5 Data Management Challenges to Consider in the Multicloud Ecosystem
Data holds a lot of promise for businesses. The value derived from data drives business growth, fuels innovation, and improves the customer experience.
While the volume and velocity of data are growing, many enterprises are unable to tap into the full potential of today's data-driven world. Not only do they capture and use just a fraction of the data they generate, but they also struggle with data management strategies. This means businesses are missing out on new opportunities and potential revenue.
Changes in Data Management Trends
One of the management challenges stems from the complexities of storing and managing scattered data. Residing in multiple locations, data often sprawls—spreading through endpoints, the edge, and multiple clouds.
Seagate's recent Rethink Data report found that enterprises, with some exceptions, are currently storing data relatively evenly across the edge and public, private, and industry clouds. And, according to recent Gartner commentary, 81% of enterprises that use the public cloud work with at least two different providers—further adding to the complexity.
Seagate's report, based on a survey conducted by IDC, found that enterprises are expecting they will shift even more data to cloud repositories. The top-five factors driving these changes include:
- Improvements to data security (identified by 17% of survey respondents)
- Increased access for data analytics and management services, such as AI/ML and IoT (14%)
- Increased visibility and manageability of IT infrastructure operations (14%)
- Reduced cost and infrastructure total cost of ownership (11%)
- Increased access to data for applications and business units (10%)
The shift to a multicloud ecosystem does not merely present a challenge for IT departments. This change—along with the growing data sprawl—directly affects business owners' ability to extract full value from data and, consequently, to grow revenues.
The Seagate report identified the followingse as top barriers to data management:
- Making collected data usable (39%)
- Managing the storage of collected data (37%)
- Ensuring the collection of needed data (36%)
- Securing collected data (35%)
- Breaking data silos (30%)
Here's a closer look at what drives each of these challenges as data goes through its lifecycle.
Challenge 1: Data Collection
Data is no longer created solely within a data center. The amount of data generated in the cloud, as well as by emerging technologies— such as IoT and edge computing— continues to grow. But organizations are only collecting 56% of the data potentially available through operations, according to the report.
Attempting to capture all available data, however, would strain the existing IT infrastructure and increase costs. That's one of the many reasons why enterprises must rethink data management. For example, identifying and classifying data at the start of its lifecycle enables faster data pruning, and that translates to lower costs.
Challenge 2: Data Silos
Data sprawl results in silos, which makes its difficult for data scientists and analysts who can transform that data into insights for decision makers to access it. Organizational culture can result in additional silos, because competing groups have their own objectives and thus want the ability to keep and control certain data for themselves.
To make data from silos accessible, business owners must address both technological and human barriers. Automated tools, such as unified policy mechanisms, can solve the technology aspect while global data management and global standards can help unify the teams.
Challenge 3: Data Security
Data security consistently ranks as one of the main concerns among IT and business leaders alike. Multicloud security in particular comes with unique issues, including inconsistent visibility across different clouds and a lack of orchestration between different security components.
Vulnerable environments create the risk of data breaches, with consequences ranging from financial losses and regulatory fines to reputational damage and privacy breaches. But the importance of security goes beyond that. Strong security is essential to unlocking the full value of data because it helps ensure both uninterrupted access to data and data integrity.
Challenge 4: Data Storage
Successful data management requires holistic visibility into data storage across on-premises and cloud architectures. This doesn't simply mean data democratization—it means storage unification and data management through a single pane of glass, regardless of where data is stored.
But it's often an enterprise's storage technology footprint, including a proliferation and coexistence of different storage technologies, that becomes challenging. Moreover, many organizations lack a coherent strategy for their data storage.
Challenge 5: Data Usability
The Rethink Data report found that organizations use only about a third of data available to them. They often dump the collected data into large repositories and leave it there to collect dust. Instead of extracting insights from a treasure trove of data, businesses often store it and forget it.
Smart data collection starts with understanding business objectives and the insights that enterprises want to glean from their data. Those goals provide clarity about the type of data that should be collected.
Sorting through massive amounts of data is also part of the usability issue. Organizations need to address complexities, overlapping tools, data integration, and other factors that impact their ability to extract valuable business intelligence.
Evolving to Succeed
Business owners must focus on the challenges of data management in the multicloud so they can exploit the full potential of collected data. This requires a maturing of their current strategies. Better data orchestration—from endpoints to core to cloud—and the adoption of a DataOps model are two methods for improving outcomes.
DataOps is an emerging discipline that’s purpose is to improve the quality, speed, and value of data analytics. According to IDC’s definition, DataOps connects data creators—bey they machines or people who generate reports and information—to data consumers—i.e. decision makers.
DataOps is a process that facilitates collaboration. It can include AI technology to help provide a holistic view of business processes, as well as to better correlate data from cloud, core, and edge sources. By using this advanced data management tool, businesses can start solving the challenges posed by the complexities of the multicloud. Having the ability to use more data will result in better business insights.
Tapping the full potential of data means transforming data into information. As IDC Research Director Phil Goodwin notes in the Rethink Data report, “Whether structured, semi-structured, or unstructured; generated by humans or by machines; or stored in the data center or the cloud—data is the new basis of competitive advantage."
Read more about how enterprises can put more business data to work in Seagate’s Rethink Data report.