Emanuel Younanzadeh is VP Marketing at The Modern Data Company.
Over the past decade, the data boom has created exciting strategic opportunities for adaptive companies and enabled the development of entirely new enterprises. This wave was the result of the enormous generation of real-time structured and unstructured data, primed to influence decision-making and power operational change. Big data brought a lot of big promises.
Here we are, a decade later—swamped in countless technologies, methodologies, applications, visualization techniques and data-aggregation capabilities—and many companies are still failing to achieve the promises that came with the emergence of big data.
According to Harvard Business Review, “Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all.”
The problem isn’t data. The problem is the clunky processes, outdated technology and rigid legacy infrastructure that have seen too much investment for organizations to simply shed. But the cost isn’t just slower progress. The failure to fully leverage data is a recurring tax to every arm of the organization. Mired in supportive tasks and complex architecture, the true cost is innovation.
This is why a company’s data architecture is more important than the data itself. To be fully effective, data architecture should support the needs of every facet of an organization—from R&D to marketing to culture.
To fully understand how this is a missed opportunity and how it can create countless bottlenecks to an organization’s performance, it’s helpful to understand what a modern flexible data architecture looks like and how it can enable data to drive informed decisions.
Defining Data Architecture
Data architecture is the models, standards and rules that govern how organizations collect, store, transform, distribute and use data.
Even today, IT largely controls the flow of data at many organizations. If a business analyst wants access to a different data stream, they would need to submit the request to IT for the team to approve and deliver. After a waiting period, it’s still not guaranteed that the first delivery is exactly what the analyst requested. This is only one of many inconvenient steps in the data-acquisition process that limit how individuals can perform their job, and ultimately how quickly a company can move and act on data.
A modern data architecture can put IT and business users on the same playing field, enabling them to make fast decisions on what data they need, where they can find it, how it needs to be applied and how to operationalize it.
Key Aspects Of Effective Data Architecture
Every organization’s data needs are different, ensuring there’s no single right way to approach the framework. However, looking across a variety of industries and company sizes, there are a few key components that should be at the core of every modern data architecture:
• Shared data: A modern data architecture can eliminate data silos, combining data from all corners of the organization along with external data sources. This ensures the latest information is available companywide.
• Simple access: In the past, users gained access to data that was available—not necessarily data they wanted or needed. A modern data architecture should provide self-serve tools and adequate data access for users to do their jobs and make data-driven decisions. Data should be self-serve for business users and not always in the hands of IT.
• Smart automation: Automation eliminates many tedious tasks that make legacy systems tedious to use. However, the real differentiator is the application of AI and ML to adjust and recommend solutions for such things as data quality.
• Security/governance: Modern data architectures should ensure data is appropriately available for each user. They should also recognize emerging threats to data security and support data policies and regulatory compliance with legislation like GDPR and HIPAA.
• Flexibility and scale: Companies need to work with all of their data where it resides and in any format. This means you should not have to move data before knowing its value and how it can be used. Moving data greatly increases risk and costs; so it should be avoided. Additionally, companies are not all on the same trajectory. With every swing, an organization’s architecture should scale up or down quickly and affordably. Companies also need to be able to easily test new tools without undergoing lots of integration work.
Many of today’s data architecture options possess the above components, with some rising in popularity quicker than others. Regardless of the desired architecture, whether it’s a data fabric, data mesh or something else, organizations need a flexible data stack that can create any of these architectures without any integration work to avoid rigidity, vendor lock-ins or data lock-ins.
Time To Thrive
The clunky processes and rigid nature of many traditional data architectures pose a real challenge for many businesses to compete against newer, agile organizations. Enterprises running older platforms need a leg up to achieve the full benefits that big data can provide. This can lead to the mistaken assumption that data is the problem when, in reality, it is about everything surrounding the data—the data architecture. It is time to reassess and leverage this major opportunity for positive transformation.