Big data contains all kinds of possibilities. But to paraphrase George Bernard Shaw's famous saying, how should business leaders take the initiative rather than react passively? In the process of pursuing value maximization, enterprises should take the initiative to attack and plan ahead. At the right time, through big data, we can gain timely insight into emerging trends that are difficult to find in small data, so that enterprises can be more forward-looking when formulating strategies. What should I do specifically? Jiu Zheng building materials network summarized as follows:
In fact, in a highly competitive environment, big data may force companies to take action instead of being forced to react. However, assuming that enterprises have carefully weighed the advantages and corresponding costs of big data applications, which one is the most favorable among the many possibilities brought by big data? Big data will bring three possibilities for the strategic promotion of enterprises:
Answer known questions in existing business and focus on improving performance and operational efficiency.
Answer new questions in existing business and pay attention to business growth opportunities.
Answer new questions in new business with the goal of rewriting the competition pattern.
Although the application depth of big data varies among enterprises, research shows that the application of big data mainly stays in the first stage, and it is ripe to pay attention to the application of the second stage. A recent survey of more than 100 CIOs in many industries and regions around the world found that big data (including its application in enterprises and knowledge discovery technology) will be one of the three most subversive technologies in 20 13 years, second only to cloud computing deployment and mobile support. As Clayton Christensen defined in his book TheInnovator's Dilemma, a disruptive technology should create a new market and eventually surpass the existing market. According to Christensen's definition, at present, the application of big data in enterprises generally only plays a maintenance role, that is, it is only used to improve existing products and then get more profits from higher-end customers.
From Big Data 1.0 to Big Data 2.0
"Overqualified, not at the right time, its potential will decline. Life is mediocre, not its potential, but its weakness. " -Lao Tzu
New infrastructure or data sources can realize some value of big data by answering existing business questions, especially when the existing data increases significantly, which makes it difficult to maintain the traditional way of creating business value through data. For example, Rackspace's initial e-mail hosting service had a very limited customer base. Later, the number of its customers increased rapidly to 654.38+0 million, and the daily log records in various formats reached 654.38+0.50 GB. This challenges Rackspace's ability to handle troubleshooting requirements using the original data system. What used to take a few minutes to finish now takes several hours. Therefore, Rackspace had to migrate to Hadoop's stack-based big data infrastructure to continue to realize the value of its e-mail hosting service.
Big data can answer questions faster and better. For example, telecom companies can supplement existing customer data with new customer interaction data from social networks, thus enhancing the value of customer churn analysis.
However, careful observation shows that these types of big data applications have not brought changes to the basic strategies and methods of enterprises. For example, the purpose of enterprises to understand customer churn is basically the same, but the attributes of social media data are added. This relatively conservative approach seems to represent the characteristics of today's big data applications. In a survey conducted by The Economist magazine on 20 10, when asked what new opportunities big data has brought to your company, most respondents first mentioned "improving operational efficiency" (5 1%). In sharp contrast, the number of enterprises that choose "service and product innovation" only ranks fourth (24%). In view of the economic situation of 20 10, many enterprises pay more attention to cutting costs, so it may not be surprising to choose "improving operational efficiency". However, with the improvement of economy, the focus of enterprises has shifted from cost reduction to business growth, so other big data applications should be adopted.
To carry out subversive innovation, enterprises must adopt new models and find new ways to create and stimulate growth. Recall how Web2.0 technology driven by content manufacturing subverts the era of Web 1.0 based on content consumption, and brings great changes to the interaction mode between enterprises and customers, the innovation mode of products and services, the cooperation mode and the marketing mode. Similarly, the big data 2.0 strategy will open up new markets, enabling leading enterprises to seize fleeting opportunities and gain huge benefits before their competitors.
Strategic Evolution of Big Data Business —— Taking Taxi Company as an Example
Big Data 1.0 Strategy
Extensible technology: ComfortDelGro, a taxi operator in Singapore, initially handled taxi reservation service by manual telephone. Later, with the rapid increase in the number of customers, manual telephone service is difficult to meet the demand. The company began to invest in big data technology, and invested 60 million dollars to develop a taxi reservation system consisting of an automatic dialing system and smart phone applications. The data infrastructure in the background can support the storage and processing of hundreds of thousands of trips. 15,000 taxi operation data and hundreds of millions of pieces of real-time GPS positioning information have improved the company's operation ability and can handle 20 million taxi reservation services every year.
