Power of Hadoop
The complexity of modern marketing analytics needs is outstripping the available computing power of legacy systems. Complexity and cost are two primary barriers to organizations gaining valuable insights by trying to use outdated systems to leverage big data for advanced analytics on customers and customer behavior. Proprietary legacy systems are not designed to support the logic required to process and transform disparate data sets such as structured customer data from an RDBMS, unstructured social media data and semi-structured clickstream data (from an e-commerce site, for example). By leveraging the power of distributed processing (or massively parallel processing – MPP), Hadoop can handle large volumes of structured, unstructured and semi-structured data more efficiently than the traditional enterprise data warehouse (EDW) (Fig. 1). Cost is another prohibitive factor preventing organizations that are using traditional data management systems to derive optimal value from social media data. Hadoop makes it affordable to collect and store large volumes of unstructured data and streaming data from social media networks and integrate with master data and other data to enable advanced analytics such as customer 360 view.
Modern Enterprise Data Hub
Turning Unstructured Data into Business Insights
Social media generates data that is unstructured; meaning each tweet or post is unique and does not fit into traditional schemas of RDMBS environments. Hadoop excels at processing unstructured data, using tools such as MapReduce, Pig and Hive for loading structured and unstructured data into table format. Because Hadoop works with raw, unstructured data, it provides more flexibility in how marketers use the data. For example, analyzing customer sentiment over the life of a specific marketing campaign (or multiple campaigns), or analyzing against sentiment on competitor products and brands.
Enterprise Data Hub and Real-Time Analytics
The enterprise data hub allows business analysts greater access to data - both in terms of the size and number of data sets, and the time they can access the data. ETL (Extract, Transform, Load) processes have long been a bottleneck for business users that were required to wait for a batch process to be setup for each analytics job. With the enterprise data hub, the data is extracted and loaded into the data hub once so users can run as many transformations on the data as needed. By integrating NoSQL databases into the environment, users can achieve “near” real-time access to data as soon as the data is created.
Social Media Analytics
What if you could analyze conversations about your brand as they occur on social media networks like Facebook and Twitter? NoSQL databases make it possible to analyze sentiments about products and brands in real-time when utilized with the right combination of open source, big data tools. There are two types of analytics that can be used to gain customer insights from social media data: Real-Time (Streaming) Analytics and Batch Analytics. Real-Time, or online streaming, analytics utilize live data feeds to collect, process and visualize information from social media networks. Batch Analytics collect data sets over time (days, weeks, months, years) and allow users
An enterprise data hub is a single, consolidated, fully populated data archive that gives unfettered user access to analyze and report on data, with appropriate security, as Fig. 1 - The complexity of modern analytics needs is outstripping the available computing power soon as the data is created of legacy systems. Enter Hadoop. by the transactional or other source system. The modern enterprise data hub is made possible with open source big data tools, including Hadoop (Fig. 2, next page). The enterprise data hub allows organizations to establish a single version of truth of all enterprise data from structured, unstructured and semi-structured data sources – giving business analysts the ability to perform advanced analytics and answer questions previously too cost prohibitive or difficult to answer.
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here are readily available tools on the market for analyzing social media data… so why use big data technology? Open source big data tools including Hadoop and NoSQL databases allow marketers to access, store, and retain raw social media data for real-time and advanced analytics. This data can be leveraged by marketers to gain insights into customer perception and sentiment about their products and brands… and those of their competitors.