The Data Capture Market | 2021 Electronic Data Capture Solutions
Data capture is the process of collecting, ingesting or acquiring structured and unstructured data and converting it into a computer usable data format or simply storing it, for the purpose of using that data. to get some form of information.
Data entry covers both physical data sources – mainly paper documents of all kinds – and digital sources. Optical character recognition has been around for decades, but technology has advanced, as has the technology for storing and analyzing content. Methods of automated physical data capture include, but are not limited to, documents, mail, faxes and receipts, where they are converted to a readable digital format
Digital sources range from database and general applications to data feeds such as RSS feeds, 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 from you, manually from a third party, or automatically.
Manually, on your part: scanning documents, reading in data files to be saved in a database.
Manually, from someone else: an e-merchant customer filling in their relevant information (name, address, etc.)
Automatic: recording customer sales, recording website visits, storing security video, collecting sensor data.
Automatic data capture is clearly the preferred method for several reasons. By using a business information platform such as a database with data capture capabilities, businesses can:
- Dramatically increase data volume then do it manually
- Lower the costs
- Speed ââup processes
- Eliminate boredom typing errors
- Maintain and support a single system
- Establish rules and policies for what should and should not be ingested
- Define rules and policies for who can access what data
Typically, a business could spend $ 1 to avoid data entry errors. Correcting this error could cost as much as $ 10, in comparison. Failure to detect this error could result in up to $ 100 in lost income.
Data capture methods
You must use a variety of data capture methods to manage digital and physical data. Scanning a document is different from creating a PDF or filling out a web form.
Therefore, there are many methods of data capture including:
- Manual data entry
- Automated data entry
- OCR (optical character recognition)
- ICR (intelligent character recognition)
- Barcode / QR code recognition
- Voice capture
- IDR (intelligent document recognition)
- Digital Forms (both Web and App)
- Digital signatures
- Capture images and videos
- Paperless forms
- Double blind data entry
- Magnetic Stripe Cards
Benefits of automated capture
- Reduces the amount of manual date entry required.
- Reduces costs and speeds up the entry of content into designated business and organizational processes
- Improve accuracy by avoiding typing errors and missed data fields
- Significantly increases the data entry rate
- Automates the process of delivering data to destination or target
- Improves productivity all around
- Check data accuracy
- Increased 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 variety of data capture processes.
Here are some examples of different types of data capture:
Editing data capture
Many businesses still rely on batch processing, which performs data integration tasks at regular intervals but not in constant real time. So what if the dataset changes between now and the last update? This is where you would use change data capture (CDC).
Change data capture includes the processes and techniques that detect changes to a source table or source database, typically in real time. The changed entries are then moved to a target location, typically a second location than the primary data store.
There are two main ways to perform change data capture: log-based CDC and trigger-based CDC. In log-based CDC, the CDC solution examines the transaction log of a database. Log-based CDC is designed to help databases recover from failure with low latency, but some databases use very complex logs, which makes log-based CDC difficult, and every database data has its own proprietary log file format, which makes it harder to build a generic, solution.
In trigger-based CDC, the CDC solution uses database triggers, which are functions that run when another event occurs, such as entering new data or updating a table. . Database triggers decrease the overhead to check out changes when running CDC, but they also add overhead to the source system because they must run every time the database is updated.
Declared data capture
Declared data is information that is freely and actively transmitted to your company by your customers. This includes obvious facts like customer information, mailing address and credit card, but also their motives, intentions, interests and preferences. It is also called first party data because it comes directly from the source, the customer. This is the strength of the declared data: the customer willingly gives it to you.
The benefits are knowledge and context. Your customer talks to you about themselves and this allows for more direct contact and marketing. This means providing a more personalized experience, as the reported data takes the guesswork out of it. You know what your customers want because they told you so.
Intelligent data capture
Smart capture is the process of identifying and extracting critical information from incoming paper and electronic documents without in-depth user intervention. 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.
Entering invoice data
Entering invoice data is the process of entering invoice details into an accounting system. Paper traces are important in finance, but for any large business, dealing with paper files would be a logistical headache. Digital invoice entry allows easy routing and storage of invoices without the need for paper.
Data storage, warehouse vs lake
When it comes to massive data ingestion, there are two non-RDBM ways to store it, in a data warehouse or a data lake. Both are useful for storing data for further processing to obtain business information, but they work very differently.
Data warehouses – which have been around for decades – perform what is known as a write schema. This is where the data is processed for organized and structured storage. Errors are corrected, duplicates are removed, etc. When the data is called up later, it can be processed immediately because it was prepared during storage.
Data lakes – a concept barely a decade old – make patterns when reading. It just stores everything, in a variety of formats, even plain text. Data is processed as it is read in the application that uses it, such as a business intelligence or analytics application. This slows down the process because the data must be processed first. Data lakes are a great way to absorb a lot of data very quickly and in large quantities, but eventually you need to process it.