A data integration strategy takes a comprehensive look at the types of data you use in your organization, the integration use cases and pain points that you need to address, and the best type of solution for integrating data across platforms and applications.

To arrive at the best strategy, you will have to answer several questions concerning the way your enterprise uses data, the short and long-term goals of your data integration project, the resources that you have on hand, and the scope of your integration needs. With all those factors, it can be hard to know where to begin. But when creating a data integration strategy, there are ten factors you can't ignore. We recommend you start by reviewing these core tenets, as well as the solutions available on the market, and the benefits you gain when putting this strategy in place.

Table of Contents

  1. The 10 Core Tenets of a Data Integration Strategy
  2. How to Set Long-term Goals for a Data Integration Strategy Within an Organization
  3. Data Strategy and Integration Benefits
  4. Data Integration Tools and Software

The 10 Core Tenets of a Data Integration Strategy

You have 10 core tenets to keep in mind when you’re developing your data integration strategy. If you don’t consider these factors when you’re putting together a plan, you may encounter challenges in getting the most value out of your data integration projects.

1. Scalability

Seagate reports that 163 zettabytes of data will be produced by 2025, and your organization’s workloads may change significantly over a 5 and 10 year span. Your data integration strategy requires a scalable approach that’s prepared to handle the data loads of the future and the daily needs of today. One way to handle scaling is to connect it to real-time events and automate resource allocation based on your integration activities.

2. Anytime, Anywhere Access

The rapid adoption of remote work policies due to the 2020 pandemic has further driven the need to create highly accessible data integration resources. Ad hoc data requests, routine integration, and countless other use cases may occur with remote employees, those on business trips, field workers, and other stakeholders outside of the physical business location.

You need to consider how remote access will be supported, from the infrastructure to the security required. The user experience should be seamless from both sides of the connection. Your work-from-home staff may need upgrades to their home office equipment as part of this plan so you avoid accessibility pitfalls associated with insufficient Internet speeds, unsecured home networks, and obsolete hardware.

3. Interoperability with Enterprise Solutions

You won’t get far with a data integration strategy that doesn’t account for the enterprise solutions you have in place. Consider where your data is coming from and going to, the storage options that you currently use, new types of data storage, and the Application Programming Interfaces (API) that you can leverage. Your strategy should move you closer to creating a silo-free enterprise environment that supports massive data movement and transformation.

4. Adaptable Framework

How much agility do you have in your data integration? Do you have a future-proofed way that helps you drive the adoption of new technologies, work with new data sources, APIs, and formats, and adapt to an ever-changing enterprise environment?

A flexible data integration strategy uses a framework that can accommodate new technology without a massive expenditure of resources. You can quickly bring in new solutions ahead of the competition and reduce the development workload required to make it work with your data integration processes.

5. Where is Your Infrastructure

Many applications and solutions are moving to the cloud or developed with a cloud-native approach, but on-premises infrastructure also has its strengths in the enterprise. Accommodate the full range of deployment options to avoid limiting your options in the future. You can make decisions based on what’s best for a particular solution, rather than trying to force it into a cloud-only or on-premises-only box. Sending data between these solutions is also streamlined with this approach.

6. Data Types and User Access

What data types and sources are you working within your enterprise? Do you need to integrate all of these or just certain ones? Are there data types that you’re likely to work with in the future that are not currently in use in your organization? By defining the exact data intended for integration, you can avoid choosing solutions that have challenges working with certain formats or ones that can’t support your future requirements.

The easiest way to address this factor is to conduct a data audit. You may discover data types that were not accounted for and some that may need to be replaced. The next step of this process is to understand who needs access to the data. Most users only need access to a small portion of your enterprise’s data, and controlling that access is critical from a security standpoint.

7. Regulatory Compliance

Data regulations continue to evolve and falling out of compliance can be costly. Data management and privacy are top-of-mind subjects for many stakeholders, and adapting to sweeping changes after the fact can lead to significant development costs and business disruption.

Granular control over data integration and management helps your enterprise adapt to new regulations and requirements set in place. You may already cover future compliance through your data governance policies, but revisiting it allows you to address challenges unique to data integration.

8. Data Security

How do you protect data that is being pulled from multiple sources and transformed in many ways? Data breaches and losses are another costly concern for enterprises, but you can manage your risks with the right strategy.

The typical attack surfaces in your organization, the type of data that you need to protect, and the industry regulations you must follow all inform your method for data security during integration. A few vulnerabilities that are common in many industries include attack opportunities as data moves from one solution to the other, internal malicious actors, phishing, and social engineering. A proactive, multi-layered security approach can get ahead of existing and emerging threats. 

9. Available Resources and Talent

Implementing a data integration strategy requires access to sufficient resources and specialists. Think about the support you need to successfully deploy data integration across your organization and how to drive adoption of this capability.

You may need a combination of in-house and outsourced staff to handle key parts of data integration, such as the initial development and deployment, but that’s just one part of the equation. Removing data siloes from your organization also requires a strong change management plan so stakeholders understand what that means for them, and how they can benefit from it. End-user training and ongoing feedback from stakeholders will shape data integration rollouts and the solutions you use.

