The data and analytics landscape is changing. Although many organizations are still analyzing structured data from their data warehouse, TDWI research indicates organizations have increasing interest in analyzing disparate kinds of data. This data is often large in volume and can require modernizing data infrastructures and platforms. The industry around big data and data science and the emerging role of the data scientist is one result of this evolution/revolution.
Users of all types are spending more and more time on mobile devices, whether they are business executives, a line-of-business (LOB) managers, retail inventory clerks, or frontline service technicians. While engaged with customers, managing operations, or strategizing about new products, they need access to critical business intelligence (BI) reports and analytics. For an increasing number of organizations, it is now a high priority to extend BI and analytics to the mobile workforce.
IT operations management (ITOM) deals with monitoring and controlling IT infrastructure and services such as networks, servers, and help desk. Today, IT management typically relies on “swivel chair” monitoring between unrelated reactive monitoring tools. However, this is changing. Modern, interrelated IT departments can benefit from a single view across IT to improve root cause analysis and reduce meantime to resolution.
Big data and data science can provide a significant path to value for organizations. These technologies, methodologies, and skills can help organizations gain additional insight about customers and operations; they can help make organizations more efficient, be a new source of revenue, and make organizations more competitive.
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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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