- Organizations leverage less than half of their structured data for insights. The latest Forrester data and analytics survey finds that organizations use on average only 40% of their structured data for strategic decision-making.
- Unstructured data remains largely untapped. Organizations are even less mature in their use of unstructured data. They tap only about a third of their unstructured data sources (28% of semistructured and 31% of unstructured) for strategic decision-making. And these percentages don’t include more recent components of a 360-degree view of the customer, such as voice of the customer (VoC), social media, and the Internet of Things.
- BI architectures continue to become more complex. The intricacies of earlier-generation and many current business intelligence (BI) architectural stacks, which usually require the integration of dozens of components from different vendors, are just one reason it takes so long and costs so much to deliver a single version of the truth with a seamlessly integrated, centralized enterprise BI environment.
- Existing BI architectures are not flexible enough. Most organizations take too long to get to the ultimate goal of a centralized BI environment, and by the time they think they are done, there are new data sources, new regulations, and new customer needs, which all require more changes to the BI environment.
- Partial solutions — Agile BI and big data — address only parts of the challenges. Agile BI addresses the challenges of complexity and timeliness, and big data makes more data available to decision-makers. But only a few leading organizations leverage the best of both worlds by using their Agile BI tools to seamlessly access, process, and analyze data staged in Hadoop-based data hubs.
- Manage delta updates with custom coding or commercial ETL platforms.
- Consider commercial SQL-for-Hadoop engines to handle referential integrity.
- Deploy commercial platforms to supplement missing rollback capabilities.
- Recognize that OLTP is not a native capability of any file system, including HDFS.
- Custom-code transactional controls..
Step 2 — Discover: Find What Data Exists In Your Source Systems And Hadoop
- Discover and profile your data sources before they are ingested into Hadoop.
- Discover and profile HDFS-based files.
- Provide data lineage and impact analysis.
For more details on the 6 steps to analyze Hadoop based data, plus overview of specific use cases such as building new BI apps on Hadoop vs. porting existing BI apps to Hadoop, as well as evaluation of key BI on Hadoop vendors such as Alation, Apache Kylin, Arcadia Data, AtScale, Attivio, Datameer, DataTorrent, JethroData, Kyvos Insights, Oracle Big Data Discovery, Platfora, SAP Lumira, Splunk, Tamr, Teradata Loom, Waterline Data, and Zoomdata please see the detailed research report.