In a 2015 survey by TDWI, 69% of respondents identified SQL on Hadoop as a must-have for making Hadoop ready for enterprise use. This is not surprising because both technical and business users know and love SQL, plus have portfolios of tools that rely on it. The catch is that early versions of Hadoop were devoid of ANSI-standard SQL.
Philip Russom, Ph.D.
IBM, Looker, Teradata
Many organizations need a more modern data warehouse platform to address a number of new and future business and technology requirements. Most of the new requirements relate to big data and advanced analytics, so the data warehouse of the future must support these in multiple ways, while still supporting older data types, technologies, and business practices. Hence, a leading goal of the modern data warehouse is to enable more and bigger data management solutions and analytic applications, which in turn help the organization automate more business processes, operate closer to real time, and through analytics learn valuable new facts about business operations, customers, products, and so on.
Philip Russom, Ph.D.
As organizations incorporate newer data strategies, they also need to consider data-centric security. Data-centric security focuses security controls on the data, rather than perimeter servers or other infrastructure or the network. The goal is to protect sensitive data where it is stored and where it moves. This is becoming increasingly important as organizations start to deal with big data and newer data management platforms and hybrid architectures that include Hadoop and the cloud. Yet, TDWI research suggests that organizations still seem to focus on perimeter security and on application centric security for sensitive data. They think they are focused on protecting their data, but the reality is that many organizations don’t classify their data or know where their sensitive data lives, much less how to protect it.
Fern Halper, Ph.D.
Cloudera, Liaison Technologies, Striim
Many organizations are responding to their raised awareness of the need for data governance by introducing data governance programs, hiring Chief Data Officers, and forming a data governance council. And while there are numerous guidelines and methods for the operating models for a data governance practice, recommendations regarding its day-to-day operationalization are much harder to come by. Specifically, how does an organization design an operational environment for instituting business data policies for usability and enforcing those policies consistently across the enterprise? Answering this question is necessary for achieving the data governance discipline without getting in the way of the business.
In a highly competitive market, today’s forward-looking organizations are seeking to optimize and modernize their IT investments, specifically in enterprise business intelligence (BI). There’s a strong push to capitalize on newer features such as self-service BI, advanced analytics, and customized visualizations—all of which relinquish the centralized data governance necessary for corporate and regulatory compliance.
Organizations are pursuing data lakes in a fury. Organizations in many industries are attempting to deploydata lakes for a variety of purposes, including the persistence of raw detailed source data, data landing and staging, continuous ingestion, archiving analytic data, broad exploration of data, data prep, the capture of big data, and the augmentation of data warehouse environments. These general design patterns are being applied to industry and departmental domain specific solutions, namely marketing data lakes, sales performance data lakes, healthcare data lakes, and financial fraud data lakes.
Philip Russom, Ph.D.
Informatica Corporation, Cognizant Technology Solutions
Big data analytics is full of potential – but also fraught with pitfalls, obstacles, and a fog of hype surrounding the technologies. To be successful, organizations need to know where to begin with big data analytics and how to sustain progress so that they can achieve objectives. With key strategic initiatives hinging on success with big data analytics – including developing competitive innovations in customer intelligence and engagement, fraud detection, security, and product development – organizations need a roadmap for how to move ahead.
Fern Halper, Ph.D., David Stodder
Hewlett Packard Enterprise