Interpreting large data

Gartner recently released a large data analysis report. The report shows that while the Asia-Pacific region is lagging behind the United States, it will soon overtake the US in the next two years from the growth momentum of companies in the Asia-Pacific region. The global media and communications industry, as well as banking and finance, are at the forefront of big data investments, the report said, and the results are most evident in Australia.

According to the report, the Federal Bank of Australia and some retailers have become one of the forerunners of the current big data. But in our country, the electricity merchant and the airline company also began to carry on the big data deployment and the implementation.

According to the relevant data, more than one-third of the companies in the United States have started to invest big data, far higher than other regions. We can see from the figure below that 37.8% of American companies are starting big data projects, while Europe is 26.8%, Asia Pacific is 25.6%, and Latin America is only 17.8%.

Global large Data Deployment statistics chart

However, Asia's companies show huge data ambitions, and another 44.7% will invest in the next two years, something that is not in the rest of the region, and it appears that large data deployments for companies in the Asia-Pacific region will increase significantly over the next two decades.

From the distribution industry of large data companies, media and communications are the first industries to accept and implement large data projects. We can see a lot of TV dramas, movies and so on that are based on large data analysis. Banking and services in the second tier, after all, the user's demand is still the key to the survival of these industries. The most backward is the government and utility investment.

For banking, banks need to analyze consumer habits, user's consumption level, and these data is often very alarming, but once from the user's use habits, banks can be based on user's habit to push different services, from the user's feedback information on their own services for better transformation and improvement.

The Federal Bank of Australia, for example, has launched services based on its own large data analysis program, which can provide personalized and streamlined services, and can provide any customer at any time, based entirely on the processing power of large data.

Put large data products in the window

The results of the big Data Project at the Federal Bank of Australia are obvious, especially in terms of its products and services. The bank has used the results of large data analysis in advertising, and more and more users are starting to focus on these ads, thereby increasing the number of users. Large data analysis helps the banking industry find the value of the data and find more business opportunities.

The Australian Federal Bank has shown its own trading ratios in advertising, which gives banks a powerful competitive weapon that translates the data into customer-service-specific information and related personalized products, which can be a good example of data conversion. In some industries, the monopoly of data is a strong support for companies to dominate.

These data are also a means of creating value for the enterprise itself. The enterprise analyzes and classifies its own user data, and consolidates each data according to certain rules, and then sells the data to other enterprises that need it, thus a complete chain of data analysis is formed.

For example, retail businesses can use these data to promote promotions based on different user spending habits. More than 30~35岁 married men usually go to the supermarket at night to work, while women prefer to spend in the afternoon or at noon.

Do you have the eyes to find the data and use it

According to Gaerner's research report, the world's most backward large data investment sector is the government sector, but there are already a large number of government departments have begun to build their own big data projects. including the establishment of national data base, the National health status of the input statistics. Even in the presidential election, we can see big figures.

For example, when President Obama was in the election, his logistics team defeated the opponents on the basis of large data analysis. This means that large data is not confined to the business environment. We can think that as long as there is data in the environment there is large data, the key is whether you want to find the data and use it.

The large data strategy for public services is a parallel, performance-oriented government data analysis center that requires in-depth analysis and analysis of data collected from all aspects of society. Such data can enhance the Government's ability to reflect on itself, so that the government will get a lot of guidance when it comes to the relevant work.

The key to the enterprise's most critical data projects is the strategic direction of the data and the reorganization of the data. These include best practice guidelines for large data, identification of large data barriers, improved skills, production for data analysis and information asset registration guidance, and timely understanding of large data technologies.

A defining action point is to "improve skills and experience in large data analysis", a challenge that is not the only government, nor is it Australia. The lack of analytical skills in Gartner's research report highlights one of the top three big data challenges, identifying how to get the value from large data and defining large data strategies together.

Sydney University of Science and technology data analysis resources are very scarce. Its Advanced Analysis Institute will provide data and analytical scientific research aimed at helping production graduates to analyze skills in depth. It has worked with collaborating organizations and government departments such as Microsoft, Nokia, AMP,SAS,IBM, western Pacific Bank, IRD, and Human Services. If Australia becomes part of Gartner's research report that accelerates data collection in the Asia-Pacific region.

Other challenges will be determined by the speed with which the Australian organization accepts data analysis. Overcoming the challenge of identifying the top two data from Gartner's research-determining how to get value from large data and defining a large data strategy-will be key.

These challenges are an unavoidable obstacle to organizing large data paths. As Gartner's report points out, the initial effort begins with knowledge gathering and subsequent policy settings.

Then the enterprise shifts to proof-of-concept and, following a successful pilot, starts deploying and using data analysis tools, a point where investment is rising. In Gartner's study, 70% made large data investment institutions "already moving over the formative stages of early knowledge gathering and strategy, into pilot (44%) and Deployment (25%)".

However, for enterprises to adopt large data now, the learning curve to keep getting shorter, because of successful early adopters, they can learn the number of people continue to increase. For example, the earliest adopters of large data-global online businesses such as Google, Amazon, Yahoo and Facebook-must undergo a pilot and pilot process. These pioneering enterprises have to plan themselves of course, in many cases, they even have to invent some of the big data tools that today are considered crucial, for example. Large data are used by leaders who now benefit from the experience of the original pioneers and from their own country and industry big data.

In the early adopters of the value chain enterprises can benefit in a great place, because earlier adopters gain maximum value from large data when they have access to data from the entire supply chain. The advantages and obligations of this approach and participating organizations need to agree on the principles of data ownership, governance and privacy, and how to leverage large data expertise in value relationships.

Other developments can help companies accelerate returns on big data. For example, valuable data are now available from the government's dataset, and procurement is often not charged, such as through the statistics of the census data published by the Australian Statistics Bureau, which can help students in school to carry out relevant academic research.

Datasets play a significant role in the growth of many businesses. Analyze the competitor's information, and improve its service and data continuously. Filling in the blanks in the value of their data sets, along with increasing the depth of data analysis techniques, helps the enterprise gain a fairly solid position in the industry. Enterprises can integrate their large data tools and related expertise, so that small enterprises are also strong competitiveness.

As Gartner's report points out, "for large data, 2013 is a year of trial and early deployment." We can expect to see their footsteps, and some businesses in Australia from 2014 onwards have come close to the use of large data. Large data using curves similar to the cloud where the place is 18 or 24 months ago-a lot of learning, a preliminary experiment, and no much deployment. With the development of cloud computing has been relatively mature, and has a certain service capabilities, large data will become a perfect service system.