HP Large Data Discovery experience helps enterprises accelerate large data applications

While the enterprise's high management solution controls the importance of data diversity, speed, and quantity, many executives believe that their institutions cannot address the challenges involved. According to a global survey commissioned by HP, more than half of corporate executives reported that their institutions did not have the right solution for big data. In addition, they lack the technical expertise and strategy to combine all the components together to bring new and old data together. With more insight into big data, companies can take action to improve and transform their businesses.

The new HP Data Discovery experience (HP Big Discovery Experience) simplifies and accelerates the way customers release their information value. This service provides a scalable discovery environment. Customers can explore different technologies there to experience insights from their own data. This enables customers to transition from a high level of large data concepts to the reality of governance and action based on this information.

These insights, for example, can help an enterprise optimize real-time cross channel marketing, customer service, and demand forecasting. Other application cases include support for customer efforts to detect and prevent fraud, to ensure compliance in real time, to use social media for risk management, and brand reputation.

HP's Big Data Discovery Experience supports a low-risk shortcut to leveraging large data technologies for technological innovation and better business outcomes. The HP Data Discovery experience is provided in a service-wide manner, with HP providing large data consultants, data scientists, more convenient processes and advanced technology. This service does not require customers to invest upfront for skilled resources, software, and hardware.

No matter what the industry, the big Data solution is an important investment in time and money for any institution, said Markus Ruff, vice president of information management and analysis at HP's corporate Services division. The HP approach combines business and it from the outset, helping customers shorten the time to start large data plans and discover new business insights to make faster and better decisions.

Evaluate and experience the value of large data

HP's large data discovery experience helps customers adjust their business and it strategies for large data, giving customers an understanding of their data and the value they can derive from that data. This insight will help customers accelerate the development of their information strategy.

As part of HP's large data discovery experience, HP Consultants collaborate with customers ' employees to provide advice and identify core parameters for success, and conduct a series of planning processes to assess current and future state requirements. In addition, HP will make diagnostic evaluations of important aspects of people, processes, and data preparation.

HP's large data discovery experience can identify key insights based on customer data to create a functional proof of concept, roadmap, and business justification.

The service provides customers with an information ecosystem that explores different types of data through the use of specific industries using case and analysis models. HP is also able to use insights based applications in the production process.

HP's large Data discovery experience provides consulting-focused consultancy services and rich solution functionality in a standard configuration. This standard structure combines the best structured and unstructured data features, application design and deployment tools, and leading security, privacy, and confidentiality features. The platform, a service-provided approach, leverages HP's various technologies, including HP autonomy and HP Vertica department software and HP's infrastructure and cloud products. The platform also leverages Open-source software frameworks such as Hadoop and partners such as Tibco Spotfire Analytics.

(Responsible editor: Fumingli)