Role of Data Integration in Data Analytics

They say go with your “gut feel” when making important decisions, but as data engineers and data scientists, you do not have the luxury of going with your gut, do you? Firstly, it is not you who is making the decision, and even if you are, it is better off going with a combination of your gut plus data points.

The explosion of data-capturing technology has created a Big Data phenomenon – an entirely new class of data analytic tools and techniques that allows business users and analysts to become data analysts. However, with data spread across multiple systems and platforms (such as OnPremise and OnCloud), one cannot make use of these dynamic data analytic tools/techniques until the information is integrated.  

DBSync’s Cloud WorkFlow Enterprise–a mature and fully-featured Integration Platform-as-a-Service (iPaaS) built for the cloud as well as OnPremise systems–is the integration answer. Through its comprehensive, cutting-edge framework and architecture, it enables business users and technical analysts to accelerate analytics through various platforms like Redshift and other cloud warehouses. DBSync’s iPaaS collects data from multiple sources in the front, middle and back offices, and feeds it into the data analytics systems. Unlike other heavy OnPremise legacy tools, DBSync can integrate data across platforms, solving the common problem of having disconnected, channel-specific systems in place that work well in their closed environment, but completely fail when used together with other channel or cloud systems.

Consolidated View

DBSync combines ease of use, sophisticated transformations, extensions for on-premises, and cloud integration with a managed list of close to 30 different systems called “DBSync connectors.” Integrating data from various sources into a single, consolidated view has never been more straightforward. As with any integration solution, integration begins with the ingestion process and includes steps such as cleansing, ETL mapping, and transformation. This type of integration ultimately enables analytics tools to produce useful, actionable business intelligence with a constant feed of data, on a “real-time” or timely basis. 

Data Governance

Analytics performed on top of incorrect data yields incorrect results – this can be detrimental in the quest to operationalize innovation. Data governance is of primary concern to IT organizations charged with maintaining the consistency of data routinely accessed by citizen data scientists and citizen integrator populations. Gartner estimates that only 10% of self-service BI initiatives are governed to prevent inconsistencies that adversely affect the business.

Data discovery initiatives that use desktop analytics tools risk creating inconsistent silos of data. Cloud data warehouses afford increased governance and data centralization. DBSync helps to ensure robust data governance by replicating source tables into Redshift clusters, where the data can be synchronized at any time interval desired, from real-time to overnight batches. In this way, data drift is eliminated, allowing all users who access data (whether in Redshift or other enterprise systems), to be confident in its accuracy.