A complicated data architecture is emerging to cope with the explosive growth of data types and data sprawl. It’s rare that a company has one pristine data lake in which all data sources are stored, secured, accessed, and analyzed. Instead, most companies find themselves owning multiple data lakes and clusters that are managed by varying architecture types, and spread over a series of tiers—cloud, on-premises, and hybrid.
This mishmash can complicate how organizations approach their big data strategy. Businesses realize that big data must be an integral part of their data analytics function, yet data has evolved as a fabric rather than a lake, raising questions about how a business can manage, govern, and organize it. It’s not possible to have one person solely responsible for this task, so an organization must consider which technology resolves this problem.
How can today’s businesses shape their architecture around the unwieldy outgrowth of their data sources? Organizations should look for a global data management solution that helps the business scale its big data strategy and enables employees to manage data more effectively. Businesses need architectures that can accommodate explosive data growth while confronting the challenges of security, governance, analysis, and portability that have resulted from that growth. Choosing open source technology, which can fit inside any environment and work with any data setup, can prevent the need to rearchitect the data environment and avoid vendor lock-in. The ultimate goal is to allow businesses to deploy and access their data when and where they want, without being locked into any single vendor. Following this path allows maximum flexibility and ensures that time and capital spent on big data support are not wasted.
Businesses must address the reality of disparate and ever-growing data sources. They require technology that enables their big data strategy—whatever that strategy entails. What should businesses look for?
Data architectures aren’t being built. Instead, they are being born, taking their shape from new data sets as they originate. You need technology that is as elastic and adaptable as the data sets themselves. Find technology that reduces the risk of your data operations becoming obsolete or constrained by the restriction of only using a single vendor.
Choose technology that reduces dependence on scarce and highly technical skills. Data science skills can be both hard to find and expensive to pay for. Find an environment that lowers the need for those skills and allows you to repurpose or expand the skills of your current staff. Look for tools that democratize the use of data, lowering barriers between access to data and the staff members who need that access most.
Recognizing that every business and data center is different means that extensibility must be a consideration. Look for a pluggable architecture that allows the easy addition of third-party services. In today’s big data ecosystem, this is simply a pragmatic move that will extend the life and future growth opportunities of your data architecture.
Data management no longer means one massive, centralized repository. Global data management must deliver an underlying structure that enables individuals at the local level to make data-based decisions while still keeping data secure and compliant. Choose tools that can scale up and down cost-effectively, matching the changing nature of your business demands.
The speed with which data has grown in recent years pales in comparison to what is coming. Connected homes, cars, hospitals, transport, factories—the list goes on and on—that are driven by sensors and devices that gather and transmit data will only proliferate as this technology expands. Today, your business cannot predict what data sets it will need to connect to tomorrow, so it’s important to select an open data architecture that can accommodate the connected world.
The European Union’s General Data Protection Regulation (GDPR) is a looming challenge facing many businesses, but it will not be the last challenge. Data governance will remain a concern of users and consumers. Look for technology that facilitates the definition of security, and governance policies that are then enforced automatically and seamlessly across all data repositories.
A global data management system can help you manage and consume data with multiple sources, types, and tiers from a single pane of glass. When choosing the best technology to manage, govern, and organize your data, keep these key characteristics in mind, and you’ll be well on your way to successfully scaling your big data strategy.
To learn more about global data management, download this webinar.