Emerging Technologies Defined

To help you make your way through the many powerful case studies and “lessons from the experts” articles in What Works in Emerging Technologies, we have arranged them into specific categories: analytic databases, collaborative/agile/lean data integration, columnar databases, data governance, data warehousing appliances, mobile business intelligence, real-time data integration, software-as-a-service, and unified data integration platforms. What do these terms mean, and how do they apply to your organization?

Emerging Technologies in Data Integration

Data Governance

Data governance is usually manifested as an executive-level data governance board, committee, or other organizational structure that creates and enforces policies and procedures for the business use and technical management of data across the organization. Common goals of data governance are to improve data’s quality; remediate its inconsistencies; share it broadly; leverage its aggregate for competitive advantage; manage change relative to data usage; and comply with internal and external regulations and standards for data usage. In a nutshell, data governance is an organizational structure that oversees the broad use and usability of data as an enterprise asset.

Collaborative/Agile/Lean Data Integration

Collaborative data integration is a collection of user best practices and tool functions that foster collaboration among the increasing number of technical and business people involved in data integration projects and initiatives. Collaborative data integration is important to data governance and providing visibility into ongoing data integration projects. Agile data integration involves the implementation of agile software methods and frameworks to increase the speed and effectiveness of data integration development and collaboration between business and IT on data integration projects. Lean data integration applies lean manufacturing management system ideas to data integration projects. Lean data integration focuses on continuous improvement and the elimination of wasteful steps in project development.

Real-Time Data Integration

Real-time data integration addresses business needs for data to support operational decision making and notification of business events as they occur. Technologies for changed data capture, messaging system integration, ELT, SOA data services, and dynamic partitioning for concurrent data processing are among those frequently deployed for real-time data integration. Requirements vary; organizations must make architectural decisions about whether real-time or “right-time” data integration is appropriate, and whether some transformation steps would be performed faster inside the data warehouse than using external middleware servers.

Unified Data Integration Platforms

Unified data integration platforms bring together technologies to address the range of data integration needs in a unified fashion. In this way, organizations can reduce the cost of data integration, ensure that the most important requirements are being met with the appropriate resources, and execute policies for governance, risk, and compliance. Unified data integration can enable organizations to provide services for managing data integration processes across hybrid environments that include cloud, software-as-a-service, and on-premises systems.

Emerging Technologies in Data Warehousing

Analytic Databases

Analytic databases are distinct from OLTP or mixed workload systems because they are designed from the ground up specifically to meet requirements for analytics, business intelligence, OLAP, data warehouses, and data marts. Increasingly, analytic databases are appliances that feature tightly integrated components. Some analytic databases are designed to run in memory. Analytic databases include columnar databases and OLAP (including MOLAP) systems. Most analytic databases are read-only and are updated by operational and transaction systems.

Columnar Databases

A columnar database stores data by column rather than by row. Columnar databases have garnered attention as organizations design systems or deploy appliances that are specialized for analytic workloads. Unlike transaction systems that need to perform functions on entire rows of data, analytical queries are typically concerned with selecting data with particular attributes, located in a small number of columns. Columnar databases excel at reducing the time it takes to find this data by storing, accessing, and retrieving data by column rather than by row. Many columnar databases use advanced compression techniques, indexing, and in-memory architecture.

Data Warehousing Appliances

A data warehouse appliance offers a special-purpose, pre-installed, plug-and-play system that integrates servers, storage, operating systems, database management, and other software necessary for supporting data warehousing workloads. While data warehouse appliances can be specific to certain hardware platforms, many can run as software-only systems on different platforms. Most take advantage of massively parallel processing (MPP), which offers linear scalability. While the appliance concept is not new, reductions in the price of components are making these powerful systems affordable for mid-market and departmental organizations as well as larger enterprises.

Emerging Technologies in Business Intelligence

Mobile Business Intelligence

Mobile business intelligence is about performing data reporting, visualization, analysis, access, and sharing via a smartphone or tablet computer. While mobile BI is still an emerging technology, as users spend more of their time on these devices, expectations will grow for having BI and analytic applications available on them. Mobile BI furthers the trend toward putting BI in the hands of nontechnical operational users, giving them actionable information for customer interaction, purchasing, performance management, and more. Users will implement mobile BI in a combination of ways: natively on devices, remotely over networks, or via applications available in the cloud.


Software-as-a-service is a new way of delivering applications when a third party hosts your applications or infrastructure on a platform outside your firewall. The service provider hosts all customers on the same application and platform, achieving economies of scale and simplifying administration and upgrades.

This article originally appeared in the issue of .

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