In Cambridge in 1914, Arthur Stanley Eddington was charged with a simple task: protecting Isaac Newton’s model of classical physics from the theories of one Albert Einstein.
However, as Eddington parsed through the theory, he realises that the data pointed in the opposite direction to his initial brief.
He contacted Einstein, exchanged notes, and gathered his own data in a few years’ time to provide proof of the General Theory of Relativity.
Only, this was happening in 1914, at the beginning of the First World War, when the scientific communities in the German and non-German worlds had restricted the exchange of information with one another. The data-driven collaboration between the two men was an outlier, not the norm.
In that sense, today’s business ecosystem is not very different from the scientific community of the early 1900s.
Enterprise data is often stored, shared, and used in silos, rendering significant business value either completely invisible or unexplored.
The result? Despite having access to insights that can transform their operations, businesses struggle to realise their full potential.
There is a need to understand the fragmented nature of enterprise data and the need for data Integration
The average large enterprise today uses over 1,000 different applications to run its business. This number is only going to grow as organisations strive to be agile and personalised in the way they serve their customers.
However, this also leads to an increase in the number of applications that need to be integrated with one another to ensure a cohesive user experience.
The problem is that most of these applications were not built to work with each other.
They were built to meet the specific needs of the departments that use them and, as a result, they often store data in different formats, use different coding languages, and have different security protocols.
This makes it challenging to share data between applications - especially when certain datasets, such as enterprise data hosted on in-premise SAP systems, have conventionally been extremely difficult to move to a cloud-based environment, leading to data silos and disparate data resources that work orthogonally.
Such data silos have several negative consequences for businesses. For one, they make it difficult to get a holistic view of the business. They also make it difficult to make more accurate decisions as different departments often have different interpretations of the same information.
AI-led data integration
To realise the full value of their data, businesses need to be able to extract and combine data from different sources. This requires a real-time data framework that can connect different applications and databases, no matter where they are hosted or what format they are in.
That said, traditional data integration processes are unable to tap into the immense potential that enterprise data warehouses and data lakes represent.
Practices, such as scheduling batch updates and performing manual design and transformations, are slow and coding-intensive as they often result in error-prone data pipelines, data integrity and trust issues, and delayed time-to-insight. To top it all, they are expensive and require significant engineering and data science resources.
This is where AI-powered data integration offerings such as the Qlik Data Integration platform step into the picture.
Such technologies help businesses to connect different data sources, no matter where they are hosted or what format they are in. It also helps businesses to clean and transform their data so it can be used for analytics.
Top platforms also come equipped with self-service capabilities that enable business users to easily combine data from different sources and create a single, unified view of specific functions, in real-time.
Different departments can easily share critical information with each other to explore and define mutual objectives, as well as to unlock and capitalise on hidden value.
The 360-degree view of the business also helps stakeholders across the board gain a deeper understanding of enterprise operations, enabling them to make more accurate decisions, drive better business outcomes, and improve operational efficiency.
Zift recently saw the benefits of moving to such an AI-led data integration framework. The company needed to deliver vital integrations with customer relationship management (CRM) and other customer data platforms to enable timely response to customer requests and replace the need for complex, code-based integration builds.
To this end, it implemented Qlik Application Automation to simplify and optimise integration builds and drive real-time data into software-as-a-service (SaaS) applications.
This significantly reduced the time-to-insight as, instead of waiting between 2 to 24 hours to have their CRM data reflected in the Zift platform, the company’s customers were able to utilise real-time data as current as within the last 30 seconds.
Predicting the future by peeking into the past
Data is the lifeblood of any organisation but, to make accurate decisions and drive better outcomes, businesses need to be able to integrate their data, no matter where it is stored or what format it is in.
Data integration platforms can help businesses transform their operations by unlocking the true value of their data through collaboration and information sharing.
It is what helped Einstein and Eddington change the approach to physics. It is what will help businesses be prepared for an increasingly digital-first future.