When data is inaccurate, missing, or outdated, the organization’s business teams are forced to make less-than-ideal decisions on behalf of colleagues, clients, and prospects.
Addressing the foundation of the problem requires improving the end-to-end process of data collection from different source systems, storing it in a data warehouse, and then exchanging it with myriad downstream applications.
We’ll empower organizations to undertake this initiative by highlighting the common faults of specific data integration processes, along with the solution for each issue.
1. Delays in implementing data connections
Organizations rely on their IT teams to execute data-driven operations. Those teams need to implement custom codes and build extensive data mappings to create customer data connections, which can take weeks or even months of calendar time. During that time, business workers wait to connect with customers, and these delays cause organizations to find it difficult to identify the customers’ needs and meet them.
Solution: Self-service data integration enables non-technical business users to implement data connections much more quickly and effectively. Also, they can onboard customers 80 percent faster, which helps companies deliver the value promised to customers.
2. Rising security risks
It’s likely that organizations are integrating a large share of data that is regarded as confidential. This can include customer data (including billing information), financials, employee information, etc. Failing to safeguard this highly vulnerable data can keep organizations at risk and ruin their brand reputation.
Solution: A modern data integration solution with such features as an end-to-end encrypted environment allows only authenticated users to access and leverage data.
3. Resource constraints
Enlisting IT and engineers to implement data connections from scratch puts an immense burden on those resources. The time and effort required to make the endeavor a costly one.
What’s more, already scarce and burdened IT resources are busy creating extensive codes and data mappings, and they are unable to devote their time to more high-value business priorities. This inhibits growth and innovation.
Solution: By leveraging self-service data integration solutions, organizations can free up their IT resources and enable them to focus on more strategic business tasks. What’s more, features such as pre-built connectors help teams build data connections quickly and easily.
4. Compromised data quality
Poor data quality can result for many reasons, from duplicates to incorrect formats. Relying on IT teams to identify and address these problems might work well when they’re managing a small volume of data, but at a larger scale, the task leaves them frustrated and prone to slip up.
What’s more, these employees may not be properly suited to perform the highest quality of control. They may be unfamiliar with the data they’re handling, which leaves the organization poorly positioned to identify problems.
Solution: Similar to the previous solution, organizations need to rely on a low-code data integration platform that doesn’t involve much coding and scripting. The risks of errors go down and finally the data quality goes up.