Why Are We Still Dealing with Data Integration Challenges?

Over the last few years, we've observed the positive impact of big data integration & analytics in the healthcare sector and e-commerce. If we take Amazon, for example, we have all engaged with smart algorithms that enable highly personalized recommendations.

Nevertheless, the 2018 Data Connectivity Annual Report evidences serious problems faced by companies trying to derive real value from enterprise data. Regardless of which industry you’re working in, some of these problems will be all too familiar.   

So why are we still faced with data integration issues?

To get accurate and timely business intelligence from oceans of data, companies need to have a comprehensive and unified view of all the data. To achieve this, businesses will be charged with collating and cleaning data from a variety of different sources (and that’s where the headache begins). 

At the same time, you’ll also have to effectively manage the latest Application Programming Interface (API) trends, new data types, and multi-sourced environments. We can even say that data integration is more challenging now that companies are demanding more sophisticated analytics. 

Technologies Are New, but the Problems Remain the Same

If you dive deep into Progress’ Data Connectivity Annual Report, you’ll find the same problems we have encountered time and again. For example, as much as 47% of those surveyed stated they were trying to effectively manage the congestion of APIs. 

Another 32% were dealing with velocity (from batch to streaming and everything in-between). 31% were overwhelmed and even feared data variety, while 36% worried about data ambiguities, and inconsistencies.

Does any of this sound familiar? It should. 

If we roll back the clock to email campaigns in the late 1990s, the wide variety of data formats and sources was a huge problem that threatened to annihilate direct marketing campaigns. This issue needed a new approach to managing it, and the same ethos applies today.

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Rapid cloud adoption across industries is also driving the need for real-time hybrid connectivity. However, as much as 44% are still challenged with integrating cloud data with on-premises data.

Many companies thought that they could seamlessly access advanced automated data and analytics technologies by simply moving to the cloud. However, the reality on the ground is quite far from that. 

According to Buno Pati, CEO at Infoworks.io and Partner at Centerview Capital, “there’s not a cloud vendor today that can give you a highly automated, integrated, and abstracted system on which you can manage the entirety of your data and analytics activity.”

As corporations now focus on a hybrid approach with both on-premise and external cloud solutions, it’s only going to get more complicated. At the same time, you’ll also be expected to deploy and manage one or more of the leading data visualization tools like Qlik (16%), Power BI (18%), and Tableau (22%).

They all offer a variety of solutions that you have to efficiently integrate and develop with the help of highly skilled data and engineering professionals. It’s also vital to keep in mind that this can quickly become a mess when you add Artificial Intelligence and Machine Learning into the mix.

Learn from the Past and Avoid Repeating the Same Mistakes

The massive amount of data that’s being generated in real-time isn’t going to diminish or slow down. Instead, it’s only going to grow exponentially. So it’ll be critical for enterprises to adapt their infrastructure to accommodate (almost) infinite oceans of data.

It all starts with acknowledging the fact that data integration is a major issue. As the Internet of Things become more pronounced, you’ll have even more APIs, more apps, and more form factors generating more semi-structured and unstructured data (than ever before). 

It’s also important to consider the fact that some solutions to these issues can create new problems. So don’t focus too much on area-specific silos to solve problems because they can potentially add to your integration complexity.

One way to overcome this hurdle is to completely reimagine your approach to managing enterprise data. For example, you can build a centralized data ecosystem where all the data flows into the same platform. This will enable seamless access to data being generated from several different touchpoints. 

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As long as companies are still operating with legacy systems where data is confined to different spaces, you’re going to continue to hit a brick wall. So what do businesses have to do to end the data integration nightmare?

First, you have to change your present data management philosophy. Next, you have to leverage the latest tools with the help of top tech talent. The latter, however, will present a whole new challenge (and you can read all about it HERE).

Are you failing to meet your business goals because of data integration challenges? We can help, reach out to one of our in-house experts! 

Learn more about our Integration Solutions for businesses here.