Business Intelligence - BI - Business Data Warehouse - BDW - Analytical Business Application - Solutions for Data Quality - Unise

By: A W Tunbridge Land Surveying  09-12-2011

Business Intelligence - BI - Business Data Warehouse - BDW - Analytical Business Application - Solutions for Data Quality - Uniserv

Incorrect, out-of-date or duplicate data - the basis for bad decisions

Incorrect, out-of-date or duplicate data from different IT systems is the biggest obstacle to comprehensive analyses, reliable evaluations and related BI solutions such as CPM. This can also mean that you do not identify potential business opportunities in good time, since the buying behaviour of the customer is analyzed incorrectly. You can only identify and assess risks inadequately and put pressure on your risk management, since your sales figures are out of date. You are also unaware of possible weak points in your company, e.g. material or supply bottlenecks, until it is too late. Poor data quality is responsible for this - with possibly fatal consequences which can range from a fall in revenue and the loss of market share to an increasing threat to competitiveness.

Business Intelligence silos prevent company-wide analyses

BI solutions are still used by departments with their own data marts or Data Warehouses in many companies and the data quality can therefore vary. The result: data is stored redundantly or in different formats. And while the data may be correct in one database, it may be incorrect or outdated in another data pool. Reliable company-wide analyses are only possible here with a considerable amount of manual correction. Not to mention the additional costs and the reduced capability of reacting to the uncertain analysis results.

Unused BI potentials through limited possibilities for evaluation

The better the data quality in the Data Warehouse, the more evaluation options are open to decision-makers and other users of the BI solutions. For example, managers should be able to define individual levels of detail in their dashboards. Staff in the finance department have to be able to prepare specific reporting themselves. Users should be able to develop analyses from different perspectives by means of OLAP analyses, e.g. sales figures according to region, product group, sales volume, etc. And warning indicators based on defined rules, e.g. too little stock on hand, should be guaranteed to reach the respective personnel.

Reduced confidence causes decisions based on gut feeling

If the provided information and analyses repeatedly lead to bad decisions on account of poor data quality, confidence in the capabilities of the BI system is reduced. This can mean that Business Intelligence functions are no longer used and are replaced by subjective assessment. More bad decisions are only a matter of time here.

Poor data quality puts adherence to your compliance requirements at risk

Inadequate data quality puts the accuracy and reliability of the BI evaluations at risk. This can have legal consequences and may entail high costs for the company.

Manual data cleansing as a brake on decisions and a cost driver

If urgently required data is not complete, error free and consistent, it has to be compiled, analyzed and prepared manually. This leads to unnecessary delays which you cannot afford, particularly in the case of time-critical business decisions. And it costs money and resources which you can use to better effect for other important projects.

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