Effectively managing data in an edge-to-cloud world is becoming increasingly complex.
Enterprises need data management simplicity and agility to maximize the benefits they can get from their data. The enterprise that will succeed will shift resources away from mundane data management tasks to focus on using data to innovate and add business value. But to attain that data-focused business agility, it pays to look back at how the world of data management has changed, and to identify where it needs to go.
The three evolutionary phases of data management
Data management has come a long way in a relatively short time — and change is accelerating. In the first phase, most enterprise data was located on premises, used across many applications and stored in data warehouses. The need to manage both structured and unstructured data added complexity and led to the popularity of data lakes.
That all changed with the cloud, a much more scalable and often more economical alternative. In this second phase, enterprises started migrating their data lakes to the cloud. Amazon Web Services (AWS) was first to market with its cloud platform. Soon other vendors, such as Microsoft and Google, brought their own strengths to the cloud.
The third phase of the evolution launched as organizations began storing their data in multiple clouds. Today, this multicloud environment promises both cost reductions and operational efficiencies. However, it also makes data management even more complicated. Moving multiple siloed data lakes to the cloud doesn’t solve all data management problems. In fact, it raises questions:
- Where to store different types of data
- Whether applications have to be in close proximity to the data they use
- How to govern data operations in this new, more complex data landscape
- How to find the data that’s needed, when it’s needed
Today’s leading challenge
As we navigate this third phase of data management, the rise of edge systems is compounding these issues. In fact, Gartner predicts that by 2025, 75% of the data generated by enterprises will be created and processed outside traditional, centralized data centers or cloud environments. And note, this complexity will apply not only to industrial or IoT use cases, but also, for example, to remote and branch offices, the financial industry, hospitals and retail outlets.
Enterprises struggle with how to govern and manage data close to the edge to conduct certain types of processing tasks while — at the same time — moving data to the data center or cloud for analytics purposes.
The way applications in this multicloud environment use data is changing as well. There is no longer a clean boundary between transactional applications (intended for autoprocessing and routine tasks) and analytic applications (intended to derive insights from data). With the convergence of these two application types, every application is rapidly becoming a smart application, embedded with analytics and artificial intelligence.
The result is that there are now myriad, highly distributed sources for data as well as a proliferation of smart applications consuming and processing that data. In between sits enterprise data operations, which must effectively bridge both worlds. This is the leading data management challenge of today.
Answering the challenge
Enterprises need to gain business agility and reduce complexity from edge to core to cloud. Hitachi Vantara is working on solutions to address this data management challenge so that you can focus on using data to add business value. You’ll hear more about our solutions in future articles. In the meantime, here are three critical features you should look for to make the most of this evolutionary phase — and to prepare for the next:
- Flexible data infrastructure. Your data needs to be in the right place at the right time, and you must have a cost-effective way to store it. Moving from siloed data lakes to a single logical data lake not only breaks down silos, it also gives you a flexible data infrastructure where you can cost-effectively move your data so that it is available when and where it is needed.
- Automated data governance. The days of manual data management are over. You must have automated and AI-driven governance capabilities for cleaning, moving and fixing your data. Automated data management should implement policies such as those that cover data privacy and compliance.
- Intelligent data placement. First and foremost, your data must be near the applications that will be using it. Intelligent data placement allows you to place your data in close proximity to the applications and analytics that need it. Second, you want to optimize data management and storage (whether in the cloud or not) for cost efficiency and compliance.
Watch for more in upcoming blog posts that will dig deeper into the cost issues, governance, intelligent data placement and analytics capabilities of simple, agile data management.
Learn more about how to manage your data for business agility with Lumada Data Services.
 Gartner Inc., “What Edge Computing Means for Infrastructure and Operations Leaders,” Gartner Inc., 2018, accessed April 7, 2020, https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders/