Sensor data from Internet of Things (IoT) devices is becoming more pervasive throughout the world of data management, but it can be both an opportunity and a challenge to existing platforms, integration, and best practices. Your organization needs to understand how its existing integration and data management tools can help with the introduction of sensor data, as well as how business stakeholders, in particular from operations teams, will be using that data to impact revenue and costs. In addition, your organization must enable the speed of performance required in operational and analytics use cases, including productivity to improve organizational performance, process efficiency to streamline company activities, new product development to better meet customer expectations and experiences, new business models for revenue generation and supply chain monitoring, and inventory and cost reduction.
It’s no surprise that data warehouse professionals are quickly adopting Hadoop. According to a recent TDWI survey, the number of deployed Hadoop clusters is up 60% over two years. While Hadoop is an effective design pattern for capturing and quickly ingesting a wide range of raw data types, there have been a number of challenges organizations have faced in realizing the true business value from their Hadoop-based data lakes.
In today’s demanding economic environment, companies that can develop and deploy analytics faster have a significant competitive edge. They can use analytics to detect patterns and changes in markets, learn customer preferences, be alert to fraudulent activity, and more. With the advent of cloud computing, users quickly gain access to new data sources and analytic techniques, enabling companies to finally unleash their analytics – they are no longer constrained by the limits of their on-premises computing, database platform, data warehouse, and data storage capacity. However, to avoid even more data siloes, data governance issues, and more, organizations should consider a hybrid analytics architecture that brings together on premises and cloud, enabling a more controlled journey to the cloud, while enjoying the flexibility, power, and speed they need to handle a range of analytics demands.
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Organizations that seek to be data-driven are experiencing considerable change of late, because data itself, the management of data, and the ways businesses leverage data are all evolving at accelerated rates. These changes sound like problems, but they are actually opportunities for organizations that can embrace new big data, implement new design patterns and platforms for data, scale to greater volumes and processing loads, and react accordingly via analytics for organizational advantage.
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.
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.
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.
Cloudera, Liaison Technologies, Striim