Data Capture, Data Segment, and Data Mining

Data Capture

Data input can happen in several ways. One way is as the result of data entry. In data entry, data is placed in chosen fields of a database by a human agent using a device such as a mouse, keypad, keyboard, touch screen, or stylus, or alternatively, with speech recognition software. Data capture is a kind of data input in which there is no data entry. Instead, data is collected in conjunction with a separate activity.

One of the devices involved in data capture includes supermarket checkouts equipped with barcode readers. Barcode readers are electronic devices that use a laser beam to scan a barcode. These readers are categorized as non-contact automatic data capture devices. They need to be within a few inches of the material they are scanning to read it.

Magnetic stripe readers, also called card swipe machines, collect the information stored in the magnetic material that is found on bank, charge, and credit cards. This information often includes an account number, the customer’s own identification number, and other information. ATMs can also read this information. If the magnetic stripe is damaged or exposed to a strong magnetic or electrical field, the information will not be retrievable.

A point-of-sale (POS) terminal, through which credit card transactions are submitted and validated, reads the bank name and customer account number of a card swiped through a magnetic stripe reader. If the bank responds that the funds are available, the POS terminal transfers the approved amount to the account of the seller, finishing the transaction with a printed receipt.

Optical character recognition (OCR) involves the conversion of a digitized image of text created in print or handwritten to characters that are recognizable by word-processing programs. It is also used to preserve documents in an electronic format without having to re-enter data by hand.

Radio frequency identification (RFID) is a data capture technology in which identification of items is done through transponders that are attached to them. A transponder is a type of radio-relay equipment that is passive. Its function is to passively respond with a repetition of the original signal or a coded recognition signal when struck by an initiating signal. RFIDs work from greater distances than barcode readers can, which is one of their values.

Data Segmentation

Data segmentation is the process of taking your data and segmenting it so that you can use it more efficiently within marketing and operations.

Data segmentation will allow you to communicate a relevant and targeted message to each segment identified. By segmenting your data, you will be able to identify different levels of your customer database and allow messaging to be tailored and sophisticated to suit your target market.

Trying to understand the different characteristics of customers and prospects is a common challenge experienced by organisations today. Without this valuable information it is difficult to produce targeted and cost-effective communications. Marketing spend can be wasted on communicating with unprofitable customers and unlikely prospects. Resource can be ploughed into hitting the wrong target markets. Data segmentation can be used to overcome such issues.

Without data segmentation, an organisation can face various problems such as:

  • General lack of knowledge about their customer and prospect base
  • Untargeted communications producing low ROI
  • Poor customer retention
  • Incorrect messaging can lead to customer dissatisfaction

Data Mining

Data mining uses a relatively large amount of computing power operating on a large set of data to determine regularities and connections between data points. Algorithms that employ techniques from statistics, machine learning and pattern recognition are used to search large databases automatically. Data mining is also known as Knowledge-Discovery in Databases (KDD).

Like the term artificial intelligence, data mining is an umbrella term that can be applied to a number of varying activities. In the corporate world, data mining is used most frequently to determine the direction of trends and predict the future. It is employed to build models and decision support systems that give people information they can use. Data mining takes a front-line role in the battle against terrorism. It was supposedly used to determine the leader of the 9/11 attacks.

Data miners are statisticians who use techniques with names like near-neighbor models, k-means clustering, holdout method, k-fold cross validation, the leave-one-out method, and so on. Regression techniques are used to subtract irrelevant patterns, leaving only useful information. The term Bayesian is seen frequently in the field, referring to a class of inference techniques that predict the likelihood of future events by combining prior probabilities and probabilities based on conditional events. Spam filtering is arguably a form of data mining, which automatically brings relevant messages to the surface from a chaotic sea of phishing attempts and Viagra pitches.

Decision trees are used to filter mountains of data. In a decision tree, all data passes through an entrance node, where it faces a filter that separates the data into streams depending on its characteristics. For example, data about consumer behavior is likely to be filtered based on demographic factors. Data mining is not primarily about fancy graphs and visualization techniques, but it does employ them to show what it has found. It is known that we can absorb more statistical information visually than verbally and this format for presentation can be very persuasive and powerful if used in the right context.

As our civilization becomes increasingly data-saturated and sensors are distributed en masse into our local environments, we will inadvertently discover things that might be missed on the first pass over. Data mining will let us correct these mistakes and discover new insights based on past data, giving us more bang for our data storage buck.


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