Data warehouses of an organization are filled with data which would reflect all the activities within the group. Data may come from various sources and gathered using routing business processes. It is imperative that the processes in the data warehouse should be precise and accurate because the usefulness of data goes far beyond the software applications that generate it.

All companies have been depending heavily on data from the business data warehouse for decision support systems. Data are frequently integrated with many other applications and connected with external applications over the internet so data is continually expanding at such tremendous proportions.

Data quality has been a persistent problem for many data warehouses. Data managers or administrators have found it a cumbersome task to fix erroneous data or changed processes to ensure accuracy and less important data have been overlooked. Business companies have taken great efforts to have data warehouses with data quality requirements and they make intensive assessment an integral part of any data project.

In order to achieve Data Accuracy and good quality, data professional should understand the fundamentals of data which are quite simple.

IT professional unanimously agree that Data Accuracy is a strong foundation in the data quality dimension. If there is wrong data in the warehouse, a wave of negative effect flows through the whole system.

The quality of data has many dimensions. These include accuracy, timeliness, completeness, relevance, easily understood by end users and trusted by end users.

Of these dimensions, Data Accuracy is the most important as it represents all business activities, entities and events. Two important requirements should be met for a data to be accurate. First, it has to be or the right value. Second, it has to precisely represent the value in consistent form in accordance with the business data model and architecture.

There are several sources and causes of data inaccuracy. The most common of these causes come from initial data entry of users. In simple terms, it means that the user entered the wrong value. This could also be that typographical errors were committed. This is an aspect that can be overcome by having skilled and trained person do the data entry. Or since mistakes can happen to everybody, data inaccuracy from data entry can be overcome by having programmatic components in the application detect typo errors. For instance, many applications have spell checks or some web forms like combo boxes offer a list of possible values there can be no mistake in typing.

Data Decay can lead to inaccurate data. Many data values which are accurate can become inaccurate through time; hence data decay. For example, people’s addresses, telephone numbers, number of dependents and marital status can change and if not updated, the data decays into inaccuracy.

Data Movement is another cause of inaccurate data. Data warehouses extract, transform and load data very frequently within a short period. As data moves from disparate system to another, it could be maybe altered to some degree especially if the software running the database is not very robust.

Data Accuracy is a very important aspect in data warehousing. While the problem can still persists, companies can have measures to minimize if not eliminate data inaccuracy. Investing in high powered computer systems and top of the line database systems can have long term benefits to the company.