Businesses often have mission-critical questions about their operations. Maybe you’d like visibility into your supply chain. Maybe you’d like to answer some questions regarding costs and spending. Maybe what you’re looking for is that ever-elusive 360 degree view of your customers.
Regardless of what the exact strategic business questions you need answered today are, I’m sure your enterprise has taken concrete steps to get those answers. You’ve built business operations teams specializing in analytics and armed them with the latest BI, AI and ML analytics tools.
However, in order for your teams to use those tools to deliver the data-driven insights that could provide the answers to your questions, they need to have access to usable data (data that is both high in quality and standard in formatting).
The process that is responsible for delivering usable data into analytics storage tools accessible by business operations and analytics teams (data warehouses, lakes etc.) is called “data integration” and there are a plethora of data integration tools on the market.
The idea behind data integration is that having a tool that can clean, standardize and unify data can empower businesses to analyze data, manage master data sets, maintain a single source of truth and share data across the enterprise.
The problem is traditional approaches to data integration lead to bottlenecks, which in turn slow down analytics efforts and decrease the number of data-driven projects teams can complete.
Traditional data integration technologies are problematic, not because of their feature sets or capabilities, but primarily because of the workflows they support.
These tools, like Infomatica and others, are built to be used by ETL or data engineers, the former a group of professionals experienced and trained in using specific data integration tools and the latter a group of highly technical professionals.
They are complex, and not particularly user-friendly, meaning that analysts or data scientists that sit in business operations teams cannot actually operate these tools.
This poses a key challenge:
ETL and Data engineers are not usually housed in business operations teams. Far more often they work in centralized ingestion or IT teams. This creates a fundamental divide between data consumers (those that work with and analyze data) and the data integration process itself.
This is significant for several reasons:
In recent years a new class called “self-service” data integration tools have come to the market. These tools were built to uncomplicate data integration, that is to introduce user friendly and low to no code UIs.
Strategically this was meant to bring the data consumer and subject matter expert into the integration process, allowing them to easily select what data they need and to bring that data into their analytics environments in the needed format.
Unfortunately, many of these self-service data integration tools have a gap in visibility and control that would lead to serious governance issues within enterprises, especially those in regulated industries.
Another result of aggressively simplifying tools has been relatively paired down feature sets that compromise on data transformations available.
Datalogue entered the self-service data integration space knowing that as it stands today, self-service tooling cannot be adopted in large scale enterprises. After all, governance and flexibility when it comes to data integration are key when dealing with complex data landscapes.
That’s why we built our self-service data integration platform specifically with the enterprise in mind.
Our solution brings to bear:
That means our platform can be used by your analytics teams, by your central ingestion team, or both. Regardless, its simplicity makes the data integration process move faster.
What will your team accomplish when getting usable, timely data is never a bottleneck?