Data analytics has the potential to improve nearly every aspect of society. Its practical applications include improving cybersecurity, preparing for natural disasters, reducing crime, allocating healthcare resources where they are needed most, responding to staffing challenges, and ensuring food safety. Agencies that successfully integrate and analyze data across business units are able to make faster and better decisions that ultimately improve citizens’ lives.
To glean accurate insights, agencies need to access and analyze data spread across disparate systems. Many are turning to the hybrid cloud as a pragmatic way to manage all of their data. In fact, 98 percent of organizations globally are planning to use multiple hybrid clouds within three years, according to IBM research. However, hybrid cloud is just part of the solution to a complex problem that doesn’t have a one-size-fits-all answer. For most organizations, cost, governance, security, and compliance requirements dictate what data can move to the cloud and what must stay on premises. Organizational culture also comes into play – business units must be assured that the accuracy and security of their data will be protected.
Before embarking on a data analytics effort, agencies must first understand the following:
- The number of data sources and how many reside in an internal environment versus third-party services
- The physical location of data, including whether it is spread across cloud environments
- Data analytics tools in use that don’t work well together
- Regulatory requirements for data placement and privacy
- Whether teams have necessary skills to use data in a self-service environment
Next, agencies need to determine how to integrate data for analysis. One approach is to develop a data lake, which creates a vast pool of data elements. However, because it ingests raw data, a data lake may run afoul of governance and security policies. An alternative, pragmatic approach is the creation of an end-to-end data management and analytics platform. Such a platform, based on industry-standard containers and using standard application programming interfaces, makes it possible to consistently manage and use data in a hybrid and multicloud environment.
Historically, many organizations embarking upon data analytics have used a range of tools, from data management and governance, to data science and visualization offerings. The IT organization had to enable those components to work together. Today, however, with the emergence of machine learning and artificial intelligence (AI), traditional integration methods can’t keep pace. A platform designed with pre-integrated, unified work?ows and a visual, intuitive interface ensures all elements work together to support an agency’s data and analytics goals.
A comprehensive data analytics platform should include the following core capabilities:
- Ingestion and integration
- Analytical data management and storage
- Data access
- Discovery and exploration
- Actionable insight
- Analytics in motion
- Information management and governance
- Security and compliance
IBM Cloud Pak for Data, IBM’s a pre-integrated, Kubernetes-based multicloud platform for data and AI, helps agencies become data-driven organizations. Through data virtualization, IBM Cloud Pak for Data enables agencies to collect, organize, and analyze data wherever it resides – databases, data warehouses, catalogs, and cloud stores. Data virtualization retrieves and interprets data, but does not require uniform formatting or a single point of access. Unified, collaborative workflows help business units to overcome data silos and put insights into action.