Why You Need A Diverse Approach
6 mins read

Why You Need A Diverse Approach

Amit Sharma, CEO, CData.


Enterprise data is more complex, diverse and vital than ever. The only problem? The way we store and manage data has remained, by and large, the same.

The prevailing data management strategy is to store all data in a database, no matter its source, structure or use in the business. But not all data needs to be confined within the walls of a database, and a one-size-fits-all approach no longer fits our dynamic data-driven world.

To fully harness the business intelligence insights buried in data, you need flexible data storage and management solutions that complement your existing infrastructure and grow with your changing needs. With more nimble connectivity options, your organization can unlock greater value from data-driven insights throughout your business.

Modern Data Use Cases Demand Greater Flexibility Than Warehouses Can Offer

Today’s cloud environment has introduced added complexity and new challenges to the management of diverse types of data. Traditional monolithic databases structured around fixed schemas are too rigid to handle the current influx of data sources, structures and applications.

In the cloud, every dataset requires a different level of structure, permanence or accessibility. For example, a sales team may need to access and share the latest figures from a CRM system in real time, while data teams may need to feed AI systems with massive datasets on a weekly basis.

Attempting to fit all data into a uniform system can result in inefficiencies and unnecessary complications. Moreover, storing, integrating and querying information in a single database within the cloud environment often leads to over-provisioning and escalating costs with little value to show for it.

These databases also struggle to efficiently handle diverse workloads and large data volumes, resulting in slower latency, limited access and hindered performance. For instance, if an e-commerce platform experiences a surge in user transactions, a monolithic database may fail to provide real-time inventory updates and process orders in a timely fashion.

As your organization grapples with growing data volumes and evolving data structures, you need more adaptive, versatile solutions that can meet the speed and scale of cloud deployments and align with the characteristics and requirements of each dataset.

How To Diversify Your Data Management Strategy

A robust data strategy strikes a balance between centralization and diversification. Rather than defaulting to a traditional database structure, consider adopting a multi-tiered architecture that provides a unified and consistent view of data across distributed and diverse environments.

As you look to diversify your cloud data management strategy, here are four considerations to keep top of mind:

1. Assess Your Data Landscape, Then Adopt Technology Solutions

The foundation for an effective data management strategy starts by understanding your data needs, capabilities and requirements. Before adopting new technology tools and storage solutions, conduct a thorough analysis of the data that your organization creates, collects and uses.

By identifying the unique characteristics of your data ecosystem, you can choose the right solutions to achieve optimal performance, scalability and cost-efficiency. For instance, a solution best suited for unstructured or semi-structured data, like log files, social media feeds or sensor data, looks a lot different than tools designed for structured data in a CRM, ERP or CMS platform.

2. Embrace And Optimize The Hybrid Cloud

Data doesn’t need to reside exclusively in the cloud or on-premises. In fact, the vast majority of enterprises now rely on hybrid infrastructure that combines cloud and on-premise systems. It’s crucial to ensure that datasets are optimized for the right environment.

For example, you may choose to keep highly sensitive data like intellectual property, personnel records or legal documents on-premises to ensure compliance and robust security measures. Meanwhile, data used in everyday operations—like finance reports, sales and marketing figures or supply chain data—could live in the cloud to ensure collaboration, connectivity and real-time access. Embracing a hybrid data strategy enables you to leverage the benefits of both worlds.

3. Prioritize Data Accessibility (No Matter Where Your Data Resides)

Your employees need the ability to seamlessly access, share, and use data no matter where it resides in the enterprise. Yet, 6 in 10 businesses struggle to bridge disparate data sources.

Many organizations are turning to a data fabric architecture that provides a mix of technology and data solutions to connect different data sources, formats and locations to integrate into a cohesive framework. This multi-tiered approach offers added flexibility and scalability by leveraging a combination of data tools, including data lakes and warehouses, NoSQL and in-memory databases and other storage options. Regardless of the mix of technology, your organization relies on; it’s important to provide data storage and connectivity solutions that offer the greatest breadth and depth of connection possible.

4. Accelerate AI And Other Technology Innovations

Advancements in AI, data analytics and other technologies are revolutionizing data management. Generative AI and large language models enable organizations to simplify data access, analyze vast amounts of data and enhance querying to make data operations more user-friendly and valuable. Other advancements in NoSQL databases and new SQL provide more efficient and effective ways to store and process Big Data.

While many of these technologies are still in the early stages, now is the time to future-proof your data infrastructure so you can take full advantage of them as they mature and become more sophisticated. Businesses that can leverage these solutions sooner will experience a greater advantage in the increasingly competitive, data-first business landscape.

Making Progress On Data Investments

Data management is a long-term investment. You don’t need to create your entire data architecture overnight. Instead, you can take incremental steps to address specific data needs, build upon success and achieve ROI over time.

But you must get started now. Organizations that fail to leverage the diversity of data tools at their fingertips are not only limiting their options—they’re also limiting the potential to revolutionize their business.

No matter where you are in your data management journey, you need every available tool to propel your business forward. So, are you ready to get going?

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