IBM launches "Big Data" global research white Paper

Recently, IBM announced a global publication of the "Big data" research White Paper "Analysis: The application of large data in the real world", and in the next 2013 data press conference on the Chinese market to explain the depth of the White paper's core ideas. The white paper was published by the IBM Institute of Business Values and the Oxford University School of Commerce. Based on extensive research on 1144 business people and IT professionals in 95 countries and 26 industries worldwide, the report presents a global perspective of the Organization's view of "Big data", revealing the way in which these organizations have embarked on "big data", while also reflecting the progress that these organizations use "Big data" to service their businesses.

"Most companies have recognized the potential of ' big data ' to improve decision-making processes and business outcomes, but they don't know how to get started," said Michael Schroeck, head of global information management at IBM's Global Enterprise consulting service. Surveys show that organizations from all walks of life and around the world have begun to take a pragmatic approach to the ' big data ' work. Although most of these organizations are still in the early stages of acceptance, the best of them have begun to derive significant value from the ' Big Data ' project. ”

"The school is working with academics from Oxford University to develop and support courses and research projects, and we will integrate world-class ' big data ' analysis and application expertise into teaching and research content," said Janet Smart, a management researcher at the School of Business. ”

Five driving factors in the practice of "big data"

"Analysis: The application of large data in the real World" surveys show that most of the "Big data" projects currently being carried out by various organizations are aimed at improving the customer experience, and that being close to customers is the top priority for most organizations to practice "big data".

In addition to "customer-centric" (49% per cent of respondents ranked them first), "Big Data" is also used to achieve other functional goals in the early stages. Nearly one-fifth (18%) of respondents ranked optimal operations as their primary goal. Other applications of "big data" focus on risk and financial management (15%), implementation of new business models (14%), and employee collaboration (4%).

Internal data: The main source of "Big data"

More than half of respondents see internal data as a major source of "Big data". This suggests that companies are taking a pragmatic approach to "big data" work, as well as demonstrating that there is still great value in their internal systems that has not yet been developed.

Internal data is the most mature and easy to understand data that an organization can obtain. These data are collected and collated through years of enterprise resource planning, master data management, business intelligence applications, and other related work, and are consolidated and standardized. Using analytical techniques to interpret these internal data from customer transactions, business transactions, events, and e-mails can provide valuable insights into the organization.

External data: not yet fully utilized

However, in all organizations that implement "Big data" projects, less than half of organizations are currently conducting data collection and analysis of external data sources such as social media.

One reason is that many organizations have difficulty coping with and harnessing the uncertainties inherent in certain data types, such as weather, economics, or the emotions and real ideas that social networks reflect. Respondents questioned the possibility of believing in online comments, opinions, tweets and other forms of free speech. Despite the uncertainty, there is still valuable information in social media data. Organizations must understand and harness the uncertainty of data and how it should be used for their own purposes.

Another reason why social media and other external data sources are underutilized is the skill gap. For most organizations, mastering advanced new data analysis capabilities is still a major challenge in gaining value from "Big data", such as unstructured and streaming data such as text, sensor data, geospatial data, audio, images, and video. Only 25% of respondents in the survey said they were capable of analyzing highly unstructured data.

"Large Data" adoption

Three-fourths of respondents (76%) are currently carrying out "big data" project development work, but the report confirms that the majority of respondents (47%) are still in the early stages of planning, but at the same time 28% of respondents are developing pilot projects or have implemented two or more "big data" solutions. Nearly One-fourth (24%) of respondents have yet to embark on "big data" activities, and are still studying the benefits of big data for their organizations.

Clearly, "Big data" will bring booming business opportunities. Nearly Two-thirds (63%) of respondents said that the use of information (including large data) and analysis created a competitive advantage for their organization. In the survey, the proportion of respondents to the "competitive advantage" was increased by 70% compared with the 2010 IBM Survey (37% per cent in 2010).

Analysis: The core ability to practice "big data"

Today, most organizations that practice "big data" are starting with analyzing structured data using core analytical capabilities, such as queries and reports (91%) and data Mining (77%). Two-thirds of respondents said their organization used predictive modeling techniques. But "Big data" also requires organizations to have the ability to analyze semi-structured and unstructured data, including a variety of new data types.

In more than half of the "big data" projects, respondents said their organizations used advanced technology to analyze text in natural state, such as a transcript of a call center conversation. These analytical techniques include interpreting and understanding subtle linguistic features, such as emotion, slang, and intent. Such data can help businesses, such as banks and telecommunications service providers, understand the customer's current emotional state and gain valuable insights that can be used directly to drive customer management strategies.

(Responsible editor: The good of the Legacy)