The owners of a local coffee shop and the CEO of GE have one very important thing in common. They need information to make business decisions. The difference is in how they accumulate and access the right data to make these critical decisions. Large or small, we all face the issue of turning seemingly unrelated documents, files, and papers into electronic data that we can analyze, weight the risks and benefits, then make our decisions. Data capture is the extraction of data from hardcopy or electronic files into a useable electronic format.

Data capture should not to be confused with document capture. Document capture is the process of digitally scanning paper documents, also known as imaging, to create electronic photocopies of the original source document. Document capture creates an image that is a picture of the document, whereas data capture turns these images into editable / searchable text. These text files are used by organizations to increase efficiencies, perform research and increase sales.

Just about every industry requires the process of data capture. Some applications are:

* Business and Retail - A/R, A/P, payroll, service records. inventory.

* Insurance - claims, applications

* Medical - patient records, radiology reports, patience care plans and profiles.

* Financial and Banking - statements, applications, retirement plan records, checks.

What resources are available to you that can make this happen? Some organizations have the necessary resources: personnel, computer network, technical staff including programmers, management time, capital and training capabilities to implement data capture in-house, while other organizations may not be well suited due to the nature of their core business. In either case, a thorough needs analysis that outlines what your current costs are, as it relates to the data that you are trying to capture or what the cost is to your business by not having the data. A key question one may ask themselves, "What is the key data element to be captured?" For example, the program designation officer for a government client of ILM Corporation is able to key from scanned forms. They had this to say, "The data collected from the source documents is the heart and soul of the office functionality. Without this information, our office is put in a very difficult position of administering the program." For some organizations, their data is their business, while for other organizations it is a means to an end.

METHODS OF DATA CAPTURE

There are essentially two methods of data capture. Capture from paper or from electronic images. The decision to capture from paper or images has many variables and will depend upon your specific requirements. Some of the variables are: Do you have a use for the images? Are the paper documents good candidates for scanning? Does it make good business sense to electronically archive images rather than keep the paper documents for your retention requirement? Is there an advantage to scanning documents at remote locations, but centralize the data capture process? These and many other questions are required to make the decision to capture data from images or paper.

From either paper or images, to capture the data requires either manual data entry or machine recognition. More often than not, there is a combination of manual data entry and machine recognition. These two are combined to maximize the benefits that each has to offer. Manual data entry requires a person to manually key enter the information or validate the results from machine recognition. Machine recognition is the process of using computers to interpret hand, written or computer print characters into electronic data.

There are several proven machine recognition technologies available:
* OCR - Optical Character Recognition: good for capturing data that is computer printed; medical claim forms and invoices.

* ICR - Intelligent Character Recognition: good for capturing data that is handwritten; sweepstakes or rebate forms, credit card applications.

* MICR / OMR - Magnetic Ink Character Recognition / Optical Mark Recognition: checks or standardized tests.

* Barcode: a series of thin and thick black lines in defined patterns that represent a numeric or alphabetic character; inventory control labels, claim forms, UPC.

We've discussed what data capture is, who and how it is being used and some of the high level technology being implemented, but how do you begin the process of implementing your data capture project?
A clear set of objectives, outlining in detail how you will use the data (database, Website, archival, or publishing) and what business decisions will be made from the data, is the first place to start, Once you have a stated goal, for example, "I want to be able to image and perform data capture on critical fields from my hills of lading so I can analyze distribution routes and delivery times in my database, and I also want to be able to provide copies of the bill of tailings to my customers on a secure Internet web site." This clearly stated objective will be the starting point to help you identify what technology, staffing and business changes will be required.

For some organizations the objective can be as simple as, "Decrease the time it takes to search for documents." What are your considerations for: turnaround time (how long it takes the entire process before you have usable data), accuracy (what is the level of integrity of the data), price (what is the cost per data element). Rarely will an organization be able to achieve maximum results of each, without sacrificing, to some degree, one of the three.

There are several functional requirements to implement a data capture program such as: representative samples of the documents, a well defined record layout defining which fields are to be captured, a structured record output (what the data is to look like to export to your data management software), budget, and documented instructions. This high-level roadmap will allow you to focus on each requirement and show how one may affect the other.

Whether you choose to implement data capture in-house or outsource, the items discussed here should be the first things to consider. Many other factors can, and should, influence your final decision. Carefully consider your needs, and make the decision that best fits your organizational requirements. There is no reason that you cannot have the best information to make those critical business decisions. The past few years have demonstrated the value of data, and data analysis, in operating businesses.

Your company's data is its greatest asset, be sure you use it effectively.

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