From Big Data 1.0 to Big Data 2.0

From Big Data 1.0 to Big Data 2.0

Big data contains various possibilities. But to paraphrase George Bernard Shaw’s famous quote above, how should business leaders take proactive action rather than react reactively? In the process of pursuing value maximization, companies should take the initiative and prepare for a rainy day. At the right time, big data can provide timely insights into emerging trends that are difficult to detect in small data, making companies more forward-looking when formulating strategies. How should this be done? Jiuzheng Building Materials Network summarizes it as follows:

In fact, in a highly competitive environment, big data may force companies to take action rather than being forced to react. However, assuming that the enterprise has carefully weighed the advantages and corresponding costs of big data application, which one of the countless possibilities brought by big data is the most beneficial? Big data will bring three possibilities to the enterprise's strategic improvement Characteristics:

Answer known problems in existing businesses and focus on improving performance and operational efficiency.

Answer new questions in your existing business and focus on business growth opportunities.

Answer new questions in new businesses with the goal of rewriting the competitive landscape.

Although enterprises have different depths of big data applications, research shows that current big data applications are still mainly in the first stage, and the time is ripe to focus on the second stage of applications. A recent survey of more than 100 chief information officers in multiple 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 disruptive technologies in 2013, ranking only After cloud computing deployment and mobile support. As Clayton Christensen defines it in his book The Innovator’s Dilemma, a disruptive technology should create a new market and eventually overtake the existing market. According to Christensen's definition, the current application of big data in enterprises generally only plays a maintenance role, that is, it is only used to improve existing products and thereby obtain more profits from higher-end customers.

From Big Data 1.0 to Big Data 2.0

“If a person is born with great talent and does not meet the right time, his potential will surely decline. If he is born with mediocrity and his potential is not transformed, his nature will definitely be weak.” --Laozi

New infrastructure or data sources can realize some of the value of big data by answering existing business questions, especially as existing data increases significantly, leading to traditional ways of creating business value through data unsustainable situation. For example, Rackspace's initial email hosting service had a very limited customer base. Later, its number of customers quickly increased to 1 million, with up to 150GB of log records in various formats every day. This challenged Rackspace's ability to use its legacy data systems to handle troubleshooting requirements. Tasks that once took minutes now take hours. As a result, Rackspace had to move to a Hadoop stack-based big data infrastructure in order to continue to realize the value of its email hosting service.

Big data can answer questions faster and better. For example, telecommunications companies can supplement existing customer data with new data on customer interactions from social networks, thereby increasing the value of customer churn analysis.

However, a closer look shows that these types of big data applications have not brought about changes in the basic strategies and methods of enterprises. For example, the purpose of companies understanding customer churn remains basically the same, with just the added attribute of social media data. This relatively conservative approach seems to characterize today's big data applications. In a 2010 survey by The Economist magazine, when asked "What new opportunities does big data bring to your company?" the first thing most respondents mentioned was "improving operational efficiency." (51). In sharp contrast, the number of companies that chose "service and product innovation" only ranked fourth (24).

Given the economic climate of 2010, with many companies focusing more on cutting costs, it may not be surprising that "improving operational efficiency" is the choice. However, as the economy improves, companies' focus has shifted from cost reduction to business growth, so other big data application methods should be adopted.

To engage in disruptive innovation, companies must adopt new models and find new ways to create and stimulate growth. Recall how Web 2.0 technology driven by content production subverted the Web 1.0 era based on content consumption, bringing huge changes to the way enterprises interact with customers, the way products and services are innovative, collaborated, and marketed. Likewise, Big Data 2.0 strategies will open up new markets, allowing leading companies to seize fleeting opportunities and reap huge benefits before their competitors do.

Big Data Business Strategy Evolution - Taking Taxi Company as an Example

Big Data 1.0 Strategy

Scalability Technology: Singapore Taxi Operating Company ComfortDelGro Initially Taxi booking service is handled via human phone calls. Later, as the number of customers surged and manual telephone services were unable to meet demand, the company began to invest in big data technology, investing US$60 million to develop a taxi booking system consisting of an automatic dialing system and a smartphone application. The backend data infrastructure could Supports storing and processing hundreds of thousands of trips. The operation data of 15,000 taxis and hundreds of millions of real-time GPS positioning information have enhanced the company's operational capabilities and can handle 20 million taxi bookings per year.

