The importance of data quality in the world of business

The key factor of a successful business is the data asset of the company and the quality of information that is based on it. Information related to business processes, customers and products is valuable only if it is accurate and reliable, which means that information quality meets the requirements of business analysts and sales managers. The need for quality data in the business processes might be at stake in several different situations. Here we give you a summary of those cases where data quality is essential and it can have a heavy impact on business efficiency.

Data cleaning, CRM, DWH

The core element of business information architecture is the company’s database where according to the different business needs data coming from transaction systems and outside sources are stored after being cleaned and consolidated. On this database rely all those business decision supporting tools that automate the production of reporting systems, enable quick, case-by-case analyses and are able to detect hidden relationships between business data. In business CRM solutions this central database is a key element.

Practical experience shows that in most cases the company’s data asset does not possess a good quality that is needed for building a Data Warehouse or for the implementation of a CRM system. Even if data quality from the point of view of the company’s basic systems is adequate (or rather acceptable), this does not mean that this will be enough for the Data Warehouse or the implementation of the CRM system. Data Warehouses and CRM solutions both function based on several data sources, and the databases of the sources can have different structures and data content problems. This can lead to a redundancy in the collected data content jeopardizing thus the functionality of the Data Warehouse and the CRM system as well.

These problems are known only partially, mainly on a qualitative level for the company’s experts, so the probable consequences will show only in a later phase of the Data Warehouse and CRM implementation projects when the alternatives for corrections are much less, more limited, and they can be done at a much higher cost as than if they were planned from the start.

Central Customer Database

The most important characteristic of a central customer database is that it collects all customer data of the connected systems together with their system identifier. Before the implementation of a client database a thorough data cleaning is needed that will reveal the duplicated items both in and between the systems. After detecting the duplicated items these will receive a master identifier and together with this ID will the data coming for the source systems be implemented into the central customer database. This way the users of the system will know which records belong to the same customer within each system.

Data Migration

Companies that would like to keep up with the accelerated evolution of today’s information technology developments would like to switch form their existing product and data management systems to those that are more up-to-date. With the implementation of these new systems data migration from the existing systems cannot be avoided. During this migration process the same data quality issues might arise that are present at the implementation of a CRM system or a Data Warehouse. For a successful migration data need to be prepared and data quality issues must be faced in order to manage properly erroneous, duplicated and redundant data. If these issues related to data quality improvement are not handled with priority, this will lead to the new system’s inefficiency and the business domains will feel that the new application does not meet their requirements.

Daily operation

Data quality issues and inadequately defined data management issues can be a problem not just at the implementation of a CRM system or a Data Warehouse but also on a daily, operational basis at companies that work with large volumes of data.

In some cases the customer of a transaction cannot be identified easily and this leads to insecure and slow customer service provision. This can be a real problem in a bank at a money withdrawal process when data to identify the customer are missing from the system, and the clerk has to confirm the validity of the payment based on customer documentation. For the customers these verification processes mean longer queueing time, not even mentioning the problems caused by missing customer documentation. These issues related to data management mean unnecessary workload and in some extreme situations can even lead to losing customers.

Similar difficulties can be found in product data for inappropriately treated item strains. In case of inventories or product supplying processes having the same product in the system by several other names can cause problems with financial implications. If, according to one of the product records, the inventory is at minimum stock and the other is at maximum stock, placing an order based on the first version can cause serious problems, especially if it is a perishable item.

Latent costs

Related to daily operations it is important to notice the risks and costs that inadequate data quality can lead to.

Work that is based on inaccurate information is accompanied by latent costs that is paid by the company without even being manifest in the company’s costs. The causes of these latent costs:

  • Time and energy spent on subsequently acquiring the missing information
  • Time and energy spent on improving inaccurate, missing information

On the other hand, if there is a continuous data quality assurance and accurate information is provided, the costs are considerably lower.

It also has to be mentioned the damage resulting from those decisions that were previously made based on inaccurate information. An example can be here the marketing letter sent to an incorrect address, or the market targeting process chosen for a newly introduced product when this is based on erroneous data.

Data quality assurance

The data quality and proper data handling has to be the main focus in daily operational processes, at the implementation of CRM systems and Data Warehouses as well. The following words of Philip B. Crosby highlight the risks lying in working with poor quality data that in today’s information society cannot be brushed aside:

Quality is free. It is not a gift, but it is free. What costs money, a lot of money is the absence of quality, the cost of all those activities that need to be done because the same work was done improperly.

DSS Consulting