Decision-makers leverage actionable insights to drive value. The fact is that actionable insights play a crucial role in enabling organizations to make confident decisions and ultimately improve the ease of doing business. For companies to strengthen their “decision muscle” and drive value much more quickly, the implementation of extraction, transformation, and loading of customer data to deliver actionable intelligence is a crucial challenge.
ETL processes are tough to scale. Companies require IT integrators to develop and maintain scripts that facilitate data flow in a streamlined way. As the schemas or APIs change, the data engineers scramble to update the scripts to accommodate the change. Consequently, these companies are more likely to face downtime and suffer owing to heavy operational overheads. With data being ingested from multiple, highly complex data sources, the IT integrator teams find it tedious to maintain and refurbish pertinent ETL flows.
The step of creating source-to-target mappings to facilitate data transformation presents yet another roadblock. Technical teams struggle immensely when the underlying source and target systems undergo a change. That creates a plethora of problems, such as missing information and data inserted into the wrong data fields. Incorrect ETL data mappings ultimately risk organizations’ ability to make informed decisions, which leads to missed opportunities and lost revenue.
Businesses can resolve these issues and drive outcomes by transforming their ETL-powered approach. By reinventing the way they extract, transform, and load complex data streams of customers using self-service, organizations can not only make informed decisions but also improve their ease of doing business and deliver delightful customer experiences.
Transform ETL through Self-Service
To create strong ETL flows, the majority of companies have to spend weeks or months mapping multiple source data fields to target data fields or schemas. In the process, they have to perform long hours of coding and extensive EDI mappings. However, it takes time to create connections from data sources to target schemas. Complex, bi-directional data feeds along with disparity in semantics have turned the data mapping process even more challenging. While already scarce data integration developers map and integrate complex data feeds, customers are forced to savor the value they’ve been promised, and ultimately companies witness a financial decline.
The problem worsens with the growth of data sources and the degree of heterogeneity. Extracting data from multiple unstructured data sources, for example, web pages, text, and emails, and then transforming and loading it into a data lake or database is tedious and costly. As a result, the decision-making ability of the company takes a toll, and they fail to create new revenue streams and expand market share.
Companies that supercharge their ETL capabilities using self-service, companies can empower their non-technical business users to implement new data connections in minutes instead of months. These reimagined ETL data integration solutions enable non-technical business users to onboard customer data up to 80 percent faster. Because business workers connect with customers much more quickly, they can address and meet the needs of those customers without delay. This fills customers with delight and satisfaction. Business users can rely on AI and ML to map customer data securely, smartly, and easily. They can leverage easy screens, machine learning, and security protocols to onboard and use complex data and stream it in real time to conduct modern-day business transactions.
At the same time, IT becomes free from creating custom codes and EDI mapping, and they can focus on other priority projects instead. Thus, the productivity of both business users and IT users increases by leaps and bounds.
A Road to Better Decision-Making and Faster Revenue
When companies leverage self-service data integration solutions, they can reimagine their ETL processes and turn them far more resilient and well-instrumented to drive decision-making and value generation. While it puts the onus on non-technical business users to perform data mapping, data integration and more, it keeps IT in control of the companies’ data governance.
What’s more, companies that can transform the way they extract, transact, and load customer data will be successful. They’ll be able to make informed decisions, accelerate operations, create stronger customer relationships, and expand a larger market share.