Big Data 2.0 Strategy

Reshape customer behavior: ComfortDelGro has collected years of daily taxi operation data and demand fluctuation data. As Singapore's population and tourism continue to grow, in response to the continued increase in taxi bookings at specific times of the day or week, the company has adjusted prices through various surcharges during specific times and areas, a move that has reshaped its customers booking model that enables the company to consistently meet customer needs.

Create new products and services: Real-time understanding of the location of customers and taxis, combined with historical booking records, allows taxi companies to technically predict congestion at different times of the day, such as every day or weekends, to avoid congestion Based on the best driving route, the company can provide a new service of real-time route recommendation. This service can not only help taxi drivers predict business volume and traffic conditions, but can also be sold as a third-party value-added service to taxi drivers of other companies.

Data ecosystem vision: Reliable automatic traffic route prediction services are based on a data ecosystem vision. The data in this system is shared by taxi operating companies, traffic control departments and environmental protection departments. These organizations have complementary data and interests. The traffic control department has a real-time overview of the city's transportation, while taxi operating companies can obtain Its moving vehicles have a small but detailed set of traffic trajectories. This data, coupled with real-time weather and traffic information from the environmental protection department, can more effectively predict traffic congestion. This service benefits all three parties at the same time. The traffic control department hopes to alleviate urban congestion. Smooth roads mean a lot for taxi companies. Income has increased, while environmental protection departments are more concerned about reducing carbon dioxide emissions.

New business strategies for disruptive big data

By reviewing relevant research and discussions with industry leaders, we came up with three big data strategies for disruptive innovation.

The first is customer strategy, which uses customer interaction data to reshape customer behavior rather than simply understanding it. This type of data allows companies to predict and guide market demand that has not yet emerged, thereby creating new profits. This strategy can be combined with product strategy to develop new needs for new products and services to enable big data to generate revenue. Equally important, these strategies alone do not deliver sustained benefits. We also need an ecological strategy, which is the third strategy, whereby companies can participate in or even reshape a new industry-oriented group, and members can improve their overall operating level through data sharing.

However, in some areas, some companies have begun to actively reshape customer behavior, rather than just understanding customer behavior. This involves a comprehensive understanding of customers, including their behaviour, preferences and competitive behaviour, as well as real-time location data derived from triangulating signals from base stations or wireless hotspots.

Customer Strategy: Reshaping Customer Behavior

Michael Cavaretta, technical leader for predictive analytics and data mining at Ford Research and Innovation Center, believes that “big data’s The essence of it is that it allows you to see and react." This reactive stance is widely adopted by many data-driven businesses when dealing with customers. Until recently, the main way for companies to understand customer behavior was to hire a market research firm and then respond to customer needs based on the survey results. Today, the market's channel for expressing emotions has gradually shifted to social media, but the main way for companies to understand customer behavior is still basically a passive approach.

However, in some areas, some companies have begun to actively reshape customer behavior, rather than just understanding customer behavior. This involves a complete understanding of customers, including their behaviour, preferences and competitive behavior, as well as real-time location data derived from triangulating base station or wireless hotspot signals. This allows companies to provide highly customized products and services to customers at the right time through the most appropriate channels.

Companies such as Netflix and Amazon use this data to determine the hobbies and preferences of their respective customers, and use this information to provide customers with relevant and useful services in real time, thereby influencing their purchasing behavior. For Netflix, the recommended service is not limited to new movies, but also includes old movies, which helps reduce licensing costs. Likewise, retailers can understand customer preferences by leveraging customer credit cards and real-time registration data from places like Foursquare, and then send promotional messages through mobile apps to influence customer purchasing behavior.

Recently, we worked with a financial institution to carefully assess its loan and borrowing risks by gathering data on multiple macroeconomic indicators, including consumption index, house price index, and national loan write-offs (how many loans are there) Cancellation due to irrecoverability), etc. This comprehensive approach raises the threshold of stress testing to a more realistic level and changes the attitude of financial institutions towards risk assessment.

However, implementing this strategy faces particular challenges. The main issue is personal privacy. Issues related to personal or sensitive information should be handled with as much care and transparency as possible, even if the information does not originate from personal data. From an execution perspective, companies also need to anticipate changes in customer behavior. Since it is impossible to determine how many customers a company's recommendation service will ultimately impact, this issue cannot be ignored. In some cases, companies are unable to fully understand and control their supply chains to meet the changing needs of customers with real-time services. The conclusion is that companies must continue to focus on their customers to determine what level of “impact” is appropriate.

