Turning data into information: the cost of making diamonds from coal - Informatics

Physician Executive, Sept-Oct, 2003 by Paul P. Antonecchia, Teresa Kryspin, Mike Summerer

Many executives and managers recognize the benefit of having solid information to make strategic decisions. Most fall to recognize the true cost of obtaining that information.

As with any process, there are quantifiable costs associated with taking any raw material (such as data) and refining it into a finished product (useable/actionable information). Diamonds are only converted from coal with the application of intense heat and pressure. So too, does data require the intense heat and pressure of processing to become useable information.

Such "heat and pressure" does not come without cost. Much of the process of turning data into information is hidden from the end-users of the information and is not factored accurately in the determination of whether the information is economically valuable.

The cost associated with processing data into information must be balanced against the benefit gained from the information. To have economic value, the benefit of the information must at least equal, if not exceed, time costs.

Information can be broken clown into four cost centers:

* Data collection

* Data input

* Data analysis

* Information reporting

Each of these can be broken down further into:

* Direct costs

* Labor--Dedicated staff to perform the functions

* Materials--Essentially the "raw" data used to perform the data analysis

* Indirect costs--Materials (or labor) that may not be directly attributable to the data analysis process, but are used to perform analysis functions (such as computers used for data analysis and employees who participate in facets of data analysis but are not dedicated it)

* Overhead costs--Costs (facility, maintenance, etc.) that allow for the data analysis to occur that are not direct materials or direct labor

Before embarking on a process of generating information, you should address each of the cost centers individually. This does not need to be done with accounting precision. It should be an exercise to identify potentially hidden costs in processing data into information.

Data collection can occur in many forms. It may require manual abstraction from review of records or it may be done electronically. For example, if you are abstracting data manually from a medical record, costs may include the direct labor of record abstraction as well as the simple costs of tools to document the data, such as a spreadsheet, database, or pencil and paper.

If data is being abstracted electronically costs may include time data analyst/programmer who needs to create and run the report and materials may include the computer hardware and software necessary to generate data.

While the data input step may at tinges be incorporated as part of the data collection process (or may replace it), many times it is a data processing step in its own right.

Manually collected data has to be entered into a data analysis tool, whether it is a manual tool or electronic database, for further analysis. Electronic data may have to be imported or interfaced from a data repository to your data analysis tool.

This function may also have its own associated costs, such as direct labor to manage and perform this task, as well as material costs if electronic interfaces need to be established between databases.

Ultimately, the goal of data processing is to take raw data and find some meaning in it, whether it is examining trends, testing hypotheses, establishing correlations, determining causes of outcomes or possibly being able to predict outcomes.

To take advantage of data, you must be able to analyze it and this usually requires a direct labor cost of a data analyst and may entail direct costs of a dedicated data analysis tool.

Once information is obtained, it must be communicated appropriately to those who have the ability to use it and make decisions. Communicating data starts with making it comprehensible to the decision makers.

Depending on the complexity of the information, this may require specialized tools/resources to present this information appropriately (direct material cost) as well as staff with specialized skills at data display or facilitation/presentation skills (direct labor costs).

In addition to depicting the information in an understandable format, it must be disseminated to the responsible decision makers. This may range from a minimal cost to prepare a report for a single individual or a more substantial cost to prepare reports/graphics/presentations for large numbers of stakeholders.

The economic value of information

Obviously, significant resources can be consumed generating informarion. Much of this process may remain completely hidden from the end user/decision makers.

There are anecdotes in every organization about reports being produced and distributed (sometimes at significant cost) to managers who admittedly find no value n the information.

For information to be of economic value, it must outweigh the cost associated with generating that information. If weekly length of stay data is generated in a hospital organization and distributed to all managers and there is ultimately a reduction in length of stay that translates to decreased cost to the institution, that information has proven economic value.