10. Business Value

Exactly how does your organization benefit from a large-scale data integration strategy? What results are you hoping to see after a solution is in place? Identify key metrics and how they affect the organization in a positive way to increase buy-in for your strategy. You can point out greater efficiency and productivity, better visibility into the organization’s data, and potential bottom-line impacts that come from data integration.

How to Set Long-term Goals for a Data Integration Strategy Within an Organization

A long-term view is an essential part of choosing the right data integration options for your organization. If you only focus on your current requirements and infrastructure, then you may need to completely change your integration processes and solutions in a few years’ time. When you go through the factors in the previous section, think about where your organization may be in the next year, five years, and ten years. Some types of data integration are better suited to supporting your organization’s long-term needs than others, and you can implement those now rather than needing to switch in the future.

Data Strategy and Integration Benefits

  • Gets stakeholders on the same data integration page: What are everyone’s goals with this project? What challenges do they want to solve through data integration? What is the scope of this process? When you create a comprehensive strategy, you answer all of these questions and leave nothing up to the imagination.
  • Better informs the tool and software selection process: Your data integration requirements drastically influence the software or platform that best suits these needs. If you don’t have a strategy in place, then you could have a solution mismatch that costs significant time and resources to fix. 
  • Addresses current and future challenges for data integration: You cover any problems that stand in the way of data integration in your organization and what you need to change to solve them.
  • Reduces wasted resources: Your strategy provides a blueprint for data integration and cuts down on resources that could be wasted due to inefficiencies in this process.
  • Opens up communication channels between impacted departments: An enterprise-wide data integration process should have input from stakeholders outside of the integration team. You can discover new use cases, solutions that need supported, and data formats that were not known outside of that department.
  • Creates a security-centric foundation for data integration: Keep your data safe when it moves between solutions through the data integration tool. You have to consider the security challenges that this process introduces, and how to mitigate the risks that exist.
  • Identifies potential data integration pain points: How can data integration improve the way people use data in your organization? Pain points come in many forms, such as needing to manually enter data from one solution to another. Talk with end-users throughout the organization to discover where they need the most support, the limitations of their current software, and the data they would like to access.
  • Empowers organizations to become data-driven: When you improve access to data throughout the organization, you also boost the visibility of this data. Ad hoc data requests and real-time updates can go a long way to influencing business decisions made. 
  • Removes data silos in the organization: You create an environment that promotes collaboration between departments, as it’s much easier to do so when they can access data across teams and business units.
  • Maximize the value of your data: You can use the data in many solutions and use cases, which allows you to get more out of this information. 
  • Improves data management: A comprehensive data integration strategy has many provisions in place for controlling the way that data moves throughout the organization.

Data Integration Tools and Software

A strong data integration strategy provides a clear picture of the tools and software that best support your business goals. Some organizations may need a relatively simple integration solution, while others need a robust enterprise-wide platform. Here are the major categories of tools available on the market.

  • Extract, Transform, Load (ETL): This software category has a straightforward name. Once it pulls data from an application, it can change it into a format that is better suited for the destination application. For example, Integrate.io offers automated data loads that you configure with a simple, visual data pipeline. In addition to a range of transformations, it can normalize this data, clean it to improve its quality and maintain compliance requirements. It’s a cloud-based platform that uses a code-free approach, so business units can handle many of their own integration needs. Developers can focus on more complex data integration use cases. Since it’s cloud-based, your organization does not need to invest in significant amounts of resources to support big data analytics. You can use Integrate.io to centralize your data into a data warehouse, move it between different types of databases, and pull more data into Salesforce.

  • Data migration: These tools are relatively simple, and focus on moving data from one solution to another. They may be built into a platform that you already use, and facilitate adopting new services or improving its capabilities through more data. Platforms that offer native-integration with third-party solutions are an example of data migration tools. Stand-alone third-party tools also exist for popular software and database technology.
  • Data governance platforms: A data governance platform goes beyond integration and moves into a complete management solution. If you want to implement robust control over the data flowing into, through, and out of your enterprise, then you may want to consider this category. However, it may have a significant overlap with platforms you already have in place or could go well-beyond the functionality you actually need for a data integration strategy. You also need the hardware, software, and personnel to support this resource-heavy option.
  • Data visualization: Analytics tools often streamline the process of accessing data in your organization for business intelligence. The automated processes take this information and visualize it through dashboards, reports, charts, and other formats. Typically, data scientists and business units use these solutions.
  • API: Many platforms offer APIs of varying usability that facilitate data integrations between sources. However, you need developer resources to leverage APIs to their fullest, and you may be better off using a data integration platform that already has access to the API built-in.

Your data integration strategy can take many forms, depending on your business requirements. Form a strong foundation with these 10 factors guiding your data integration choices. If an ETL solution best fits your needs, contact us for a demo and seven-day trial to see if Integrate.io can work for your organization.