Product Strategy: Developing New Products and Services

Many companies in the data value chain are in the “busy zone” of data communications and are strategically positioned to benefit from existing data obtain economic benefits. Most of these companies come from the communications, media and entertainment industries. These businesses interact extensively with customers through digital channels and are becoming repositories of valuable customer data.

Many companies rely on this type of data to gain insights and support their daily operations to serve existing markets and customers. Banks have been improving customer satisfaction by gaining a comprehensive understanding of their customers through customer profiles, transactions and online and mobile banking. For example, minimizing ATM machine shortage incidents and improving product and service pricing. However, other companies are already creating value through data, targeting new markets, innovating and designing entirely new business models. For example, through smartphone clients, telecommunications companies can obtain detailed information about their large customer base in real time, including location, usage, social networks and other characteristics. They use this data to launch new services, such as location-based marketing.

For another example, in addition to ordinary telephone services, Singapore's three local telecom operators M1, Starhub and Singtel have cooperated with Singapore Press Holdings and other retailers to provide customers with location-based advertising short message services. The number of SMS messages sent and possible customer response rates ultimately translate into additional revenue for the telecommunications company.

Therefore, big data can be used to provide customers with real-time life information services. These strategies can help telecom companies retain customers while generating more revenue. This idea also applies to other fields. For example, insurance companies launch new products and services rather than just selling standardized policies. Integrating customer risk appetite, policies taken and historical claims data over a period of time into new regulatory reports is more practical than traditional methods.

Since new products or services often cater to unknown markets, this product strategy is not limited to well-known companies and their subsidiaries, but also provides huge business opportunities for new entrants into the market. For example, real-time price comparison services in the retail sector allow Australia's GetPrice and the UK's PriceRunner to provide customers with more price information while also opening new channels for more targeted online advertising. In the field of health care, Castlight Health, founded in 2008, uses big data to provide patients with health care cost information, which is generally difficult for customers to access. The social networking site PatientsLikeMe has established a free forum and friendly communication environment, where patients can find other patients with similar conditions, taking similar drugs, and even similar laboratory test results. It generates revenue by selling data to drug manufacturers, keeping all processes open and transparent, and users knowing how its data ratings, comments and opinions are being used.

Of course, innovating products and services through big data also faces many challenges. New entrants to the market should be aware of the legal and ethical aspects of data use, especially if it involves customer personal data or extracts information from private big data for profit. Policymakers around the world have been reviewing data-related laws, and the body of case law is developing in multiple jurisdictions. 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 will likely keep pace to cover all possible applications. Therefore, for companies that use big data to develop new customer and product strategies, they are at least obliged to ensure that customers have the right to know about the use of their own data and to provide them with sufficient information for them to make informed choices. Only in this way can both parties benefit. At the same time, operational transparency is conducive to strengthening supervision and moral self-discipline, and enhancing corporate reputation, customer loyalty and corporate brand.

Relying entirely on product and service innovation to achieve data commercialization may also cause certain long-term risks. Before a complete system is established, the new market is likely to be disrupted by other new developments. From a data perspective, data needs to be examined from an ecosystem perspective. Within this system, data providers, beneficiaries, competitors and regulators can develop healthily and benefit from data sharing.

Ecosystem Strategy: Data Ecosystem Vision

Often, a company cannot fully understand its customers and find it difficult to launch new and attractive products or services. In this case, companies can obtain supplementary data from other companies in the ecosystem to fill in the gaps. This ecosystem is based on appropriate collaboration strategies that benefit all parties involved, from businesses to consumers. This ecosystem vision can take many forms. One end is cooperation between enterprises that compete with each other in the traditional sense, while the other end is full-scale collaboration among public institutions to better deliver services. In addition to the short-term benefits of mutual cooperation, this ecosystem strategy also helps spread risks and benefit all parties in the long term.

There are already examples of this type of data collaboration in the insurance field.

For example, identifying and preventing fraudulent auto insurance claims can not only improve insurance companies’ bottom lines but also lower auto premiums. BIA members share claims data from millions of customers, which is then analyzed centrally at the Insurance Fraud Bureau, a not-for-profit body set up by the BIA to tackle fraudulent insurance claims. This information from the database is called an “insurance fraud record” and significantly reduces the number of fraudulent claims each year. The British Insurance Association said that "these insurance fraud records help insurance companies identify user fraud and take appropriate response measures. This information can be used throughout the life cycle of car insurance products, whether it is renewal, claims or any other stage." Comes in handy."

