The process of integrating data, which we went over in a previous blog post, is time consuming because of the multiple handoffs needed between cross-functional analytics teams and data integrations teams to compensate for the data subject matter experts (SMEs) being separate from the data integration process.
The solution then, is to bring data integration closer to data SMEs, saving the time spent waiting for data on the analytics side of the house and freeing up precious technical resources within data teams and IT departments to focus on complex data challenges.
At the same time, self service without governance can mean disaster for an enterprise. Allowing multiple analytics teams to have what amounts to “edit access” to many data systems, some of which contain sensitive data, could mean a lack of visibility or a lack of a coherent and holistic data strategy.
Of course, it’s crucial that that does not happen.
Which is why self-service is only half of a viable solution. For a self-service vision, a vision of speed and agility, to work there needs to be robust data governance and management capabilities in place.
The tools that are strong in self-service, lack in governance capabilities rendering them useful, perhaps, for smaller businesses, but completely inept for an enterprise.
On the other hand, the tools that are strong in governance are entirely out of reach for non-technical or even semi-technical users.
That’s why it’s time to switch over to a data integration software that makes it super simple to get usable data into a centralized analytics environment regardless of technical know-how, essentially making data integration part of a cross-functional team’s self service toolkit, without compromising on data governance and management.
In other words, the enterprise needs a solution that provides teams with the ability to extract data from any source within their enterprise (or from external data sources if needed), by simply clicking to select what data is needed.
Users should also be able to manipulate data formats and schemas on the fly to ensure that when the data arrives in their analytics environment it arrives in the exact specifications it was needed in. These transformations should also be able to be applied visually, without needing any code, to ensure true self-service is provided.
Finally, loading the data into an analytics environment, or having the data sync on set intervals of time must be no more an involved process than clicking a button is.
This simple workflow is only half of what an enterprise grade data integration software needs to bring to the table. The other half (you guessed it — the governance half) happens behind the scenes, in the form of forced sensitive data obfuscation, activity logs, role based action controls, administrative dashboards and so on. Data governance is serious business, and data integration softwares needs to take it that way in order to be suitable for the enterprise.
Data integration is just a bridge from raw data to answers, and it’s time to make that bridge shorter than ever because when teams within your enterprise are armed with the data they need, you’ll see unbelievable results.
Learn more and reduce the time and energy spent on data integration so that your cross-functional teams can carry data-driven projects end to end and do more, faster.