Big Data 2.0 Strategy
Remodeling customer behavior: ComfortDelGro has collected daily taxi operation data and demand fluctuation data for many years. With the continuous growth of Singapore's population and tourism industry, in order to cope with the increasing number of taxi bookings at specific times of the day or week, the company adjusted the price through various surcharges at specific times and regions, reshaped the customer's booking model, and enabled the company to consistently meet customers' needs.
Create new products and services: know the location of customers and taxis in real time, and combine with historical booking records, taxi companies can technically predict the best driving route at different time periods, such as different time periods every day or weekend, to avoid congestion. Based on this, the company can provide brand-new real-time route recommendation service. This service can not only help taxi drivers predict business volume and traffic conditions, but also be sold to taxi drivers of other companies as a third-party value-added service.
Vision of data ecosystem: Reliable automatic traffic route prediction service is based on the vision of data ecosystem. The data in the system is shared by taxi operators, traffic control departments and environmental protection departments. These organizations have complementary data and interests. Traffic control departments can grasp the general situation of urban traffic in real time, while taxi operating companies can grasp a small but detailed traffic trajectory from their mobile vehicles. These data, together with the real-time weather and road information of the environmental protection department, can predict traffic congestion more effectively. This service benefits all three parties at the same time. Traffic control departments hope to alleviate urban congestion. Smooth roads mean increased revenue for taxi companies, while environmental protection departments are more concerned about carbon dioxide emission reduction.
New Business Strategy of Disruptive Big Data
By reviewing the relevant research and discussions of industry leaders, we have come to three big data strategies of disruptive innovation.
The first is customer strategy, that is, using customer interaction data to reshape customer behavior, rather than simply understanding. This kind of data enables enterprises to predict and guide the demand that has not yet appeared in the market, and then create new profits. This strategy can be combined with product strategy to develop new demands for new products and services and make big data realize benefits. Equally important, these strategies alone cannot bring lasting benefits. We also need an ecological strategy, which is the third strategy. Enterprises can participate in or even reshape a brand-new industry-oriented group, and members can improve the overall management level through data sharing.
However, in some areas, some enterprises have begun to actively reshape customer behavior, not just satisfied with understanding customer behavior. This involves a comprehensive understanding of customers, including their behaviors, preferences and competitive behaviors, as well as real-time positioning data obtained through base station triangulation or wireless hotspot signals.
Customer strategy: reshaping customer behavior
Michael Cabala, the predictive analysis and data mining technology of Ford Motor Research and Innovation Center, believes that "the essence of big data is that it can let you know and react". Many data-driven enterprises widely adopt this passive position when dealing with customers. Until recently, the main way for enterprises to understand customer behavior was to hire market research companies, and then respond to customer needs according to the survey results. Now the channel of expressing emotions in the market has gradually turned to social media, but the main way for enterprises to understand customer behavior is basically passive.
However, in some areas, some enterprises have begun to actively reshape customer behavior, not just satisfied with understanding customer behavior. This involves a comprehensive understanding of customers, including their behaviors, preferences and competitive behaviors, as well as real-time positioning data obtained through triangulation of base stations or wireless hotspot signals. This enables enterprises to provide customers with highly customized products and services at the right time through the most suitable channels.
Companies such as Netflix and Amazon use this data to determine customers' hobbies and preferences, and use this information to provide relevant and useful services to customers in real time, thus affecting customers' buying behavior. For Netflix, the recommended service is not limited to new movies, but also includes old movies, which helps to reduce the licensing cost. Similarly, retailers can understand customers' preferences by using real-time registration data of customers' credit cards and Foursquare, and then send promotional information through mobile applications to influence customers' buying behavior.
Recently, we cooperated with a financial institution to carefully evaluate its loan and borrowing risk by collecting data of various macroeconomic indicators, including consumer index, house price index and national loan write-off (how many loans were written off because they could not be recovered). This comprehensive method has raised the threshold of stress testing to a more practical level and changed the attitude of financial institutions to risk assessment.
However, the implementation of this strategy faces special challenges. The main problem is personal privacy. Issues related to personal or sensitive information should be handled as carefully and transparently as possible, even if the information does not come from personal data. From the perspective of execution, enterprises also need to predict the changes caused to customer behavior. Because it is impossible to determine how many customers will be affected by the recommendation service of the enterprise, this problem cannot be ignored. In some cases, enterprises can't fully understand and control their supply chain, and meet the changing needs of customers through real-time services. The conclusion is that enterprises must continue to pay attention to customers to determine what degree of "influence" is appropriate.
