The quality of your data integration strategy can make or break your organization’s investment in digital modernization.
Data integration is at the heart of good data access and use. Good quality, timely data that’s easy to access enables better workflows and business decisions.
"Business isn’t about luck; it’s about making smart bets. Smart bets can be made on a hunch, of course, but data-driven businesses make the most informed guesses possible before putting their money where their mouth is."
—Efe Ejofodomi, "What’s the Value of Becoming a Data-Driven Business?", Forbes
The right data integration strategy helps you leverage the best value from your data.
It can empower you with useful metrics to outperform the competition. It can channel real-time, data-dependent services to your customers and keep them coming back.
Using metrics and speedy data connections can also improve workflow efficiencies, boost your employee experience, and help you adapt to shifting conditions.
So, how you connect your data sets is a critical decision. It demands a good strategy.
1. What is a data integration strategy?
A data integration strategy identifies what you need to do to achieve your data integration goals.
Based on the strategy, you can form a detailed plan to achieve your strategic outcomes. The plan will get into the details: what’s to be done, who does it, and when. It also identifies who is responsible and accountable for managing it.
Your data integration strategy should align with your organization’s business goals.
A good data integration strategy will
- consider your (goal-related) data use cases and pain points,
- examine the types of data your people use,
- consider your budget,
- guide you to a wise, practical, and consistent integration approach, and
- enable a solution flexible enough to adapt to changing future needs.
Ideally, your integration approach should help achieve business goals, enable easy data access, and connect the data sources the business needs to be efficient, innovative, adaptable, and profitable.
2. Define your vision, goals, and business value
Defining your data integration’s guiding vision and purpose will illuminate your choice of priorities of what to connect and how best to connect them. So, it helps shape the wisest integration approach for you.
What business value do you expect to create through integration? Research and articulate the value.
You might start by asking yourself: Why do you need to integrate data differently now? Which data? For what new purpose or business goal?
For instance, do you want to:
- Boost process efficiencies with speedier data access?
- Improve regulatory compliance because of new state or federal rules?
- Lower costs to beat your competition?
- Increase revenues through savings in data-enabled automation?
- Link up cloistered, lonely data archives whose resources are being ignored?
- Connect geographically scattered service points and empower them with better tools and platforms for a better, more consistent customer experience? (Think omnichannel.)
- Mine datasets for more comprehensive reporting?
- Enable visual analytics tools, such as digital dashboards, to drive more strategic decision-making?
- Achieve a combination of these goals?
You can identify and implement specific metrics to increase buy-in for the integration strategy.
3. What do you need to integrate? Prioritize.
Identify the applications, data sources, systems and users you want to connect to—and be strategic about this selection. Prioritize them.
For example, do you need to connect (remove question mark in online version)
- Data from one department with another?
- Selected data from your HRIS system with the entire company?
- A specific application with another?
- One cloud service to other cloud services or apps?
- As part of a hybrid integration plan, connect an on-premises system with a private and public cloud service?
- Migrate data from outdated legacy systems to newer, better systems?
You can start by doing a data audit.
Then, determine who needs access to which data to determine specific user access.
4. Plan for interoperability
Whether data comes from distant cloud-based platforms, data warehouses, data lakes, smart connected things, mobile apps, or on-premises data centers, a good strategy will enable them all to be accessed in a common system.
An integration is a connection between two or more products or systems, enabling communication. The approach to integration varies.
Some approaches focus on application-based integration with good interoperability. Interoperability is the design of your different key apps and systems to work together.
Other approaches use middleware to build communication between legacy systems and updated ones.
5. Ensure scalability.
The Gartner glossary defines scalability as “the measure of a system’s ability to increase or decrease in performance and cost in response to changes in application and system processing demands.”
In short, scalability measures how easy it is to grow or shrink a piece of software. This could refer to a software’s ability to handle much larger (or much smaller) workloads, whether this comes from changing numbers of users, or from expanding and contracting data sets.
When software is designed too rigidly, scaling it can be costly.
A data integration strategy should consider how scalable its software solutions will be to pivot quickly to changing circumstances.
For instance, there may be limits to the numbers of users you can add to a system, data limits; and license and cost issues related to scaling.
6. Adaptable framework for new tech, on-demand access
While remote work policies always existed, they became much more common during the Covid-19 pandemic.
Supporting such remote access makes demands on your computing infrastructure and your IT security. Data integration solutions these days must take this into account.
For instance, do you need a system that makes use of distributed or edge computing services? Do you need on-demand, anytime, and anywhere data service for your employees or your customers? Might you need this at some future point?
Your data integration strategy needs to consider the relevance of these issues for your business.
Adaptability also includes the capacity of your integrations to adopt new technologies, work with new data sources and formats, and work with new APIs coming on stream—without breaking the bank.
7. Where is your infrastructure:
On-premises? Cloud? Edge? Hybrid?
A primary consideration in integration is the nature of your data sources. A firm may have one main data storage and processing source or combine several sources, as it strives to modernize.
Depending on the sources, linking them into an integrated view or data pipeline will entail other integration solutions.
Traditionally, organizations kept important and sensitive data in secure on-premises data centers behind firewalls.
While many on-premises systems remain critical, cloud-native and hybrid integration approaches are becoming extremely popular. Analyst house Gartner, Inc. notes the rising trend of hybrid and multicloud data management and integrations in its 2021 Magic Quadrant for Data Integration Tools report.
Last words
A good data integration strategy will consider many other issues not explored here, such as data security, regulatory compliance, and the availability of resources and skilled IT specialists to implement it.
From initial development and deployment of new data systems to the need for a change management plan, end-user training, and ongoing feedback, successful data integrations involve many moving parts.
That’s why a strategy is helpful as a focus for organizing those parts.
A successful data integration strategy will
- align with your business goals,
- save time and resources when building integrations,
- identify, prioritize, and assess the feasibility of use cases,
- minimize business disruptions,
- lower risk by centralizing data governance and data management,
- enable a faster response to threats, and
- enable better agility in times of change.
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About Pixentia
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