As data leaders prepare to execute their objectives for the new year — centralizing data is your greatest opportunity for successful AI initiatives and to impact revenue this year.

In order to deliver on AI projects, enterprises first need to overcome their data integration challenges by centralizing data from their SaaS applications, databases, ERP systems, and more — quickly and reliably.   

One of the main benefits of data centralization, and the democratization it enables, is reducing an enterprise’s data-to-decision time. For example, through views like a Customer 360, data leaders can provide their organization with a real-time, comprehensive view of their customers for predictive insights, real-time personalization, and more.

Storing data in the cloud, however, is only half the battle; the method in which data is moved is paramount. Let’s talk about how to move towards data centralization.

Data centralization: A deceptively complex engineering challenge

Data centralization is easier said than done if you’re still operating under a do-it-yourself mentality. The number of data sources is growing, the volume of data is growing, and there are only so many hours in the day.

Centralizing data comes with several challenges, including:

1. Accommodating a wide and growing range of sources
2. Ensuring that syncs run reliably and are resilient to upstream schema changes
3. Maintaining and upkeeping existing pipeline connections as endpoints are updated
4. Guaranteeing data integrity and offering visibility throughout the syncing process

The fundamental challenge associated with centralizing data is that moving it from a source to a destination is a deceptively complex engineering problem. It involves: designing new architecture, provisioning the right computing and storage resources, ensuring timely performance updates, building resistance failure, and more.

As a result, do-it-yourself (DIY) pipelines or legacy solutions make AI a complex undertaking that demands considerable investment in time, labor, and money.

For example, all of the time used on pipeline building and maintenance amounts to a misappropriation of critical resources, leaving data teams vulnerable to wasted spend and high attrition rates.

This combo of infrastructure complexity and data abundance makes automation a critical component for enterprises to enable free-flowing, scalable data movement. Without expediting and simplifying data movement, enterprises will struggle to centralize.

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Leveraging a data integration platform to centralize data

Successfully leveraging AI to gain deeper customer insights and drive meaningful business impact is a top priority for nearly every organization.

All of this, of course, can create added pressure for CDOs and their teams, as data teams are ultimately responsible for:

• Understanding the data requirements that business stakeholders define and aligning with them on said objectives
• Building, maintaining, and scaling the necessary infrastructure to support the sources, volume and complexity of data involved
• Establishing and enforcing data governance policies, including privacy regulations, to protect customer information

Fortunately, when data movement is executed efficiently, data access no longer acts as a source of frustration and stagnation — instead, it serves as a competitive advantage.

Moving data automatically provides real-time access for decision-makers and stakeholders to ensure data integrity, reducing the risk of downtime and flexibility.

For these reasons, an automated ELT (Extract-Load-Transform) model is preferred to a traditional ETL (Extract-Transform-Load) model, as the former provides nearly instant, self-service access to analytics-ready data.

Building a 360-degree view of your customer is just one example of the transformative potential of AI — and to make it impactful, it must be AI-driven. By integrating and transforming data at the end of the workflow, data teams can combine raw data from disparate data sources into data models that best meet their needs.

More than just delivering on data-enriched, organization-wide goals, the key to a high-performing Customer 360 model is achieving accelerated time to insights. A company’s marketing team, for example, may want to leverage AI for their Customer 360 to personalize outreach at scale, predict preferences, and adjust marketing strategies in real-time with live data.

That makes a data integration platform a cornerstone of the modern data stack. By easily and automatically connecting scattered data sources and delivering data where, when, and however you require — you’ll have a reliable stream of customer data to leverage for high-value AI-driven initiatives.

How to evaluate a data integration platform to centralize data

Data centralization fundamentally requires a technological solution in the form of a fully-managed, data integration platform. Not all platforms are the same though and understanding the critical functions and features to centralize data is integral.

Consider these key capabilities when investing in a modern data platform:

• Easy to use out of the box, with minimal need for configuration and engineering time to get started
• Utilizes an ELT architecture rather than the ETL method, which simplifies the data pipeline, enables secure data processing, and leverages the scalability of the destination base for data transforming
• Built with robust security features, especially if you’re an organization in a highly regulated industry where sensitive data must be obscured, encrypted, or excluded
• Reliable in real-time, including the ability to cope with upstream scheme changes, new data source configurations, and optimizations for pipeline and network performance
• Fully supportive and customizable, in terms of your organization’s current data sources and destinations, as well as those you’re likely to use in the future

By checking off all of the above boxes, an automated data movement platform frees data teams from building and maintaining pipelines, all while fueling key AI initiatives through the centralization of data access.

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