Product strategy: developing new products and services
Many enterprises in the data value chain are located in the "busy area" of data communication, and their strategic positioning enables them to obtain economic benefits from existing data. Most of these enterprises come from the communication, media and entertainment industries. These enterprises interact with customers extensively through digital channels and are becoming a resource pool with a large amount of valuable customer data.
Many enterprises use this data to gain insight and support their daily business to serve existing markets and customers. For a long time, banks have learned everything about customers through customer information, transactions and online and mobile banking services, thus improving customer satisfaction. For example, reduce the shortage of ATM machines as much as possible and improve the pricing of products and services. Other companies create value through data, aim at new markets, and innovatively design brand-new business models. For example, through the smart phone client, the telecom company can obtain the detailed information of its large-scale customer base in real time, including location, usage, social network and other characteristics. They use this data and information to launch new services, such as location-based marketing. For another example, in addition to ordinary telephone service, three local telecom operators in Singapore, M 1, Starhub and Singtel, cooperate with retailers such as Singapore Press Holdings to provide customers with location-based advertising SMS services. The number of short messages sent and the possible customer response rate eventually translate into extra income for telecom companies.
Therefore, big data can be used to provide customers with real-time life information services. These strategies can help telecom companies retain customers and bring more revenue. This idea can also be applied to other fields. For example, insurance companies introduce new products and services, rather than just selling standardized policies. It is more practical than the traditional way to integrate the customer's risk preference, adopted policies and historical claims data into the new regulatory report.
Because new products or services usually cater to unknown markets, product strategies are not limited to well-known companies and their subsidiaries, but also provide huge business opportunities for new enterprises entering the market. For example, the real-time price comparison service in the retail sector allows Australia's GetPrice and Britain's PriceRunner to provide customers with more price information, while opening up new channels for more targeted online advertising. In the field of health care, Castlight Health, established in 2008, uses big data to provide patients with information on health care costs, which is generally difficult for customers to obtain. The social networking site PatientsLikeMe has established a free forum and a friendly communication environment, where patients can find other patients with similar conditions, taking similar drugs and even having similar laboratory results. It earns revenue by selling data to pharmaceutical manufacturers. All processes are open and transparent, and users are well aware of the purpose of their data ratings, comments and opinions.
Of course, innovating products and services through big data also faces many challenges. Enterprises newly entering the market should pay attention to the legal and ethical issues of data use, especially in the case of involving customers' personal data or extracting information from private big data to obtain profits. Policymakers around the world have been reviewing data-related laws, and the case law system in many jurisdictions is constantly improving. In the near future, the regulatory environment for data commercialization and profit opportunities will change.
With the rapid development of big data, data protection and privacy legislation may follow to cover all possible applications. Therefore, for enterprises that use big data to formulate new customer and product strategies, they have at least the obligation to ensure customers' right to know about their data usage and provide them with enough information to make informed choices. Only in this way can both sides benefit. At the same time, transparent operation is conducive to strengthening supervision and moral self-discipline, enhancing corporate reputation, customer loyalty and corporate brand.
Relying entirely on product and service innovation to realize data commercialization may also cause certain long-term risks. Before a perfect system is established, the new market is likely to be disturbed by other new developments. From the point of view of data, we need to look at data from the point of view of ecosystem. In this system, data providers, beneficiaries, competitors and regulators can develop healthily and benefit from data sharing.
Ecosystem strategy: from the perspective of data ecosystem
Usually, an enterprise can't fully understand its customers, and it is difficult to launch brand-new and attractive products or services. In this case, enterprises can obtain supplementary data from other enterprises in the ecosystem to fill the gap. This ecosystem is based on an appropriate cooperation strategy so that all stakeholders from enterprises to consumers can benefit from it. Ecosystem view can take many forms. On the one hand, it is the cooperation between competing enterprises in the traditional sense, on the other hand, it is the whole process cooperation between public institutions, aiming at providing better services. In addition to the short-term benefits brought by mutual cooperation, ecosystem strategy also helps to spread risks and benefit all parties in the long run.
