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
Business users, business analysts, and data scientists have diverse data needs and specialties, but they all have one thing in common: they are tired of long, complicated, and tedious data preparation. Unfortunately, data preparation is getting even more difficult as users doing analytics and data discovery reach out to larger volumes of different types of data.
Cambridge Semantics, Datameer, Pentaho
The Internet of Things (IoT) is hot and getting hotter. Consumers use it for health monitoring and “smart” home devices, such as thermostats and appliances. On the business front, a piece of equipment—or any business asset, really—can be tagged, monitored, and analyzed. This might include a sensor-enabled pressure valve on a piece of drilling equipment, a tagged piece of construction material, food moving to market, or a chip placed in an employee badge, not to mention smart cities, smart power grids, and more.
Fern Halper, Ph.D.
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IBM, Teradata, Tibco
Despite their ongoing evolution, data warehouses (DWs) are more relevant than ever as they support operationalized analytics and wring business value from machine data and other new forms of big data. In the age of big data analytics, it’s important to modernize a DW environment to keep it competitive and aligned with business goals.
Philip Russom, Ph.D.