The Value of Data Integration

Our simplified approach to leveraging data as a strategic asset to create a competitive advantage groups activities into three distinct technology areas of focus: Data Integration, Data Management, and Data Presentation. These areas require best-practice approaches to handle the organizational dynamics and to successfully deliver these capabilities to the enterprise.

Data Integration

 Data Integration is an extremely valuable step in the process. Integration streamlines the data lifecycle to enable a data-driven organization by moving the data closer to the point of action.

Extracting the Data 

Extracting the data is the first critical step in the Data Integration process.   In its most simplistic form, extraction is about determining the best source of data and making it accessible for additional processing—either now or at some point later in the data lifecycle.

Combining the Data

Combining data refers to creating a unified view of the data that was extracted from multiple data sources. Your future state architecture will determine which data-combining techniques are best for your situation. Combining the data is a very complex step that requires detailed knowledge of the source systems, since data is stored in different ways in each data source. Planning is paramount, and combining data is essential for reducing data complexity, streamlining data connections, and increasing the value of your data through a unified view.

Standardizing the Data

Standardizing the data is a worthwhile step to ensure consistency of the data prior to sharing across the enterprise. This step increases the usability and quality of the data, which is crucial to gaining increased adoption and therefore maximizing the value of the data assets. Keep in mind that standardization increases the quality of the data but doesn’t ensure quality.

Cleansing the Data

The data cleansing process is a critical component of the data lifecycle that ensures the accuracy and integrity of the data. The integration of the data cleansing and data quality techniques should be a vital component of any data-related project, and various data quality techniques such as data validity, accuracy, completeness should be integrated into each step of the process. Data cleansing and quality are also ongoing processes that should be leveraged to continually monitor and assess the quality of data over time. Successfully implementing these components will increase the level of trust in the data along with increasing the data’s level of usability, adoption, and value. 

In Summary, Data Integration is an extremely valuable activity that improves your data assets and the re-usability of data throughout the data lifecycle. Successful Data Integration will provide lasting business value to business processes such as increased analytics, decision-making capabilities, more integrated business processes, and real-time data delivery that brings the data closer to the point of action. Having a clean, consistent, complete and accurate data set is essential for success. The more data that is leveraged throughout your organization, the more business value you will realize from your data assets.  


Written by Mike Sargo
Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.

14 percent of investment professionals use credit card and POS software data to derive data.

59 percent of executives say big data at their company would be improved through artificial intelligence (AI).

53 percent of CEOs consider themselves the primary leader of their company’s analytics agenda.

60 percent of businesses believe it is harder to source talent for data and analytics positions than for any other roles

61 percent of businesses that recognize the effect of data and analytics on their core business practices say their companies either have not responded to these changes or have taken only ad hoc actions rather than developing a comprehensive, long-term strategy for analytics.

Analytics leaders are nearly twice as likely as others to report enacting a long-term strategy to respond to changes in core business practices.

26 percent of businesses say data and analytics have significantly changed the nature of industry-wide competition.

8 percent of businesses say data and analytics have fundamentally changed the nature of industry-wide competition

50 percent of businesses say data and analytics significantly impacted new entrants launching data and analytics businesses that undermine traditional competitors’ value propositions.

30 percent of businesses consider the Spark software framework critical to their big data analytics strategies.


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