Data Denial and Business Intelligence – How to Achieve Data Quality
The greatest battle you may face inside the organization will be to get management to the point where they agree that data quality is a goal even worth considering.
Everybody talks about data, but many often confuse it with information and knowledge. Basically, data is a core corporate asset that must be synthesized into information before it can serve as the basis for knowledge within the organization. Nevertheless, data is ubiquitous – it is used to support every aspect of the business, and is an integral component of every key business process. However, incorrect data cannot generate useful information, and knowledge built on invalid information can lead organizations into catastrophic situations. As such, the usefulness of the data is only as good as the data itself – and this is where many organizations run into trouble.
Many organizations neither recognize nor accept the bad quality status of their data, and try instead to divert the attention to supposed faults within their respective systems or processes. To these organizations data denial has practically become an art form, where particularly daunting corporate barriers have been built – typically over long periods of time – to avoid the call to embark on any “real” Data Quality improvement initiatives.
However, we have found that the best way to measure the extent to which your organization may be dealing with data denial is to ask the following key questions:
- Are you aware of any Data Quality issues within your company?
- Are there existing processes that are not working as originally designed?
- Are people circumventing, the system in order to get their work completed?
- Have you ever been forced to deny a business request for information due to an issue of Data Quality?
- If the system was functioning properly, would this information have been readily available?
- Has a business case been made outlining the economic impact of this issue? And, if so, has it ever been addressed with the organization’s leadership?
- What was the response to these issues? And if there was no response, what is stifling this process?
- What causes these “gaps” in Data Quality?
- How are these issues affecting the responsiveness of your organization (i.e., to customers, stockholders, employees, etc.)?
- If these issues were to be addressed and corrected, what strategic value would be added or enhanced?
- Who bears the responsibility for addressing these issues within your organization?
- What can be done to address these issues in the future?
- What support is needed to implement a Data Quality data hk strategy?
Depending on the answers to these questions, your organization may already be facing significant barriers to attaining Data Quality, each of which will need to be identified, assessed, prioritized and corrected. According to William K. Pollock, president of the Westtown, PA-based services consulting firm, Strategies For GrowthSM, “Most companies already know what data they do not have – and for them, this is a significant problem. However, the same companies are probably not aware that some of the data they do have may be faulty, incomplete or inaccurate – and if they use this faulty data to make important business decisions, that becomes an even bigger problem”.
Common Problems with Corporate Data