Looking Forward to Enhancing the Quality of Data in Data Warehousing? Take Note

One of the most crucial aspects of the data present in any data warehouse is its quality. Since data warehousing services have been used extensively to amp up an enterprise’s decision-making process, the quality check of data becomes necessary. Data quality issues can hamper the project anytime, anywhere. That’s why it becomes extra important to keep the quality of data in check.

Now the question arises, how to accomplish this? Our below-mentioned comprehensive guide is the answer. Have a look:

1.       Data collection: A lot of enterprises are dependent on specific ETL tools. They help in readying their transactional data for OLAP. The effectiveness of these tools is directly proportional to the quality of data that is currently present in the system. This makes it important to use data quality checks at the beginning itself i.e., starting from the data collection process.

This can be understood from the following example: When the customer feedback is collected, various ad-hoc information or comments may also be there.  This makes it difficult for the user to separate valid feedback comments from the invalid ones. To undo this, techniques including parsing feedback text for specific keywords, text mining algorithms, etc. are leveraged by the organizations.  Consequently, ETL offload becomes smooth, and data quality remains in check from the very initial stages of the data warehousing projects, fostering effectiveness.

The data collection process can be viewed from both implicit and explicit perspectives, and proactive data quality measures have proved to be beneficial in both cases.

2.       Data cleansing: Data cleansing process is crucial for any data warehousing project. However, it’s not easy to ‘cleanse’ the vast amount of data present due to obvious reasons. There are tons of terabytes of data present in the system that makes it hard for the user to weed out the invalid content. Such issues of data handling do not arise if strict data quality check policy is in place from the very initial stages of data collection and data modeling.

If we talk about the best methodology to proceed with data cleansing, having a good knowledge of the source data will do for starters. The next comes laying down basic ground rules for the data quality check.

Many times, complete data is not provided by the client; only the trial data is made available. No matter the amount of data in hand, there must be ground rules in place for data quality checks. This is in direct relation with the fact we discussed above – the more the user understands the source data, the easier it would be for him or her to set data quality rules. This further boosts the possibility of successful data migration, be it Teradata migration or otherwise, for the client.