Today’s global digital economy is bringing new technologies, products, customers, and business models together faster than ever. In order to keep pace and compete effectively in this environment, organizations must be capable of processing, integrating, and managing traditional internal sources with external data quickly.
Doing this creates additional demands on you as your business seeks to insert intelligence into all decisions, whether they’re made manually or as part of an automated process.
Our Big Data and Analytics team can help. By focusing on the business problem you are trying to solve, our team can help you create a flexible, dynamic data warehouse that quickly adapts to the changing requirements of your users and customers. If you’ve already got a data warehouse that isn’t meeting the needs of the business, our consultants can review its data model and performance and optimize its operations.
The LRS® Big Data and Analytics team has expertise in:
- Traditional Data Warehousing: The LRS Big Data and Analytics team can help you with a variety of activities, including evaluating or recommending technology options. We work with your primary business objectives in mind, and are experts in collecting and analyzing information, locating data sources and planning data transformations, and creating and populating a data model that lets your uses answer the questions they need to ask.
- Data Warehousing Appliances: Do you require extreme performance? Is managing your data warehouse consuming too many resources? Let LRS show you how appliances that are purpose-built for data warehousing can increase performance by 10-100x and eliminate most of the tasks required in managing traditional RDBMS systems.
- Logical Data Warehouse: The digital economy offers new opportunities for analyzing more data from more places, but challenges around complexity, cost, and time of pulling them together may make creating a single data repository not possible. Data virtualization delivers transparent, on-demand access to relational, NOSQL, and Hadoop data that is stored on-premise or in the cloud.