From "Intuition" to quantitative analysis enterprise management makes big data decision

Remember last year 8 15 competition? If you are the CEO of a certain electric company, would you adopt such a competitive strategy? Change is the eternal theme, business management can not be static. In the big data age, it is unwise for the electric trader to adopt price warfare, and to control the management based on the big data strategy will help you to surpass the existing CEO.

Cost leading strategy, differentiation strategy and centralization strategy are the three strategies that enterprises can choose in market competition. In the era of information explosion, the fourth competition strategy-large data strategy has become the support of the original three competitive strategies.

Big Data Change Enterprise decision

The traditional enterprise management process is the problem, logic analysis, find causal relationship, put forward the solution, make the problem enterprise become excellent enterprise, this is the reverse thinking mode. The consulting process of large data competition strategy is to collect data, quantify analysis, find out the mutual relation, propose the optimization plan, make the enterprise from excellent to excellent, is the positive thinking mode.

"Data is the basis for future competitive advantage and will be an important resource." "As cloud computing, mobile Internet, social networking and big data are growing fast, this technological advance will change every aspect of business," said IBM CEO Rometty at an event organised by the nonprofit Foreign Relations Association March 9. "Rometty that big data will change the way companies make decisions, value creation and value realization."

Now the management consulting industry pursues the "plan", "feeling" type of business management is to the leaders and managers based on their subjective perspective and experience to look at information. At present, even in a scientifically oriented field, decision-making is still based on fixed perceptions. "Later, more decisions will be based on large data analysis rather than personal intuition," says Rometty. "Rometty believes that with more and more information, if the data can be used rationally, enterprise decision-making will be better and more objective." An example of a project "Using data history to reduce crime", which IBM has collaborated with the U.S. Memphis Police, is a case in point. The project analysis found a link between the rape case and the outdoor pay phone. Therefore, the police decided to transfer the pay phone to the room, which led to a 30% reduction in the incidence of rape cases. Rometty further said that in order to make rational use of large data, the way of thinking needs to change.

The biggest shift in the big data age is to give up the search for causation, instead of focusing on relationships, as Schoenberg in the big Data age. That means just knowing what "is" without needing to know why. This is different from the existing thinking practice of scientific research, which provides a new model for human cognition and the way to communicate with the world. Schoenberg points out three thinking changes in large data applications: Random samples to all data, precision to confounding, and especially large data simple algorithms that are more efficient than complex algorithms for small data;

The technical challenges of big data are obvious, but the management challenges are more daunting-starting with the role of the executive team. The most important thing about big data is that it will directly affect how companies make decisions and who make decisions. In today's entire business world, people still rely more on personal experience and intuition to make decisions, rather than on data. In an age of limited information, high cost and no digitization, it is in the real world to make decisions for people in high places. This kind of decision maker and decision-making process is a kind of intuitionistic school, which now encounters Big Data challenge.

Quantitative analysis based on platform

Big data challenges intuition, the first thing to do is quantitative analysis. Business management is divided into many factions because of different viewpoints, but the idea that "cannot be quantified cannot be managed" is a consensus. This consensus is enough to explain why the digital explosion of recent years has been extremely important. With large data, managers can quantify everything in order to master the company's business, thereby improving the quality of decision-making and performance.

The quantitative analysis of large data by enterprise managers should begin with the change of thinking mode. Industry experts point out, first of all, to develop a habit of thinking: "How to say the data?" Whenever there is a major decision, follow the question further and ask, "What is the analytical result based on this data?" The change of thinking of enterprise management will also improve the executive power of enterprise staff on large data management. Second, business managers should allow data to be the master. If employees use the big data from the first line to analyze the results, overturning the intuitive judgment of senior executives, this will be the biggest power to change the culture of corporate decision-making. Based on the large amount of data to make reasonable decisions, the middle of a long process of analysis.

