The proliferation of data is creating new opportunities for businesses to better understand their customers, their industry and their own operations. But as the various formats, sources and deployments of data grow exponentially, how can businesses optimize this wealth of new data while remaining compatible with existing systems?
Hybrid data management enables enterprises to drive innovation by seamlessly accessing, sharing and analyzing data. To achieve this level of data management, all types of data—structured, semi-structured or unstructured—should be easily accessed and analyzed whether it’s stored on-premises, in a public cloud, in a private cloud, open source or in any combination of those deployments.
Leverage data of all types and sources with hybrid data management
Hybrid data management enables seamless access, sharing and analysis of all types of data, regardless of its structure or whether it’s stored on-premises, in a public cloud, in a private cloud or in any combination of those deployments.
These capabilities are achieved through the integration of technologies that enable you to analyze a range of new data types and sources—including social, mobile, web and Internet of Things (IoT)—alongside your historical customer and operations data to yield new insights.
Seamlessly integrate cloud and on-premises deployments
Deploying workloads in the cloud can reduce costs and accelerate time to market on application development and new products. Because of these benefits, many businesses are integrating cloud into their IT environments but still depend on on-premises infrastructure for certain workloads.
The ability to access and analyze all data, whether in the cloud or on-premises, is key to effective data management. This allows users to access data from your entire IT environment while keeping that data in the best-suited deployment model.
The ability of machine learning (ML) to stream, analyze and learn from data sets without explicit programming can be extremely valuable, allowing data scientists to develop and improve ML models on the platform where the data resides.
Effective data management should enable data from various sources—including on-premises, cloud and data lakes—to be input into ML models. Workloads such as cross-analyzing unstructured data from external sources with historical structured data to uncover patterns can allow you to predict changes in customer demand, identify potential problems before they occur and more.
Embrace open source technologies and data more securely
Open source technologies drive innovation by providing environments where users can collaborate with data and developers, both inside and outside the company.
Open source users can manage large amounts of unstructured data faster and at a reduced cost. This new data can be analyzed alongside structured customer, financial or inventory information without migration, facilitating security and governance control of sensitive data sets and supporting innovation.
The ability to integrate new cloud-based and open source technologies with existing infrastructure allows you to drive innovation across a business. The flexibility of a hybrid data environment allows IT professionals to evolve with changing technology and business needs.
Empower users across your business with self-service access to data
Gartner predicts that “by 2020, organizations that offer users access to a curated catalog of internal and external data will realize twice the business value from analytics investments than those that do not.”2
With hybrid data management, authorized users from various lines of business can more securely access relevant data from practically any location. This democratization of data insight allows users across an organization to respond more quickly to new opportunities and challenges while reducing strain on the IT department.
Overcome the sprawl of data applications and data silos
Storing data on multiple, disconnected platforms can significantly hinder efforts to discover patterns and insights via cross analysis. Manually collecting and analyzing data from various infrastructures can be very labor intensive and inefficient.
Implementing an analytics engine that connects all data in your IT environment—regardless of where it’s stored—enables deeper and more efficient analysis. This approach also allows greater flexibility by supporting and unifying more deployment options.