Reduces the amount of manual date entry required.
- Reduces costs and speeds the entry of content into the designated business and organizational processes
- Improve accuracy by avoiding mistyping and missed data fields
- Greatly increases the rate of data entry
- Automates the process of delivering data to the destination or target
- Enhances productivity all around
- Checks for data accuracy
- Enhanced visibility by offering the same input resources to all staff
Different Types of Data Capture
The term “data capture” is an umbrella term for a wide range of data capture processes.
Here are a few examples of different types of data capture:
Change Data Capture
Many businesses still rely on batch processing, which runs data integration jobs at regular intervals but not in constant real time. So what happens if the data set changes between now and the last update? That’s where you would use change data capture (CDC).
Change data capture comprises the processes and techniques that detect the changes made to a source table or source database, usually in real-time. The changed entries are then moved to a target location, usually a second location than the primary data store.
There are two main ways of performing change data capture: log-based CDC and trigger-based CDC. In log-based CDC, the CDC solution looks at a database’s transaction log. Log-based CDC is designed to help databases recover from failure with low latency, but some databases use very complex logs, making log-based CDC difficult, and each database has its own proprietary log file format, which makes it harder to build a robust, generic solution.
In trigger-based CDC, the CDC solution uses database triggers, which are functions that run when another event occurs like entering new data or performing a table update. Database triggers decrease the overhead for extracting changes when doing CDC, but they also add overhead to the source system because they need to run every time the database updates.
Declared Data Capture
Declared data is information that is freely and actively given to your company from your customers. This includes the obvious facts, like customer information, mailing address, and credit card, but also their motivations, intentions, interests, and preferences. It is also known as first-party data because it comes directly from the source, the customer. That’s the strength of declared data: the customer gives it to you willingly.
The benefits are knowledge and context. Your customer is telling you about themselves and it enables more direct contact and marketing. It means providing a more personalized experience because declared data removes the guesswork. You know what your customers want because they told you so.
Intelligent Data Capture
Intelligent capture is the process of identifying and extracting critical information from incoming paper and electronic documents without extensive input from a user. When used in conjunction with content management or business process automation software, an organization can use the extracted data for digital routing and delivery of relevant documents.
Invoice Data Capture
Invoice data capture is the process of entering of invoice details into an accounting system. Paper trails are important in finance but for any large company, dealing with paper records would be a logistical headache. Digital invoice entry allows for easy routing and storage of invoicing without requiring any paper.
Data Storage, Warehouse vs. Lake
When it comes to mass ingestion of data, you have two non-RDBMs ways of storing it, in a data warehouse or a data lake. Both are helpful for storing data for later processing to gain business insight but they operate very differently.
Data warehouses – around for decades – perform what is known as schema on write. This is where the data is processed for organized, structured storage. Errors are fixed, duplicates are removed, and so on. When the data is called on later, it can be processed right away because it was prepared on storage.
Data lakes – a concept barely a decade old – do schema on read. It just stores everything, in a variety of formats, even plain text. The data is processed as it is read into whatever application is using it, such as a business intelligence or analytics app. This slows the process down because the data needs to be processed first. Data lakes are a great way to take in a lot of data very fast and in large amounts, but you need to process it eventually.
See more: Best Data Analysis Methods 2021