How Radical Simplification in Data Can Lead to Radical Innovation

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With ChatGPT in the hands of everyday users, AI technology is poised to realize its potential in revolutionizing the digital landscape. Combining the evolving consumer demands with this explosion of technological advancement results in the need and potential for more innovation than ever before.

As with most innovation waves, organizations will be tempted to haphazardly leverage AI just because it’s something new and popular. They may also try to use AI in the same way every other business is using it. With the AI hammer, every business problem may look like a nail.

However, if businesses want AI to deliver value, they’ll need a data-driven strategy that places the right AI tools in the proper business functions. This means data must be high quality, accessible and simple to understand. When you radically simplify your organization’s data, your AI tools can help create radical outcomes that drive business growth.

Looking Through a Data-Driven Lens

Radical data simplification can’t happen if companies don’t first value and understand their data. In order to be actionable, data needs to be aggregated, processed, and digestible. High quality data is a critical component to any meaningful output, especially as we consider AI. This is admittedly easier said than done when many companies have data coming from a broad spectrum of disparate and seemingly incompatible sources.

Businesses across the globe are associating good data management with the term DataOps. According to Gartner, DataOps is the “collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization.” Research from the State of DataOps survey, showed 49% of IT decision makers plan to extensively increase DataOps spending. This indicates a recognition from enterprises that good and accessible data will drive innovation. In addition, organizations that invest in their DataOps spend are more likely to succeed and compete with their peers. According to the survey, 84% of organizations saw an increase in the number of end users accessing data this past year.

The changing consumer landscape provides a great example of the need for good DataOps practices — practices that radically simplify data and push AI-led innovation.  In many cases, consumers are both directly and indirectly demanding faster and more relevant data. Data provides the necessary information for AI to generate customer-related content on business websites. Also, data on customer behavior can empower your business to personalize marketing efforts to each shopper’s needs. Internally, data can even highlight how much your tech stack is costing you and if it’s effectively helping you meet customer demands. Each of these examples show how a data-driven strategy is important for IT personnel, but across an entire business.

Implementing Radical Simplification to Streamline Innovation

So, how can a business leverage radical data simplification in order to strengthen their AI tools? The foundation of radical simplification begins with integration solutions that can connect, streamline and maintain this valuable data. This frees up your IT staff and your software engineers to spend more time innovating on the products that will drive business growth. Also, when you leverage a solution that helps preserve your data, it makes it easier — or radically simple — to access and digest that data.

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Ensure your integration solution is hooked up to important platforms where your data lives. This could include your customer relationship management systems (CRM), cloud storage applications, or daily productivity apps. This allows the intra-sharing of important data between your people and your systems. Armed with this insight, innovation can take place everywhere throughout the company from software developer, to sales, all the way up to the c-suite. These integrations also make it easier for AI tools to leverage your data and, in turn, automate processes that solve targeted and relevant business problems.

Gaining access from legacy systems is also a key part of turning radically simplified data into innovation. Oftentimes data may exist in places that decision makers have long forgotten about or don’t use anymore. This allows data to not only go to waste but translates to wasted infrastructure and lost knowledge. When the right integration solution can access these systems and data, it can provide historical context for future business decisions and even help promote return on investment (ROI) for those legacy systems.

The Current Need for Radical Simplification

Maintaining complex systems and wrangling data have been long standing bottlenecks towards innovation. Also, customers are constantly looking for the next big thing, forcing companies to accelerate innovation to keep pace with customer appetite. The goal shouldn’t be for companies to hop on the latest AI trends and force feed misguided and misaligned AI tools in their processes. Harnessing the ability to innovate at the right speed and scale should be the goal. The good news is that, when you radically simplify data — through proper data storage and data sharing with the right integration solutions — your organization will be set up to correctly use AI tools that power innovation.

About the author: Matt McLarty is the Chief Technology Officer for Boomi. He helps organizations around the world thrive in the digital age. He is an active member of the worldwide API community, has led global technical teams at Salesforce, IBM and CA Technologies, and started his career in financial technology. Matt is an internationally known expert on APIs, microservices, and integration. He has co-authored books for O’Reilly, co-hosts the API Experience podcast, and is co-author of the upcoming book Unbundling the Enterprise from IT Revolution. Matt lives with his wife and two sons in Vancouver, BC, Canada.

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