Limited Confidence

Few organizations can assert full confidence in the data they currently capture and maintain. PricewaterhouseCoopers's Global Data Management Survey 2004 found that only 34 percent of survey respondents (representing international Fortune 500 companies) were very confident regarding the quality of their data.

Everyone knows that relying on data of dismal quality can lead to all manner of harmful and unintended consequences, from the poorly filled customer orders that can cost an organization business to the weak financial record keeping than can end up sending directors to gaol. The danger for companies that cannot rate their level of data quality, and hence cannot decide whether to trust that data or not, is that they will fluctuate between either blindly accepting the data that leads them into flawed business decisions, or - having identified a serious data quality issue - refuse to accept the validity of the data for ever more and revert to basing all business decisions on gut feel and experience.

Luckily, given that high-quality data is sometimes simply unobtainable and sometimes simply too expensive to obtain, perfection is not always necessary for many planning activities in the enterprise and vital decisions can and must be made with dysfunctional data.

"I think in most decision making, certainly in business decision making, many of the strategic decisions are made on less than perfect data from the operational level," comments Andy Koronios, a professor with the School of Computer and Information Science at the University of South Australia.

In a recent piece called "You Can Make Good Decisions From Less-Than-Perfect Data", AMR Research vice president research Bill Swanton points out that while no one can argue high-quality data for transaction systems is not essential, the picture gets murkier once the organization moves beyond transactions. In these cases, Swanton argues, high-quality data is neither obtainable nor even necessary for many planning activities in the enterprise.

"Valuable decisions can and must be made with less-than-perfect data, and the sensitivity of important applications and decision tools to data quality must be well understood," Swanton writes.

Measuring data quality can be as much art form as science, Preston admits - there is a very creative, expressive component to such work as well as a metrical element.

Need to Integrate

One problem that repeatedly confounds many organizations is a failure to better integrate data for informed decision making, with too many strategic decisions being based on imperfect data from the operations area. For instance a manufacturing company might know the life expectancy of a particular machine but still need to decide whether to drive the machine at a particular level (be that 100 percent or somewhat less than that) in the face of the risk of degrading its life expectancy.

Koronios says data from embedded systems should not only be used in identifying and forecasting the health of the asset (whether the asset is going to fail or not) but also in informing design of the asset's future replacement: providing historical data about failure rates in the name of improved design, for instance. Further, Koronios says much of the data coming in from the manufacturing and asset management areas - along with that collected from embedded decisions - may or may not be dumped into a data warehouse. Either way the organization is faced with having pools of data available that do little to inform decision making at either the business or design level.

"There is so much data actually being pumped into systems these days that at the management level the bandwidth of the individual cannot actually handle the amount of data unless it's processed in the right way: highly-processed data, highly-processed information so that in fact only the relevant data - the data for management requirements - actually reaches the strategic decision makers," Koronios says. "The operational data remains with the operational people.

"Very little of that is happening in organizations. At the moment we are doing some work with large organizations in Australia, and they have exactly that particular problem. In other words, yes, lots of information comes from the shop floor, lots of information is pumped into data warehouses, lots of information is pumped into ERP systems, yet there's a disconnect between that and the people actually making the strategic decisions about the organization."

The answer, according to Koronios, is better integration and finding enhanced ways to summarize, synthesize, analyze and visualize data, although he admits that while much work has been done in systems integration there is still much more to be done. To this end the University of South Australia is a member of a Cooperative Research Centre (CRC) joint effort in conjunction with major organizations in Queensland and NSW designed to address the issues. The main complaint of the organizations, he says, is that they lack the correct data - the precise data - needed to make informed decisions.

"It appears to me that many of the CEOs and below at the strategic level are actually making decisions on imperfect data every day," Koronios says.