The big data landing causes BI to change two big challenges to uncover

With the development and application of information technology, the Fulcrum network has brought the most direct opportunity for enterprises in various industries in business development. Also produces a large number of business-related data, which may involve corporate financial information, marketing data, customer information and so on.

Will the retention of these data provide strong support for business development? According to the relevant agencies, in the Fortune 500 enterprises, the average of each enterprise will be kept in the last 7-10 years of customer data, and these data are often not fully utilized. At the same time, such data are still growing substantially. According to a recent study by economists, humans created about 2005 years of data about the number of EB bytes, and 2011 reached the 1800 EB byte. Similarly, IDC, a market-research firm, said the digital content would show a doubling of growth every 18 months. Gartner forecasts that up to 80% of corporate data will become unstructured in the future, and that data sources cover both traditional and non-traditional channels.

Two challenges facing big data

In recent years, with the development of Internet and information technology, the volume of global data shows explosive growth. Mobile communications, wireless devices, social networks, remote collaboration, and many cloud-based services are the main causes of data growth. It can be said that the explosive growth of data has brought management problems to all sectors of the world.

For example, in the telecommunications industry call data records management, financial industry transactions data and customer data management, retail industry supply chain management and manufacturing industry business performance management and so on. For corporate users, the advent of large data also virtually increases their liability and costs. Businesses must maintain the availability of these data over a long period of time and meet the storage requirements of these huge volumes of data. This leads to the enterprise needs to purchase and maintain the necessary hardware equipment, and continue to monitor and management.

So what can companies do with these massive amounts of data? Many astute managers have realised that the data could be a real advantage for businesses, providing insight into business operations beyond rivals. But the problem is the ability and speed of data processing. The speed of the data is limited by the limits of the physical device, which affects the time required to access data and operational instructions from the database. This means that a complex calculation will not be completed in real time and may last for hours or even days.

On the other hand, the speed of operation is not the only key requirement for large data development. The current large data is ubiquitous, and most of it comes from unstructured external data. Businesses need to find a tool that can help them gain, analyze, manage, and share information. As we see in the Gartner 2011 Technology Maturity Curve (Hype Cycle), large data technology is maturing and rising to the "overheating phase". As businesses demand faster data access, they also want to be able to quickly understand what these data can do. This involves two main challenges of large data in enterprise applications, real-time data analysis and real-time business response.

One of the challenges of "real-time data analysis"

About 15 years ago, there was no current business intelligence (BI), but it was called "decision support." At that time, companies have been able to obtain useful business development insights from their data until the recent explosion in data volume, which makes it more difficult for companies to gain real-time data analysis.

In the 2012 Davos 9374.html "Report of the World Economic Forum," the analyst in the enterprise now wants to be able to develop the analytical solutions they need and produce and apply the reports themselves, in the hope of being able to participate as little as possible in the operations of the IT department. They feel that reliance on it greatly slows their productivity. They also want the data to be visualized. Because at present enterprises and consumers are more than ever expected to be able to obtain a higher degree of data-related visual reporting, not only in the form of charts, graphs and hotspots, but also to design a custom form of preference. ”

It is not difficult to see that the clear data points and analysis results for the enterprise is very valuable. However, today's analysis is limited to the enterprise's existing database is not enough, but also need to maintain data and the current social and global relevance. This means businesses need to get data from social media, geographic locations, CRM systems, government-made public data, mobile-phone information, and other non-proprietary sources, for additional analysis.

On the other hand, for real-time analysis, the current mobile application potential is just beginning to appear, and its mobile analysis will be more far-reaching than expected. For example, advertising or other targeted purposes can analyze the real-time behavior of mobile device users. In mobile application analysis, companies with a large number of mobile office staff can be used to analyze the use of mobile devices, which may include data, information and geographic location and so on. Apart from mobile applications, real-time analysis can also be obtained from wireless sensor networks, road network monitoring, air traffic and railways.

In short, any enterprise's goal is to obtain a real and real-time data analysis, through large data to find new ideas. Because large data often involves years of historical management data, organizations can improve decision-making, accelerate performance, and increase productivity and efficiency.

(Responsible editor: Lu Guang)