In order to create a competitive advantage via business intelligence, you have to enable every team to use data from sources across your digital landscape. The right strategy and tools can dramatically improve your entire organization’s ability to act on current insights, optimize business processes, and improve business outcomes.
To achieve this kind of data-driven business, you need enterprise data integration.
What is enterprise data integration?
Enterprise data integration is the process of unifying disparate systems of data and information to create a single source of truth for smarter business decisions.
As your business gets more and more digitized, it rapidly collects data across disparate sources and systems, like customer data in CRMs, supply chain data in ERPs, and personnel data in legacy mainframes and HR systems, just to name a few. Additionally, these systems serve specific purposes, but if disconnected, they create silos that hold back business growth.
Enterprise data integration aims to solve this challenge by unifying data and making it available for business use.
Enterprise data integration methods.
Below are some methods organizations use to implement enterprise data integration.
- Data warehouse. This approach to enterprise data integration replicates structured data from its source by routinely extracting data out of each system, transforming it into a defined structure, and loading it all into a new system for reporting and data analysis.
- Data lake. A data lake stores unstructured, semi-structured, and structured data from its source by lifting raw source data out of each system and loading it into one new system. Data must then be transformed to support data science, machine learning, and analytics.
- Data mesh. This architecture and management approach relies on connecting domains of data through web services that are curated by subject matter experts. The idea is that by coding API integrations between these different applications, entire systems could be integrated to create an enterprise data mesh.
- Data fabric. Data fabric technology is a toolset that unifies key systems using a virtualized data layer to connect data from wherever it currently resides. Users interact with the unified view, but the data stays in its source, whether an ERP, CRM system, or an enterprise data warehouse. (It’s worth noting: These applications and systems can reside in both on-premises systems and public clouds.) One of the key benefits of data fabric is the speed at which it lets you build and modify data integrations over time due to the low-code architecture of data fabric technologies.
Want to learn more about how to solve your data silo problems and speed up innovation? Get the guide: The Data Fabric Advantage.
Compare these approaches in-depth in this article on data fabric vs. data mesh vs. data lake.
How do you know which of these approaches will work best for your enterprise? Answer this question by developing an enterprise data integration strategy, which we’ll cover below.
5 pieces of an enterprise data integration strategy.
As we know all too well, data integration is more than just plugging in a new technology to your stack. Let's dive into the key pillars of a successful data integration strategy for enterprises.
1. Strategy and planning.
This is the most essential, yet most overlooked, pillar of success in a data integration initiative. Before you take off, you need deliberate goals and key objectives for your team’s work. Ask yourself questions like:
- What are the underlying business objectives behind this work? (Unified data for faster reporting? Creating a more data-driven culture?)
- What systems are we trying to integrate and why?
- Which business units and/or teams are going to be impacted by these changes?
Once you have a clearer understanding of the business demands behind the initiative and the desired impact, you can scope and plan the work. This includes communicating the plan to stakeholders to provide visibility into timelines and expectations.
2. Data governance.
Given today’s abundance of digital data, enterprises should always have data security top of mind. When integrating data across systems or into a new one, it’s important to know exactly what that means for data access. Consider the CRUD acronym: create, read, update, delete.
You should confidently know which business units and specific users have access to create, read, update, and delete data.
3. Technology.
This is the pillar that usually takes up the most oxygen in the room. Your team may feel overwhelmed by the number of technologies, including data integration platforms and analytics platforms, that promise to assist you in your journey toward seamless data integrations.
You can combine a host of tools to fit your needs. However, the key thing to remember is that each technology should serve a specific purpose. You should be able to map each piece of technology to a specific use case in your data flow.
4. People and processes.
This is where the rubber meets the road with regard to the data integration process. You can do all the technical digital transformation work you want, but if the actual process stays the same because people don’t buy into the changes—or you fall short when it comes to communication or training—your efforts will have been for nothing.
Your integration efforts should make interacting with data more seamless for people—whether that’s more reports, better data exploration capabilities, or just a better ability to see information in a unified view. You’re changing how people can and should interact with the data.
Leaders need to communicate and train people on these changes—repeatedly. Change management takes time, hard work, and empathy. Your data integration success depends on it.
5. Continuous improvement.
Your enterprise data integration strategy won’t be built in a day. Continuous improvement is the goal. Prioritize iteration in your approach to enterprise data integration and understand that the job is never “done.” There will be changing business and regulatory requirements as long as the digital world continues to evolve.
Tip: Consider low-code architecture. Low-code data architectures allow developers to build and maintain enterprise data integrations quickly with unified and intuitive data modeling tools that limit the amount of manual coding required to connect systems. This not only allows data teams to build new integrations between systems quicker but also reduces the technical debt accrued over time as requirements from business units change.
Benefits of enterprise data integration.
A company that can successfully unify data with an enterprise data integration strategy creates a data-driven culture that is unconstrained by data silos, complex integrations, or IT bottlenecks.
Connecting data from disparate sources across hybrid cloud environments and data storehouses also improves business intelligence in a wide variety of use cases, such as creating a 360-degree view of the customer or centralizing case management information.
In addition, enterprise data integration also serves a broader reporting purpose—to help the entire organization be more data-driven. This is enabled via:
- Cleaner data
- Better, real-time dashboards and reports
- Organizational agility to handle ad hoc data requests
- Greater insight discovery and analysis (your “ah-ha” moments)
Benefits of enterprise data integration.
Your success over the next few years will be shaped by how well you can use artificial intelligence—but to use AI, you have to have a strong enterprise data strategy. The success of your enterprise will depend on your ability to harness the power of your data and learn from it to make informed decisions and drive better business outcomes.
With a proper enterprise data integration strategy and modern tools like data fabric, you can connect siloed data and put it to work in ways that will keep your organization competitive for years to come.
Learn how a data fabric can transform your organization's data management strategies: Watch the on-demand webinar.
