The concern about data quality

Lately, the concern about data quality in the companies is growing, the called dirty data or bad data.

The dirty data are the data considered incomplete, inaccurate, incorrect, outdated, redundant or misleading.

All the companies around the world have problems with the data quality, to a greater or lesser degree, and a lot of companies ignore the issue of data quality or have underestimated the importance.

The poor data quality takes significant costs to the companies, and obviously, this reflected in the profit.

Nowadays, in the age of globalization, with a lot of companies acquiring others companies around the world, those problems are aggravated. Culture different and the complication to integrated data of companies, resulted in numerous ways of entering shipping, billing, invoice and other key data. This kind of problem with inaccurate data is typical across all industries.

Especially when you are talking about Business Intelligence and Data Warehouse. To be a successful BI, you need to have data accurate, because you can have a Data Warehouse correctly modeled , a well implemented BI using good tools, but if your data are not accurate, you will make decisions using unreliable information.

Data Governance

It is very important the companies create a policy of Data Governance, but is not so easy.

First of all, you have to know your data: checking what you have, if have IT projects duplicating information, which data is inaccurate, which data is reliable and which is not. It is important to communicate to stakeholders about the value of create a policy of data governance and the cost of not having it to the company. Successful data governance depends on sponsorship in management level, preferably the CIO. You need to treat your data as a valuable corporate asset.

Focus on create data quality processes, a master data management solution and establish a culture of data governance. Ongoing training both IT and users, always showing that the measures of data governance are helping to improve your data quality.

Define a data cleaning process, considering that to clean the data is very expensive to the companies. The better way to clean your data are define strategically, IT together with the business users, which data need to be clean based on the business impact. If the cost of dirty data on the business impact are greater than the effort and cost to clean, this data certainly should be clean.

To keep the data cleaned, is very important controlling who accesses the data, and which level of manipulation have, allowing only each person responsible for each process has access to input, update and delete the data in this process.

After all, the companies need to use data quality tools, to create mechanism of standardization and validation, so they can ensure that the new data entering in the systems are clean.