The quantitative analysis of large data here is similar to the traditional "data analysis", where large data also seek to gather wisdom from the data and turn it into an enterprise advantage. The difference is that large data data are huge, the data is fast and diverse. When a data source has these three properties, it forms a platform. Companies that are born with digital genes, such as Google and Amazon, are already big data platforms. However, for traditional enterprises, the potential for using large data to gain competitive advantage may be greater. Companies can do precise quantification and management, make more reliable predictions and smarter decisions, and be more objective and efficient in their actions. These can be achieved in an area that has always been dominated by intuition rather than data and rationality. Although perceptual intuition and rational data are contradictory, perceptual judgment based on rational data is feasible, especially in the enterprise Operation level.

and June Consulting Group partner Senior Consultant Ling that the impact of large data on business management temporarily stay at the operational level, less internal management. Telecom operators, banks, Alibaba, such as platform-type enterprises have enough data, only hope to use large data for enterprise operation and Management. Ling said: "The higher the level of enterprise management strategy, the lower the value of large data contribution." Now, the big data to the enterprise's contribution mainly at the operational level. "In Ling's view, based on telecommunications, banks and other large data platforms, through quantitative analysis, can outline the image of the individual, including personality, temperament, height, weight and so on." With the constant spread of the tools and ideas of large data, the value of many deeply rooted experiences will be shaken. With some other profound changes in the business world, the company to "large data-driven" transformation will encounter enormous challenges, it requires managers to let go of the "Big Data" awareness, the ability to quantify the large data analysis, the use of large data to improve performance management capabilities.

Big Data determines performance

How to use large data to improve company performance? All walks of life have a wide variety of attitudes and approaches to large data. However, there is a certain correlation: the more those customized data-driven companies, platform-oriented companies, the more objectively measured the company's financial and operational results.

For the airline industry, the precision of time is the quality of service, especially when flight arrival time is accurate. A U.S. airlines commissioned a third party research company Passur found that about 10% of the actual arrival time and estimated arrival time difference between 10 minutes, 30% of the flight difference of 5 minutes. In order to improve the quality of service, the PASSU company forecasts the arrival time of the flight by collecting public data such as weather, flight schedule and other non-public data that affect the flight factors independently collected. So far, Passur company has more than 155 passive radar stations, every 4.6 seconds to collect a detection of each aircraft a series of information, which will continue to bring huge amounts of data. Using the Passur Company's services, the airline greatly shortened the time lag between the arrival of the aircraft and the actual arrival. The Passur company plans to provide airlines with the flight arrival time they offer, saving $ millions of a year for each airport.

Big data leads to more accurate forecasts, better forecasts lead to better decision-making and management, as is the case in retailing. American retail giant Sears collects value from these vast volumes of information by collecting customers, products, and sales data for its three brands. The potential value of large data is huge, and the difficulty of digging is huge: These data require massive analysis and are dispersed in different brands of databases and data warehouses, not only in size but also in fragmentation. The Sears company will take eight weeks to develop a personalized sales plan, but it is no longer the best option when it comes to doing so.

The Sears group began using clusters to collect data from different brands and to analyze data directly on the cluster, rather than depositing it in the data warehouse as before. To avoid wasting time, the Sears group combined the data from various sources to make the company's marketing plan faster and more accurate.

The example of Passur and Sears shows the power of big data-it leads to more accurate predictions, smarter decisions and more appropriate operations. When large data is applied to supply chain management, it gives us an idea of why a car manufacturer's product failure rate has soared, that it can continuously investigate and deal with the health care situation of millions of of people, and that it can make better forecasts and plans for online sales based on the data set of product characteristics. The use of large data in other industries has also been remarkably effective, as is the financial, tourism, gaming or mechanical maintenance, which has a great role in marketing and human resource management.

Of course, management based on large data strategies also has many challenges. Adjust the leadership, talent, technology, decision-making, culture in order to deal with large data strategic transformation. The leadership uses the data to design clear goals and know exactly what the success of their definition is. There are goals to be implemented, as the data becomes cheaper, the statistics, visualization and systematization of large data applications become more and more expensive. Good people should be able to deal with large, high-speed, diversified data. The first question that big data-driven companies ask themselves is not "what do we think?" And it's supposed to be, "What do we know?" This requires companies to not follow the feeling.

Only by finding the perfect combination of data science and traditional skills can companies defeat their opponents. Not all winners will use big data for their decision making, but the data tell us that it is the biggest chance.