Rethinking data management for the data age
Digital healthcare. Self-driving vehicles. Banking chatbots. While not all innovation will be as glamorous as an autonomous car, every industry is reimagining what’s possible with technology. Customers today are not
just demanding smarter products and personalized, in-the-moment experiences—they expect them. Emerging technologies are enabling organizations to shift from iteration to innovation and create new business and
customer value with data. As every organization races to transform, your competitive advantage is determined by how rapidly you can turn data into meaningful insights. Data is the foundation of an intelligent business. But data,
data consumers and the business expectations of data have changed. The shifting realities call for a higher level of data maturity and the right technologies to achieve outcomes.
It’s time to rethink and modernize your data management strategy to ensure you’re set up to achieve better outcomes with emerging technologies, improve business outcomes and digitally differentiate.
Five trends converging in the data era
The digital era is vastly changing the way we live and work. And the pace of transformation is accelerating. Powered by data, emerging technologies are at the heart of all these changes—and, in turn, are placing unprecedented demands on digital leaders. Emerging technologies: Edge computing, 5G, artificial intelligence (AI) and machine learning (ML) are transforming how data is being collected, processed and used. For the first time in history, we’re meeting the explosion of data with intelligent infrastructure, software and algorithms to rapidly turn it into actionable information.
• Emerging technologies bring more and better data into your organization.
• This data can be used to create new value and drive better user experiences
at the edge.
• To produce meaningful insights, the massive quantities of data must be
expertly managed, protected and operationalized across the entire lifecycle.
Exponential data growth: Fueled by an abundance of smart devices and IoT sensors, worldwide data creation has been soaring for more than a decade. More data forms—including unstructured and streaming data types—create new value, but organizations are finding it hard to keep up and harness the full value from the data they’re collecting. Decentralized data: The adoption of emerging technologies leads to more distributed locations where data originates. As data’s center of gravity rapidly moves toward the edge, data is increasingly being stored, processed and acted on closer to its source.
Rising consumer expectations: Today’s consumers are more empowered than ever
and are demanding more data-rich, personalized, real-time experiences. In the past,
you could take days to come up with new data insights, but today that’s far too long.
• The increasing reliance on AI and ML to make real-time decisions in a distributed
environment can strain even the most advanced data management strategies.
• Most organizations don’t have the IT capabilities to keep up because their data
management is fit for an outdated world where insights and outcomes can be
delivered in hours or days. That’s no longer the case today, where every second
counts to derive actionable business intelligence from data.
• To be able to rapidly turn data into insights, organizations must evolve their
expectations and data processing capabilities.
Regulatory environment: Cybersecurity threats are more sophisticated, and the number of data breaches is skyrocketing. Consequently, the regulatory environment is evolving, mandating more resilient data security, privacy and governance. As more data is collected, stored and processed in multiple locations, the attack surface for malicious activity also grows, making compliance with global data laws and regulations more complex. In addition, customers want to do business with organizations they can trust with their data. These trends underscore the ways data, data users and consumers have changed, and how organizations are adapting to stay relevant. Fueled by the escalating growth of data, emerging technologies are ushering in a new era of innovation, where an organization’s competitive advantage is directly determined by how fast data converts into meaningful insights that drive business outcomes and create new value.
What’s possible in the new data era
A decade ago, cloud computing was a novel technology. Now the cloud is ubiquitous, and 82 percent of companies use more than one cloud, while 86 percent expect to do so three years from now.5 Today’s emerging technologies will likewise dramatically reshape all industries.
In the data era:
• Connected living will continue to blur the lines between people
• Our personal and work lives will be enhanced and augmented by
new types of devices and interfaces.
• The relationship between human and artificial intelligence will
become more symbiotic.
Edge computing transforms how people and machines interact virtually everywhere—and as data management practices continue to evolve, so will the relationship between humans and machines. From smart cities to networked realities, immersive experiences will define the next decade. And for digital leaders, human-machine relationships will be the new innovation frontier. Maintaining a competitive edge in this new world will be contingent on your ability to quickly turn your data into actionable intelligence.
Overcoming today’s limitations
Although the importance of extracting actionable insights from data is clear, organizations often lack confidence in their data veracity. Most of today’s data management strategies are optimized for a workflow that transfers data to a central data center, eventually batch-processing it from databases and data lakes. But this centralized approach to data management no longer reflects the realities of the data era. As a result, most organizations will struggle to deliver on the new expectations of the business.
One indicator that organizations are wrestling with data management is the proliferation of dormant data—that is, data they collect but don’t leverage to drive business outcomes. It’s no longer humanly possible to wrap your arms around all of the data in the world today. Data management challenges are one part of the issue, but you also need to have the best technologies and workloads that are optimized for ML outcomes.
Overcoming today’s challenges to create tomorrow’s opportunities
Fueled by the immense data growth, emerging technologies are sparking a new era of intelligence at scale. These technologies enable troves of data to influence real-time decision making and outcomes—all while generating, combining and leveraging even more data insights for continuous improvement. There’s a symbiotic relationship between the advanced, connected technologies you employ to thrive in the digital economy and the wealth of new data waiting to be uncovered. Likewise, there’s a symbiosis between success with edge technologies and data management. By enabling you to act on data near the source, edge technology can both improve efficiency and allow you to create new experiences. Coupled with AI, the edge will change how machines share and react to data—and this is where you’ll find the opportunities to create new value as the world becomes more mobile and connected. But as more functions take place at the edge, you need to manage
data differently and consistently—from the core to across edge and hybrid clouds. That requires changes to your compute, network, storage and
application architectures. Now more than ever, organizations need to rethink data management if they are to become an intelligent business with a leadership position in the data era. At Dell Technologies, we envision a future where you can drastically improve the volume, type and quality of data you ingest, prepare and analyze in a consistent way across edge and hybrid clouds.
Democratizing real-time access to production-ready data sets will unlock the next generation of game-changing use cases that will create new value and differentiate an intelligent business in the data era. Read ‘A Guide to Good Data Management’ to learn how it all starts with data and what the key considerations are for orchestrating an intelligent data management strategy.