JAGGAER, the world’s largest independent spend management company, today announces multiple planned expansions to its Analytics capabilities, the cornerstones of the JAGGAER ONE global spend management platform.  A central data layer supported by data warehouse and data lake capabilities will enable the execution of Analytics from across the whole JAGGAER ONE solution. These enhancements are designed to improve the efficiency of data management for multiple industries and will be phased into production beginning with the release of JAGGAER’s 19.2 platform upgrade.

This series of planned upgrades will bring a single analytics strategy to the JAGGAER ONE platform. Analytics from JAGGAER’s central data layer will be fed directly into the platform, while being shared with an Artificial Intelligence (AI) and Machine Learning (ML) foundation that will deliver recommendations, decisions and actions, bringing the promise of predictive “what if” scenarios into real world applications. This expansion has already improved the performance of JAGGAER’s Analytics dashboards up to 60%.
JAGGAER is adding the data lake capabilities to its existing data warehouse to serve the needs of the many companies that generate massive amounts of unstructured data, but also require data from many external sources to contextualize their analytics. A data lake is designed to manage unstructured and semi-structured data, as opposed to the structured data found in a data warehouse and provides the flexibility to extend beyond descriptive and into predictive and prescriptive analytics. JAGGAER is bringing this innovation to the market to provide for the evolving needs of multiple industries:
Manufacturers make a series of decisions which include make-or-buy scenarios that impact the future of a company. A vast majority of the data is large and comes in raw, creating a natural need for the flexibility of data lakes, which aid with the streamlining of billing and improvement of distribution. Manufacturers utilize data warehousing for product shipment records, records of product portfolios, to identify profitable product lines, analyze previous data and customer feedback, and to evaluate weaker product lines and eliminate them. JAGGAER will now provide both capabilities for the manufacturing industry.
Data warehouses hold customer data that enables traders to experiment with target marketing based on customer requirements. Transportation professionals also use data warehouses to analyze customer feedback, performance, manage crews on board, and customer financial reports for pricing strategies. Data lakes are used for predictions in the transportation industry, with the added bonus of flexibility and low-cost data storage. JAGGAER’s extensive experience with the transportation industry guided the development of both data management systems to provide these capabilities.
The healthcare industry stores financial, clinical and employee records in data warehouses. Common tasks include strategizing and predicting outcomes, tracking and analyzing service feedback, sharing data, and tie-ins with insurance companies and medical aid services. Much of this data is unstructured, which poses a problem for the traditional warehouse, creating a need for JAGGAER’s data lake capabilities. A data lake, used in combination with a data warehouse, provides a powerful solution for this industry problem.
Banks conduct a significant amount of risk management, policy reversal and analysis of multiple types of data, including consumer data, market trends, financial decision making and government regulations and reports. Many financial institutions use warehouses to manage their resources for market research, performance analysis of products, interchange and exchange rates and to develop marketing programs based on patterns. Using a data warehouse is not cost-effective for massive amounts of data, however, creating the need for JAGAGER’s hybrid offering of a data warehouse and lake, to help reduce costs and manage voluminous amounts of structured and unstructured data.