Several music industry organizations, including publishers, music service providers and composers associations, are working to create a "global repertoire database" to shape the music industry's digital future. This is a unique and authoritative song library available to users in all regions. The database can be used by all organizations in the music distribution value chain to ensure accurate and efficient licensing of musical works and subsequent royalty payments. The online business model for the provision, consumption and licensing of music services is rapidly evolving, and the establishment of this database marks an important step in the transformation of this model.

Although there are relatively few empirical cases of big data applications, industry internal strategies tend to focus on using big data to solve specific regulatory, commercial or technical risk issues that all parties are concerned about, while creating a fair The environment allows companies to compete for customers in a normal way. This approach minimizes potential conflicts that would otherwise lead to the collapse of the cooperative alliance. It also confirms Evan Rosen’s point of view: This type of alliance has a clear structure from the beginning, creates value for both parties and treats participating companies fairly and consistently. Only in this way can cooperation between competing companies be successful. Makes sense. On a cross-industry basis, big data offers the possibility for telecom companies and financial institutions to collaborate and jointly gain more insights, especially in retail payments and mobile technology integration. By leveraging their respective customer data, they can collaboratively analyze the combined data and then create a truly differentiated mobile banking platform.

In this ecosystem, government departments should also play a role. Many businesses can benefit from additional data, such as real-time weather and traffic information. This information is usually collected by public authorities, and the cost of copying this data is extremely expensive for any company. Businesses are encouraged to partner with government agencies and share the input costs of data collection, as they have a stake in the downstream impacts of the service. For example, when planning cargo movements, companies can benefit from combining their internal freight and ordering data with external, real-time port data from sensors and radars deployed by port authorities. This will also help port management departments ensure the safety of personnel and ships and logistics efficiency, and then be willing to invest in sensor equipment.

What can business leaders do?

This article’s three strategies for big data will bring many opportunities to companies in the right business context. Business leaders can ask themselves some questions to determine whether they can realize the positive and disruptive potential of these strategies.

Consumer strategy. Big data provides companies with more opportunities to reshape consumer behavior and meet needs that consumers themselves may not be aware of. To determine whether you are ready to take advantage of big data, companies should first answer a few questions: What purchasing decisions do consumers make, and what processes are involved in purchasing decisions? Are there opportunities to use new data to influence consumer purchasing decisions? If Yes, where does this necessary data come from? Is the necessary infrastructure in place to utilize big data cost-effectively, efficiently and in a timely manner (including real-time, if necessary)?

Product strategy. Businesses should also assess their readiness to launch new products and services that provide a competitive advantage. This requires answering questions about the value and volume of existing data.

Do they have unique assets? Will integrating these assets solve a market need? Will the new products and services be launched in new markets or existing markets? If into new markets, through which channels? Will investments in new products and services have an impact on existing markets? Is there an opportunity cost to the business?

Ecosystem strategy. Companies should analyze whether they can gain the most value from strategic changes in isolation, or whether they are better suited to collaborate with other companies to conduct unique and powerful data analysis. Do you fully understand all other companies in the business value chain? If the answer is yes, then business leaders should determine whether these opponents have data sets or business insights that are complementary to their own business? In addition, business leaders The possibilities for sharing data without losing your competitive advantage should also be determined.

Not all companies are fully prepared or have the necessary capabilities to implement the above three strategies at the same time, or they only need to implement one or two of the strategies to improve the performance of the target business. No matter which strategy is chosen, enterprises should be able to gain timely insight into the economic value contained in big data and rationally develop big data resources, from authorizing and managing the required talents to appropriately investing in technical infrastructure to ensure operations. At the same time, enterprises should also fully weigh the costs of the facilities and technologies required to store, classify, and analyze large amounts of data against the potential benefits of big data.

Is big data bringing about a data revolution? Although the industry’s understanding of big data has increased significantly and there are more and more related tools, for most companies, disruptive changes have not yet come. As people make full use of the advantages of big data and combine it with new business strategies proposed by big data, in the near future, new companies will make a big splash and open up new markets, abandoning the hype and focusing on using big data to discover and solve new businesses problems, meet changing market demands, and maintain a sustainable competitive advantage.