Melissa IN Team | Data Integration, Data Quality | data integration, data management, data profiling

The growth of a company is accompanied by a growth in its product range, supplier network, distribution channels, customer base, etc. This means more data. When it comes to data, quantity is secondary to quality.

For businesses to be able to use the data available to them for making data-driven decisions, the data must be accurate, complete, comprehensive and valid. The only way to achieve this is by collecting and cleaning data gained from all the varied sources and creating a central, accessible system through data integration.

What is Data Integration?

Businesses deal with huge volumes of data. Data integration is focused on taking all the data from multiple sources, cleansing it, finding connections and creating a single source of truth for everyone.

Look at it this way. Siloed data is like chapters of a story. Each chapter says something but it is only when all the chapters come together that the story is complete and makes sense. Just as you may be wrong judging a book on the basis of only one chapter, business decisions taken on fragmented data may not always be correct. Businesses are not aware of this and it is one of the reasons why they continue to make many decisions based on their ‘gut feel’ rather than data.

The Need for Data Integration

Data integration involves extracting data from various sources, cleaning and enriching it and transforming it to reveal strategically vital information. Some of the ways enterprises can use data integration solutions are:

• Generate actionable insights that help decisions makers strategize the company’s future
• Create comprehensive customer profiles  to target the right audiences
• Transform data to meet required business intelligence structures for dashboards, reporting, advanced analytics, etc.
• Track and monitor the flow of data through departments and processes
• Improve visibility to inspire trust within the organization and between customers and the business.

Of course, for this to happen, the data used must meet high quality standards.

What is the Relationship Between Data Quality and Data Integration?

Data quality and data integration have a symbiotic relationship. The core idea behind both is to make data usable by de-fragmenting it and addressing defective data.

Cleaning and analyzing data is one of the first steps towards data integration. There are 6 key dimensions to measuring data quality: accuracy, relevance, completeness, validity, consistency and uniqueness. It is important to note that data that meets quality standards for a singular source may not be considered high quality for the organization as a whole. This is an important point to be kept in mind when working on data integration. 

This initial profiling helps organizations understand the source data and align it to their preferred structure and format. Inaccurate data is highlighted and corrected while gaps in information are filled. For example, a spice retailer may label spices as whole spices and spice powders while the powdered spice vendor may mention only the spice. So, as part of the data cleaning process, all entries with the word ‘turmeric’ may be changed to ‘turmeric powder’.

Data quality is not limited to these initial checks. This is an exercise that must be conducted regularly to ensure that the data does not get obsolete with time. As records from different sources are integrated, gaps in information may be filled, duplicate records may be identified and removed and data may be updated to reflect current values.

Drafting a competent data quality management policy is key to achieving this.

Ensuring Good Data Quality during Data Integration

Here are a few tips to help create a high quality, integrated database.

• Profile All Incoming Data

Profiling data helps differentiate between good and bad data. Inaccuracies and gaps in information can be flagged so that data stewards can address the issues and improve data quality.

• Set Data Standards

To ensure standardization of data entries and formats, the standards must first be established. For example, when dealing with customer data, the organization may decide that addresses must contain pin codes. If an address is found without a pin code, the same may be generated on the basis of the street address entered.

Similarly, to minimize the risk of duplication, the organization may decide to use full forms of street names. So, ‘M.G. Road’ may be changed to ‘Mahatma Gandhi Road’.

• Deal with Duplicate Data

As data from different sources are integrated, duplicates are bound to appear. The organization must have a good master data management (MDM) practice in place to know how to deal with duplicates. This means defining what data is to be kept and what is to be deleted. In some cases, companies may choose to save the most complete version of data while in others, they may save the newest data record.

For example, when dealing with clothing sizes, saving the most recent data may provide a more reliable customer profile. On the other hand, in the case of address duplication, saving the records with the most complete address would make more sense.

• Make Data Management Part of the Data Lifecycle

Data quality management must be an ongoing effort. Before data from different sources are integrated, it must be raised to a minimum quality level. From then on, data must be examined regularly and referenced against third party databases or internal trusted databases to ensure that the records match the organization’s data quality standards of accuracy, completeness, validity, uniqueness and formatting.

In Conclusion

In today’s world, irrespective of the field, businesses need reliable data to make the right decisions for their future. Implementing data integration practices creates a unified view of the available details and helps them realize the true value of the data held. Though data integration techniques are still evolving and there isn’t a universal approach that works in all scenarios, one thing is certain, data quality goes hand in hand with data integration. All data must be correct, valid, complete and unique. It is only then that the golden records created can be considered reliable and usable.