Successful companies know that analytics is the key to winning customer loyalty, optimizing business processes and beating their competitors.
By integrating data from multiple parts of the organization to enable cross-functional analysis and a 360-degree view of the customer, businesses can make the best possible decisions. With more data and more sophisticated analytics, you can realize even greater business value.
Today businesses can tap new sources of data for business analytics, including web, social, audio/video, text, sensor data and machine-generated data. But with these new opportunities come new challenges.
For example, structured data (from databases) fits easily into a relational database model with SQL-based analytics. Other semi-structured or unstructured data may require non-SQL analytics, which are difficult for business users and analysts who require SQL access and
Another challenge is identifying the nuggets of valuable data from among and between multiple data sources. Analysts need to run iterations of analysis quickly against differing data sets, using familiar tools and languages. Data discovery can be especially challenging if data is stored on multiple systems employing different technologies.
Finally, there is the challenge of simply handling all the data. New data sources often generate data at extremely high frequencies and volumes. Organizations need to capture, refine and store the data long enough to determine which data to keep, all at an affordable price.
To exploit the competitive opportunities buried in data from diverse sources, you need a strong analytic foundation capable of handling large volumes of data efficiently. Specifically, you need to address the following three capabilities:
Data Warehousing - integrated and shared data environments for managing the business and delivering strategic and operational analytics to the extended organization.
Data Discovery - discovery analytics to rapidly explore and unlock insights from big data using a variety of analytic techniques accessible to mainstream business analysts.
Data Staging - a platform for loading, storing and refining data in preparation for analytics.
Teradata Unified Data Architecture™ product includes a Teradata data warehouse platform and the Teradata Aster discovery platform for analytics, as well as open-source Apache Hadoop for data management and storage as needed.
The Teradata Active Enterprise Data Warehouse is the foundation of the integrated data warehouse solution. This appliance works well for smaller data warehouses or application-specific data marts.
For data discovery, the Teradata platform uses patented SQL-MapReduce® on the Aster Big Analytics Appliance, providing pre-packaged analytics and applications for data-driven discovery. Mainstream business users can easily access this insight using familiar SQL-based interfaces and leading business intelligence (BI) tools. If you are performing discovery on structured data, a partitioned data lab in the data warehouse is the recommended solution.
Hadoop is an effective, low-cost technology for loading, storing and refining data within the unified architecture. However, Hadoop is not designed as an analytic platform.
The Teradata Data Warehouse Appliance and the Teradata Extreme Data Appliance offer cost-effective storage and analytics for structured data. The Teradata Unified Data Architecture™ integrates these components into a cohesive, integrated data platform that delivers the following capabilities:
- unified management of both structured and unstructured data at optimal cost;
- powerful analytics spanning SQL and MapReduce analytics;
- seamless integration with the existing data warehouse environment and user skillset.
The Teradata Unified Data Architecture™ handles all types of data and diverse analytics for both business and technical users while providing an engineered, integrated and fully supported solution.