Automated integration takes much of the burden off of the technical staff by handling the numerous manual tasks required to make siloed data available to applications.
Modern applications require seamless access to data and the tight integration of multiple software elements that prep that data and enable its analysis. The problem is that companies are facing great challenges when trying to move data out of silos and into places where it can be analyzed and used for the benefit of the company, including driving digital transformation.
One way to make data more available and accessible is to move it to the cloud. However, in many organizations, that is not happening due to inefficient data management. In fact, many organizations are failing to move data to the cloud and often are keeping it in data silos until manually sorted and uploaded.
Even though a growing majority of organizations have some form of cloud data management, there still is a lack of investment, or knowledge, on how to get the most out of this data.
Data collection has also skyrocketed in the past decade, with a majority of organizations collecting far more data on users, sales, and other data points. In many cases, data is being collected at a rate much higher than it can be analyzed.
A study published by Wandisco found that 80 percent of respondents said data goes unused at their company, with 33 percent on average going to waste. Seventy-five percent said that more than half of all their data was siloed.
Almost all respondents said they had some data siloed, even though 69 percent said that by siloing it, the data loses most of its value. The main reasons for keeping the data in silos were that it takes too long to move, moving it risks disruption, and it is too expensive to move.
To understand and act on your data, it has to be in the cloud. All the big companies are finding ways to get at least some of their data there, but it’s not happening at scale. It’s certain applications here and there, but not every data point generated across the enterprise, according to the study’s findings.
These are all issues that come from a lack of understanding or talent to enable fast movement and analysis of data. The study found that 43 percent of organizations lack technical skills from employees to get the most out of the data collected.
Half of the organizations surveyed said data was the main driver of business decisions, with another 40 percent saying that it will become the main driver in the next two years.
But with organizations struggling to properly manage data already, there is a worry that without proper data management and analysis, organizations may be in the dark when it comes to making proper decisions on improving customer experience or operational efficiency.
The need for automated integration
Business processes depend on the movement of data between applications which is triggered by events, actions, customers, or clients. Integration is about getting data moved from where it is created to where it needs to be consumed. However, integration efforts often encounter obstacles.
With large amounts of siloed data, there are many manual tasks throughout the integration lifecycle, including creation, deployment, operation, and maintenance. This includes tasks like mapping fields in integration flows, creating the integration flows themselves, or writing API tests that exercise the full scope of the integration and related back-ends. These manual tasks have always required expert integration skills. This slows down the speed of any digital transformation projects due to a limited number of integration experts.
Automated integration takes much of the burden off of the technical staff to carry out these processes. The goal of automated integration is to enable applications and systems that were built separately to work together without too much manual effort, resulting in new capabilities and efficiencies that cut costs, uncover insights, and much more. Achieving these results requires overcoming problems in multiple areas. All the problems can benefit from automation, like the ones listed below:
Automated integration that makes use of technologies including artificial intelligence (AI) and no-code/low-code development methods can help address these challenges.
For example, AI in the form of natural language processing (NLP) might be used at the start of a project that requires integration. The business team could then simply describe the integration it needs. An AI-powered automation solution could then use those words to recommend various integration templates the team might use in its project. And finally, an AI-powered automation solution would create the integration workflows once the business team selects a template.
Such an approach will greatly speed integration efforts. This, in turn, will help organizations realize value from data that had been previously locked into silos.
*David Curry contributed to this article.