12 Game-Changing Technologies Fueling the Data-Driven Enterprise

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In addition, most analytics and business intelligence tools are now provided through the cloud or software as a service, providing ways to quickly access insights. Salesforce, one of the most common cloud application suites, has accelerated these capabilities with its wave analytics platform, which can provide insights on both Salesforce and non-Salesforce data.

The challenge for data managers is ensuring data security as applications and functions move to the cloud.

Database as a Service

Database as a service, also known as DBaaS, offers a way for enterprises to maintain and update back-end technologies as well as integrate data from multiple, changeable sources without the need to rewrite the applications that depend on them. It also provides an approach for making data readily accessible to end users who need it regardless of the device they are using.

A recent survey of 300 members of the Independent Oracle Users Group (IOUG), conducted by Unisphere Research, finds cloud computing is part of the mainstream of enterprises and, along with it, comes a growing interest in DBaaS as a viable approach to serving their enterprises. The survey finds organizations are employing a range of new strategies and approaches to improve the speed of data delivery and integration (“Database as a Service Enters the Enterprise Mainstream: 2016 IOUG Survey on Cloud and Multitenant Strategies,” March 2016).

DBaaS isn’t just gaining a foothold in enterprises—it is expected to take off significantly, with adoption nearly tripling over the next 24 months. In the next 2 years, 73% of managers and professionals expect to be using DBaaS within their enterprises, versus 27% who are doing so at present.

The challenge for data managers will be providing a consistent and robust environment from which end users can access any desired data source or application.

Artificial Intelligence and Machine Learning

Artificial intelligence and machine learning are poised to enter the enterprise mainstream, promising to provide self-healing and management capabilities for complex systems, ranging from climate-control to internal IT infrastructures. This is where data analytics plays a critical role, as these systems rely on algorithms that can provide both predictive and prescriptive analytics against data streaming through enterprise systems.

The challenge for data managers will be in applying these capabilities in environments that will best augment the strengths of their employees.

Internet of Things

The ability to read and analyze streams of data coming in from devices, sensors, and applications spread across the globe will change the relationship between organizations and their customers. Estimates of the number of connected devices, sensors, applications, and embedded systems across the globe run as high as 6 billion, with an approximately 5 billion-plus new “things” coming online every day.

The amount of data being generated by these connected endpoints is significant, and it is creating new requirements and ways of configuring data systems. The Internet of Things (IOT) promises to greatly enhance customer relationships, as enterprises will remain connected and capable of upgrading and maintaining the products they ship, in real time, long after the initial sale.

The challenge for database managers will be to shift from historical reporting and analytics to real-time analysis.

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