High-quality business information is a vital resource for business professionals, especially those involved in customer-centric functions such as sales and marketing. Despite this, such information users often spend a great deal of time searching for information to perform their jobs by surfing the Internet, purchasing commercial reports, monitoring news alerts, scanning trade press and wire services, networking through personal contacts and phoning into organizations.

The time and effort involved in synthesizing information into a consistent whole is significant - too great, in fact, to use anything less than the best "raw material" available. Industry studies have shown that poor quality data results in extra costs, poor decisions, delays in key projects or initiatives, failure to meet a contractual or service-level agreement and lower sales performance. Many organizations sense that information is an issue but do not know how to fix it.

All data is inherently prone to some inaccuracy due to data entry errors, interpretation and categorization decisions and translation errors as it travels from source to database to report. Also, it is impossible to reflect all the changes that occur in the world in real time, so there is almost always some delay in the updating of any information delivery system. Few information users have a comprehensive understanding of the complexities involved in gaining information on companies, industries and personnel. Many understand that data cannot always be kept perfectly accurate and up to date - but still desire access to the highest quality information available at any given moment.

One simple measure of quality has been around for years: direct marketing firms have long established an error rate of as much as 10-15 percent in their mailing lists as an acceptable, or to put it better, expectable level of incorrect information. For some other types of business information, a quality level of 85 percent may actually be hard to attain. Conversely, financial information drawn from public company filings should provide highly accurate snapshots of business performance - more than 99 percent reliable.

One of the keys to obtaining the best quality is to blend data from several sources, taking advantage of their individual strengths. To examine data quality, though, we must first examine the key components by which we can define data quality.

Components of Business Information Quality
Many information experts define quality along three fundamental axes: accuracy, timeliness and completeness. These three factors form a core measure of the usefulness of information.

Accuracy
A data item is accurate if it is generated without error. For instance, if the outside temperature was 68 degrees this morning and the weatherman''s thermometer read 68 at the time, then the data is accurate. If the temperature rises to 71 degrees but the weatherman still gives out the old reading, then his report is wrong, but the 68-degree reading is still accurate - for the time it was taken. On the other hand, if the thermometer is broken and always reads a few degrees too low, then the readings were never correct and we would call that data inaccurate.

The truth is, all information sources are not equal and data error rates vary across different primary research firms. While content has many nuances and imperfections, human-driven quality control is labor-intensive and expensive. Machines can check for the presence of garbled or invalid data and inconsistent values (e.g., a firm with five employees but $500 million in revenues) but cannot always verify if a value is correct. Content providers with a track record of accuracy employ a mix of automated and human techniques for minimizing errors.

Timeliness
As mentioned, there is a difference between information that is incorrect as the result of an error and information that was correct yesterday but is no longer correct today. Both bits of data are wrong, but they are wrong for significantly different reasons. The timeliness of business information is absolutely crucial to its quality. Different types of information have inherently different levels of timeliness. News and articles are typically available within minutes of public release. U.S. public company quarterly financials must be submitted to the Security and Exchange Commission (SEC) within 45 days, and annual results must be filed within 90 days. In the U.K., publicly traded companies have up to seven months to file annual financials (no quarterlies required); in other countries the delay is even greater.

For U.S. private company data collected by content providers, the average update cycle is 15+ months. That is because private companies are not subject to the same filing requirements as public companies. The timeliness of private company data is all about collection, not publishing.

A good information source is updated as frequently as each type of content allows. An outstanding information source monitors the business news stream, extracting breaking news and updating information immediately, based on real-time events.

It can be argued that a field without a value is just as "wrong" as an out-of-date value or a flat-out data error. Of course, in practice, business professionals are really only concerned with the completeness of information they need. We call that completeness-to-purpose. If someone is only concerned with public companies, then their expectation of completeness should be much higher than it would be for private companies (because we know that a standard set of data is available for each public company, while private company data is more difficult to obtain).

For example, management consultants developing client strategies require detailed industry data, market share statistics, analyst reports, competitor profiles and financials that can help them assemble benchmarks and generate comparables. Providing this complete set of information in a single place represents a higher-quality resource than if the user had to access five separate sources individually.

With business information, usually "less is more," because pertinent information is what most are really after, and the less filtering the user has to do, the more productive the user becomes. A good information source should include all relevant content and exclude all irrelevant content.

For instance, very few business professionals would find a news search useful that returns 1,525 "relevant" articles - it is just too much to wade through. If a search on the phrase "MegaCorp funding" returns a community relations release on "MegaCorp Family Day Generates Funds for Charity," that result is not relevant to the task at hand. Delivering relevant results in response to searches and filtering operations depends on how information items are linked to companies, industries, geographies, people and business topics such as "executive change." This interconnection increases the relevance of every search and filter operation conducted.

Obviously, poor information can lead to poor decision making. But lack of data quality hits even harder when you consider the enormous amount of time and money spent by business professionals in direct pursuit of information.

The time and effort involved in normalizing, cross-checking, converting and combining information into a consistent whole can be minimized, but it requires a clear picture of what constitutes good data quality - in the context of how it will be used. Most business people conduct these activities to the best of their ability within the time available . So, ability and time also constrain how much value is obtained from research tasks.

To ensure data quality, businesses must understand how to optimize the components of quality for specific applications and groups of users, then invest in information resources and technology to drive improvements. Setting goals based upon improvements in specific business tasks is more useful than trying to achieve the perfect data set. By better understanding data quality - and how to attain it - organizations will decrease these costs and benefit from a significant return on investment.

Beth Jacaruso is the vice president of Content at OneSource Information Services. She brings more than 16 years of experience to OneSource with expertise in information management research and development, knowledge management, data analysis and content management. Jacaruso is responsible for managing OneSource''s global repository of business information and developing information products to support sales and marketing users in financial services, professional services and high-tech businesses.