Data is the world’s most valuable resource, but its helpfulness depends on how you use it. Despite having quality information, many analysis projects fall short of expectations because of poor integration. Embracing a more modern data integration approach is a crucial step in making the most of your information.
Integration is easy to overlook but is a critical part of the data analysis process. Once you understand how this phase impacts your analytics projects, you can optimize it to unlock several competitive advantages and stand out from other companies.
What Is Modern Data Integration?
Data integration is the process of combining information from across your organization to get a complete picture. It combines varying formats and types from multiple sources to create a single, unified source of truth for more accurate analysis. Modern data integration aims to improve this process by addressing some common inefficiencies.
Many businesses invest heavily in digital transformation, but only 30% meet their goals in this journey. Why is failure so common? A lot of it has to do with an inefficient or limited integration process. Many integrations still rely on manual data entry, on-premise storage, data silos and batch processing, but these processes are no longer sufficient for today’s volumes, variety and velocity.
Modern integration techniques and designs include data fabrics, cloud storage, continuous processing, automation and more user-friendly controls. Here are five reasons these changes give you a competitive edge and how to implement them.
1. It’s Far More Efficient
Modern data integration’s efficiency is one of its biggest advantages. Automating integration processes and removing silos makes it easier to manage new information as it arises, leading to more relevant insights and effective data-driven decision-making.
Data fabrics offer real-time integration through artificial intelligence (AI), using these tools to automate discovery and virtualization. You can act on it far faster when you don’t have to manually pull information and file it into the right database. The analytics timeline can go from months to within a single day.
A faster analytics process means you can respond to changes in less time. That’s becoming increasingly important as data generation rates skyrocket. Consequently, the sooner you modernize your integration, the better you’ll stay competitive in a quickly advancing market.
2. It Involves More Team Members
Modern data integration also makes data-centric processes easier for nontechnical users. Traditional approaches often require extensive knowledge or experience in analytics and related technologies, but these skills and expertise aren’t necessarily common. That inaccessibility can stop you from getting all you can from your data.
Newer integration frameworks streamline and automate processing and analysis workflows so users don’t have to do most of the technical work. Removing that barrier lets you involve more stakeholders with less technology experience in your data decision-making.
Involving more team members is important because your IT teams aren’t always the ones who will be affected by the impact of your data-driven decisions. Including more people drives relevant, effective changes. As a result, your data initiatives will meet real-world business goals.
3. It’s More Accurate
Making data integration more accessible and streamlined boosts accuracy. Older methods involve a lot of manual data entry, silos and moving information from place to place. As a result, incomplete pictures, errors and losses in transit are common, but modern integration provides a solution.
Because modern integration technologies like AI automate data discovery and entry, they minimize the risks of human error. Similarly, keeping everything in one place through the cloud reduces transit and related data loss risks and makes it easier to see your information’s complete picture. As a result, any analysis you perform will be more reliable.
Human error is also one of the most common cybersecurity risks, so this automation makes your analytics processes safer. That security boost and avoiding inaccurate insights from unreliable data will result in considerable cost savings.
4. It’s Scalable
Your integration process must be scalable to make the most of your data. Global data volumes reached 64.2 zettabytes in 2020 and will likely pass 180 by 2025. If you can’t scale up to match these accelerating volumes, you won’t be able to capitalize on all your available information fully.
Because modern integration techniques process data in real time and automate repetitive tasks like data entry, you can add new sources or change workflows more easily. Moving away from on-premise storage and silos in favor of the cloud makes expansion faster and more cost-effective.
This adaptability lets you take advantage of rising data rates as they accelerate. As a result, you can grow faster while minimizing your operating costs.
5. It Provides Context
Modern data integration also covers a far wider range of formats and sources. Data fabrics and similar architectures can see, record and convert more file types, including surrounding metadata. Having access to more information leads to more reliable analytics.
Removing silos and workflow inefficiencies helps by letting you view everything as a cohesive whole. When you see the big picture in your data instead of isolated facts and figures, you’ll better understand emerging trends. Similarly, you can see how various changes impact your business more clearly.
This context can inform better strategic decision-making. Without it, even accurate data can be misleading, which can cause you to spend a lot of money on adjustments that may not yield significant improvements.
Conclusion
Staying competitive as a modern business means capitalizing on your data. As volumes and varieties grow, you’ll need more advanced integration technologies and strategies to stay ahead of the game.
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