Today’s businesses are making attempts to become more data-centric or develop their data culture. In order to do that, they have to start leveraging actionable insights from their data and using them to make decisions and deliver the value promised to customers. Therefore, ETLs play a central role in data integration strategies. This technology enables companies to extract, transform, and load data from a variety of sources and consolidate it into a single, unified database, where it can be analyzed and used for various purposes. They are usually two primary goals in mind:
- Firstly, garnering actionable insights from the complex, bi-directional customer data streams to make informed decisions.
- Secondly, implementing data connections with customers to provide the value promised to them on time.
Implementing and managing an increasing number of ETL flows is one of the biggest hurdles that companies face during their life cycle. IT integrators have to spend weeks or months of calendar time extracting, transforming, and loading data into a data lake or data warehouse. In doing so, they need to develop and maintain custom scripts, which is complex and time-consuming. Now, every time there is a change in the schemas, the integrators have to update their scripts to accommodate them. This results in downtime and high operational overheads. Next, IT also needs to create source-to-target data mappings for the data transformation process. However, when the underlying source and target systems change, performing data mappings becomes even more difficult. The problem of missing information and data being inserted into the wrong fields makes it even worse. Under such constraints, it’s more likely that companies make incorrect decisions, leading to missed opportunities and lost revenue.
Now, while IT is busy executing ETL flows, they fail to focus on other more strategic tasks. Plus, oftentimes, companies have to hire additional IT experts to handle all the ETL flows, increasing business costs.
Self-service-powered technologies can help companies overcome these problems and ultimately grow. How?
Self-service ETL solutions enable companies to extract, transform, and load different data streams – smartly, quickly, and easily. It empowers non-technical business users to implement data connections while freeing IT to focus on more high-value tasks.
For better understanding, let’s delve into three ways companies can rely on self-service ETL solutions to drive value.
1. Empower Business Users: Self-service ETL solutions empower non-technical business users to implement new customer data connections in minutes instead of months. Users can onboard new business customers up to 80 percent faster. This means non-techie business users can implement new onboarding connections in minutes instead of months.
2. Free Up IT Headcount: While non-technical business users implement new data connections, IT becomes free to focus on more important, strategic tasks. IT no longer needs to implement long custom codes and perform extensive data mappings that require more time and effort. Instead, they can use the time saved to drive other business priorities.
3. Delight Customers and Deliver Value: When business workers can connect with customers much more quickly, they can, obviously, address and meet the demands and needs of those customers without delay. In other words, business workers can deliver the value promised to customers with speed, fueling delight and satisfaction in them. Such happy customers are more likely to buy more products or services from the company, thus increasing revenue and value generation.
In short, companies can leverage self-service ETL integration solutions to reimagine their extraction, transformation, and loading processes by empowering non-technical business users to implement data connections and ultimately drive value.