What does a successful data integration strategy look like?
How should you evaluate data integration tools?
How do you turn data into value with a scalable data architecture?
This guide will outline the steps to build a data integration strategy, the stakeholders that need to be involved, and the key tenants of business value for measuring success.
A data integration strategy is a documented plan for how enterprise data sets will be combined and analyzed across the company.
It outlines the different sources of data (formats, data systems, amounts of data), and how the information will be moved and analyzed.
The data integration strategy provides a clear path to breaking down data silos that might exist within your company.
It's easy to get distracted by technical concepts like data integration, but we need to remain focused on the actual use cases, the business needs, and the creation of value from data.
For any data-driven organization, a data integration strategy is one of the most effective ways to align your data team with creating and capturing value from data.
There are three ways to create value from data:
You create value by centralizing data, powering dashboards, and enabling executives to improve strategic decision-making.
You create value by automating manual tasks and business processes. Systems and processes are used to ensure the right data makes it to the right place at the right time.
You create value by turning data into valuable products that customers can purchase. These could be insights, automated workflows, or raw data feeds for monetization.
Once we've identified the potential paths forward, a strategy document can outline where the company will spend resources in the near term.
The clearest path for most business intelligence teams is to start with data analytics - i.e. helping to inform business decisions with dashboards, data visualization, and automated reporting.
It's now time to assemble the right team to define the strategy.
Just like any other strategy, vision, or mission document, the deliverable is not what matters.
The goal of a data integration strategy is to make sure that all stakeholders have a shared understanding of how and why data will be integrated.
Before we outline the specific content to include in your plan, let's talk about the stakeholders to include in the process.
Specifically the experts within your business that act as the admins, owners, or stewards for the different systems that collect, store, and process data. These include your data warehouse manager, administrators for our on-premise databases, and owners of the SaaS applications you use to run your business - tools like Stripe, Jira, or HubSpot.
You never integrate data for the sake of integrating data. Data infrastructure always needs to tie back to a business objective.
What better way to identify and prioritize the most valuable success metrics than to include the end users directly in the planning process?
Before getting started, it can be helpful to identify high-value ways to impact the business. Things like 'reduce manual intervention in the customer experience, or 'increase usage by personalizing communication with customer data from our CRM system'.
In addition to the data producers and data consumers (i.e. the system owners and business end users), it's important to be able to look across all initiatives to understand the highest value use cases for data integration.
This is where your executive leadership can provide invaluable insights. Ideally, you can get a few minutes of the CEO's time to help prioritize the best strategy for data integration. If the CEO isn't available, make sure to get insights from other key executives to understand the most valuable use cases for data.
Once you compiled the stakeholders that need to be involved, now you can start organizing your data integration plan.
When documenting your data integration strategy, it's important to include answers to key questions. Here are the main considerations:
The first question to answer in your data integration strategy is, which type of data integration will your company leverage? Centralized or decentralized?
When you take a centralized approach to data integration, you load data from disparate systems into a single processing environment (a data warehouse like Snowflake, data lake, or streaming data platform) and you power all analytics and operations from that centralized environment. In these scenarios, most integrations that exist will be into or out of your centralized environment.
When you take a decentralized approach to data integration, it's more common to use an integration platform as a service (iPaaS tool) or point-to-point data pipelines to connect all of your systems directly. In these scenarios, it's important to focus on clean, reusable interfaces to connect data across your systems.
After you outline whether your company will take a centralized or a decentralized approach to data integration, it's important to outline which data integration requirements are one-time and which requirements are ongoing.
If you are moving to the cloud or undergoing a data migration from one system to another, these are one-time integration requirements.
If you are powering ongoing pipelines for analytics, automation, or product development, those workflows have fundamentally different requirements.
Extensibility is critical to any long-term data strategy.
You can't just evaluate integrations through the lens of the tools you have in place today, but rather, you need to think about how your integration strategy and architecture will scale with new technologies such as data sources, destinations, and processing environments.
You should outline a clear path to incorporate new systems, but also migration plans for when one tool is replaced by another.
As you outline how data will move between systems, processes, and people, you cannot overlook the importance of data governance.
Review your company's policies, procedures, and controls, and make sure that you're incorporating data security best practices, data privacy regulation, and ethical data use into your vision.
In addition to governance through the lens of protecting and using data, you should also ensure data is accessible and reliable. This is where you should consider best practices for data quality, observability, and monitoring.
Most use cases for data are not urgent. If data moves once a day, it's typically not the end of the world.
That being said, there are mission-critical workflows where real-time data processing is the only way to create value. Time-sensitive customer data automation is a great example.
Don't pick technologies for the sake of it, but if your use cases require streaming data, it's important to clearly outline the latency requirements upfront.
Different technologies, tools, and processes work for different volumes of data.
If your company is processing small amounts of data in near real-time, you might be able to use webhooks or event routing. On the other hand, if you're handling big data sets, you will likely need a different technical solution.
As odd as it might sound, for extremely large data sets batch processing and file transfers could be a cost-effective means of moving data and handling data storage. For small data sets that don't need to be updated automatically, it can even make sense to use a spreadsheet if that's all you need!
As your data initiatives mature and your data assets become more valuable, there becomes a time at which advanced analytics use cases can create tremendous value.
For most companies, it does not make sense to hire data scientists, to evaluate artificial intelligence (AI) or machine learning (ML).
That being said, if your company can drive better strategic decision-making, automate manual tasks, or build valuable external products with these capabilities, you should outline the value and the costs of doing so.
Once you've outlined your data integration objectives, organized the necessary stakeholders to define the plan, and created a coherent strategy, you now need to execute against the plan.
If a data warehouse like Snowflake, Amazon Redshift or Google BigQuery is part of your plan, you should check out Portable for ETL. We have over 300 no-code ETL connectors to help sync data from your business applications into a data warehouse or database quickly.
If you're taking a point-to-point approach to data integration, don't hesitate to reach out - we are happy to recommend other data integration tools that could be a good fit for different data sources and destinations.
Need help with your data integration strategy? Try Portable.