Actionable insights are key enablers for decision-makers. They play a fundamental role in helping companies make confident decisions and improve ease of doing business. For companies to strengthen their ‘decision muscle’ and ultimately drive value, the implementation of extraction, transformation, and loading (ETL) of customer data to deliver actionable intelligence is a significant challenge.
ETL processes are often difficult to scale. It requires full-time data engineers to develop and maintain the scripts that keep the data flowing. Every time there’s a change in schemas or APIs, the data engineers scramble to update their scripts to accommodate them, resulting in downtime and high operational overheads. With data being ingested from so many disparate (often fast-moving) data sources, teams find it tough to maintain and refurbish critical ETL flows.
The step of creating source-to-target data mappings for enabling data transformation presents another challenge; it leaves technical teams grasping at straws especially when the underlying source and target systems change. This increases problems such as missing information and data inserted into the wrong fields. Incorrect mappings ultimately risk organizations’ ability to make decisions, leading to missed opportunities and lost revenue.
To resolve these issues, companies need to transform the way they extract, transform, and load customer data using self-service.
To develop strong ETL flows, most companies spend weeks to map different source data fields to target data fields or schemas. A lot of times they have to hand-code or rely on complex EDI mappings to build the connections from the data source to the target schema. Complex data sets and variation in semantics have turned data mapping harder and more complex. While scarce data integration developers map and integrate complex customer feeds, customers are waiting for savoring the promised value and organizations are experiencing a financial decline.
The problem escalates with the growth of a number of data sources and the degree of heterogeneity. Extracting data from unstructured sources e.g. text, emails and web pages and in some cases custom apps and then transforming and loading it into a data warehouse or data lake is arduous and expensive. As a result, a company’s decision-making capability suffers and so does its efforts to expand market share.
By supercharging your ETL with self-service, you can empower your business users to create new customer data connections in minutes—securely and easily. Users can easily point and click through easy screens and utilize machine learning and security protocols to onboard and manage multi-dimensional, complex data and stream it in real-time to execute modern-day business transactions. This frees IT teams from tedious and thankless custom coding and EDI mapping and instead focus on more strategic tasks.
By transforming the way ETL is done, you’ll be able to delight customers and create faster revenue.
A modern data integration solution can reimagine ETL capabilities and enable businesses to develop a resilient and well-instrumented ETL process in order to improve decision-making and value generation. While it puts the onus on your business users to get things done quickly, it keeps IT in control of your data governance.
Ergo, companies can embrace a modern solution to transform the way they extract, transact, and load customer data to help them make better decisions, speed operations, create stronger customer relationships, and grab a bigger market share.