Enabling the Data Scientist to Drive Business Advantage
The business of data analysis is at an inflection point. The emergence of new sources and types of data provides a treasure trove of information available for businesses. Exploring these massive amounts of multi-structured data and applying them to business has given rise to the “data scientist” - a new role in organizations tasked with developing new data-driven products or business applications such as recommendation engines, deep behavioral segmentation and targeting, and fraud or bot detection.
The data scientist bridges skills across multiple domains including computer science, mathematics, data mining, and business analytics to rapidly explore and discover insights in data. Once found, these insights need to be shared and operationalized in the business to drive the bottom line.
The scale and complexity of both the data and analytic techniques available today poses unique challenges to the data scientist. Tools which provide both analytic expressiveness and performance at scale provide a platform for rich data exploration.
Aster Data provides solutions that enable the data scientist to quickly and easily explore big, complex data and deliver new, high-value analytics to the business. Teradata Aster Discovery Platform allows data scientists to:
- Rapidly access and process large-scale multi-structured data from a variety of sources including online, text, machine-generated, and social network data
- Deliver diverse analytics that scale to big data by using either set-based SQL and/or embedded MapReduce procedural processing in a single application
- Enable on-the-fly investigation and analysis to a broader set of users in the organization by leveraging custom or pre-built analytic functions exposed through standard ANSI SQL to business intelligence (BI) or analytic tools. Custom functions are easily developed with a visual integrated development environment (IDE) for rapid testing and deployment to the business.
- Influencer analysis – understand whose actions have impact in the network to encourage the behavior of peers for purchases, attrition, or just engagement. See a demo.
- Recommendation engines – increase cross-sell and repeat purchases by identifying other products in which a customer or prospect is likely to be interested
- Web analytics - advanced click-stream, golden path analysis, viewer engagement, segmentation, and more. See a demo.
- Cross-channel marketing attribution – move beyond the skewed input of last click analysis to accurately determine campaign impact effectiveness across all channels
Example Applications of Data Science
- Digital marketing optimization – Analysis of user behavior, intent, and actions across search, ad media and web properties to increase the ROI for digital media marketing efforts.
- Social network and relationship analysis – Uncover deep social relationships and interactions hidden in raw transaction data, online behavior, and social networks in order to gain behavioral insights, target influencer marketing, and analyze virality within the social network.
- Fraud detection and prevention- On-the-fly analysis of transactions, interactions, and systems to detect, block, and prevent malicious users, networks, and programs engaged in fraud.
- Machine data analysis - Analysis of sensor, location, and machine to machine communications to optimize operational efficiencies.
Barnes & Noble
Podcast: DMNews Interviews Manan Goel, Teradata to discuss the Teradata Aster Discovery Platform 5.10 launch
Data Scientist: IT or Business? Or Both?
The Role of a Data Scientist: The definition of a data scientist
|White Paper: MapReduce and the Data Scientist|
|Webinar: MapReduce for the Business Analyst: Simplifying Big Data Analytics for the Business|
|Webinar: How Barnes & Noble is Making Big Gains with Big Data Analytics|
Now that all our data is in one place, we can understand customer interactions across our entire [retail/ online/e-reader] ecosystem. (MapReduce helps researchers) see trends more quickly than possible in systems only using massively parallel processing.
Marc Parrish, Vice President,