In the face of continuous disruption and a constant deluge of new data sources, businesses are under pressure to quickly turn raw data from multiple sources into actionable insights and deliver real-time value to their customers.
Drawbacks of Legacy Systems
Most organizations, however, are saddled with legacy and archaic analytics systems that were not built to accommodate large volumes and new sources of data.
- Legacy data analytics platforms built on top of massive, on-premise architectures that are slow, unwieldy and hinder business agility.
- Security is often an issue because many legacy systems are no longer supported by the vendor that created them, so organizations do not receive updates and patches to address new security challenges.
- Many legacy systems were not designed to integrate with other software, reducing flexibility and scalability.
- Legacy platforms and related infrastructures may not meet the standards for data storage, management, and governance in place today in industries like healthcare and finance.
- Legacy platforms tend to have high licensing and maintenance costs.
Platform Features and Capabilities Needed to Build Competitive Advantage
To address the drawbacks of legacy systems, take advantage of new technologies and the benefits they provide, and compete effectively in today’s challenging environment, businesses must make a significant transformation regarding technology and speed. They must either have their legacy systems modernized or move to a new, advanced platform that integrates with their existing systems. Regardless of the approach taken, the following features and capabilities are needed to deliver superior customer experiences and build competitive advantage:
- Scalability to address growing volumes of data and data types- structured data, unstructured data, image files, and streaming data from the Internet of Things.
- Simplified, low code technology that reduces the need for highly skilled resources and enables developers and business users to quickly develop and deploy analytics use cases to meet the exact requirements and specifications of their businesses.
- Flexible architecture that works with existing systems
- Unified approach (data lake, analytics, visualization, administration, workflow, security, APIs, real time, AI, ML) that provides a robust, complete workbench for surfacing and delivering real time predictive insights across all types of data
- Subscription pricing for reduced CapEx
- Open-source components that reduces costs, simplifies integration and does not require specialized skill sets
- Enterprise grade governance and security that supports and simplifies data privacy requirements
- Hadoop data lake that ingests any type of data from any source
- APIs and data adapters to easily integrate with any system or application
- IoT analytics for real time AI driven processing and insights
- Built-in visualization that converts information to actionable insight
- Administration and workflow that enables multiple users and use cases on a single platform that is partner friendly
A big data management and analytics platform with the features and capabilities described above provides several invaluable benefits. It shortens time-to-value by consolidating and transforming raw data from multiple sources into trusted data insights. It provides the transparency needed across numerous types of business operations to boost efficiency and solve business challenges that businesses were previously unable to address.
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