There have been such cases of data collaboration in the insurance field. For example, identifying and preventing fraudulent auto insurance claims can not only improve the profits of insurance companies, but also reduce auto insurance premiums. Members of the British Insurance Association share the claim data of millions of customers, and then analyze these data in the insurance fraud bureau, a non-profit organization established by the British Insurance Association, to solve the problem of insurance fraud claims. This information from the database is called "insurance fraud record", which greatly reduces the number of fraud claims every year. The British Insurance Association said, "These insurance fraud records will help insurance companies identify user fraud and take appropriate measures. This information can be used for the entire life cycle of auto insurance products, whether it is renewal, claim settlement or any other stage. "
Several organizations in the music industry, including publishers, music service providers and composers' associations, are trying to create a "global track database" to create a digital future for the music industry. This is a unique authoritative music library for users in various regions. All organizations in the music distribution value chain can use this database to ensure accurate and efficient authorization of music works and subsequent royalty payment. The online business model of music service supply, consumption and authorization has developed rapidly, and the establishment of this database marks an important step on the road to change.
Although there are relatively few empirical cases of big data application, the internal strategy of the industry tends to focus on using big data to solve specific risk problems at the regulatory, commercial or technical levels of common concern, and at the same time create a fair environment for enterprises to compete for customers in a normal way. This way can minimize potential conflicts, otherwise it will lead to the collapse of the cooperative alliance. At the same time, it also confirms Evan Rosen's view that this alliance has a clear structure at the beginning of its establishment, creating value for both parties and treating participating enterprises fairly and consistently. Only in this way can the cooperation between competing enterprises be meaningful. In cross-industry, big data provides telecom companies and financial institutions with the possibility to cooperate and gain more insights together, especially in retail payment and mobile technology integration. By making full use of their customers' data, they can cooperate to analyze the merged data and then create a truly unique mobile banking platform.
In this ecosystem, government departments should also make a difference. Many enterprises can benefit from other extra data, such as real-time weather and traffic information. This information is usually collected by the public sector, and the cost of copying this data is extremely expensive for any company. Encourage enterprises to cooperate with government agencies and share the input cost of data collection, because they are closely related to the downstream impact of services. For example, when planning freight transportation, enterprises can benefit from combining their internal freight and order data with external real-time port data obtained by sensors and radars set up by port management departments. This is also conducive to the port management department to ensure the safety of personnel and ships and logistics efficiency, and then willing to invest in sensor equipment.
What can business leaders do?
The three big data strategies in this paper will bring many opportunities to enterprises under the appropriate business background. Business leaders can ask themselves some questions to determine whether they can explore the positive and subversive potential contained in these strategies.
Consumer strategy. Big data provides enterprises with more opportunities to reshape consumer behavior and meet the needs that consumers may not realize. To determine whether it is ready to take the lead in using big data, enterprises must first answer several questions: What kind of purchasing decisions do consumers make and what processes are involved in purchasing decisions? Is there an opportunity to use new data to influence consumers' purchasing decisions? If so, where did these necessary data come from? Do you have the necessary infrastructure to take advantage of big data in a low-cost, efficient and timely manner, including real-time when necessary?
Product strategy. Enterprises should also assess whether they are ready to launch new products and services with competitive advantages. This requires answering questions about the value and quantity of existing data. Do they have unique assets? Can integrating these assets solve the market demand? Will new products and services be put into new markets or existing markets? If you enter a new market, through what channels? Will investment in new products and services cause opportunity costs to existing enterprises?
Ecosystem strategy. Enterprises should analyze whether they can get the greatest value from isolated strategic changes, or whether they are more suitable to cooperate with other enterprises for unique and powerful data analysis. Do you fully understand all other enterprises in the business value chain? If the answer is yes, business leaders should determine whether the data sets or business visions mastered by these rivals are complementary to their own enterprises. In addition, business leaders should also determine the possibility of sharing data without losing their competitive advantage.
Not all enterprises are ready or have the necessary ability to implement the above three strategies at the same time, or only need to implement one or two of them to improve the performance of the target business. No matter which strategy you choose, enterprises should be able to gain insight into the economic value of big data in time and rationally develop big data resources, from authorizing and managing the required talents to properly investing in technical infrastructure to ensure operation. At the same time, enterprises should also fully weigh the facilities and technical costs required to store, classify and analyze a large amount of data, as well as the potential benefits of big data.
Has big data brought about a data revolution? Although the industry's awareness of big data has improved significantly, and there are more and more related tools, for most enterprises, subversive changes have not yet arrived. As people make full use of the advantages of big data, combined with the brand-new business strategy proposed by big data, in the near future, new enterprises will strike hard, open up new markets, abandon hype, and focus on using big data to discover and solve new business problems, meet the ever-changing market demand and maintain a sustainable competitive advantage.