Preface
Large or small, no enterprise could remain aloof today as big data has been flooded in every sector, every economy, and everywhere in our lives. Big data is hyped as the next big thing by tech geeks; along with cloud, social media, and mobility, the four transformative megatrends that will shape the global economy over the next decades, as MGI report (2011).
Big Data, though with only two short years since its inception, has been profoundly transforming our lives in all dimensions. More than that, big data, as is claimed, is disruptive in terms of velocity, volume and variety. As such, big data has been termed as data deluge. It is reported that 90% of the world’s data has been created in the last two years, it is being created more overwhelmingly with the easy access to internet through mobile devices, sensors, wearables and social networking tools. It is of no avail to predict what the data landscape would look like even tomorrow. What must be done is to harness the power of big data and uncover the benefits it can bring to our business, or what changes the big data may revolutionize in terms of efficiency enhancement, cost reductions, risk management or attracting customers.
Admittedly, logistics sector is typical of a data-driven business. Logistics and big data are a perfect match. DHL Big data solutions demonstrate how the global logistics company plays ahead of the game by capitalizing on the untamed data asset in an innovative way.
Introduction
It is the era of big data that we are entering in spite of ourselves at an accelerating speed. According to the EMC/IDC Digital Universe Report, the digital universe is growing 40% a year into next decades. It is doubling in size every two years, and by 2020 the data that we create and copy annually in the digital universe is projected to reach 44 trillion. If the foregoing future had been forecast in a sense of ambiguity, let’s watch how the date is being generated the moment we are working on the paper. Some quick facts are instrumental to our understanding how we are awash in a flood of big data every minute. The following snapshot shows the amount of data created online in one minute (NOT an hour or a day, but a minute):
- 2,000,000 Google Searches
- 685,000 Facebook Updates
- 200 Million Sent Emails
- 48 Hours’ worth of video uploaded to YouTube
- 347 New Blogs Posted
What is the implication of the above data? Unstructured, massive, and real-time? Or all of them. It is really big, isn’t it? But we are impressed not only with its being big but its ubiquitous. Big data has been making inroads into all aspects of our lives one way or another. For enterprises, it is time to make a shift of mindset to dig the value and gain competitive advantage from the untamed data by data-driven business insights. It is challenging but there is plenty of headroom for valuable innovation.
Logistics and supply chain, of all the industries, is supposed to be the most ideally situated for using big data analytics given that massive amount of data are being generated every day in supply chain business activities. For instance, the tracing of goods from origin to destination produces an extensive flow of information including geographical, customers, packages, transportation carriers, energy consumptions, and among others. When the enormous amount of data generated is aggregated on a global scale, it turns out to be a huge mine of data. Enterprises embracing the advent of big data configure the capabilities to extract values from such mines as to boost operational efficiency, increase customer experience and loyalty, fortify risk management and exploit innovative business models. Big data and logistics are made for each other, according to CSI report (2013).
DHL is a prime example of how it has significantly benefited by tapping into the power of big data, and gained its competitive advantage globally. For instance, by using GPS and sensor data, they optimized the route, thereby fuel usage reduced, and orders delivered more efficiently. That is only a part of the benefits that big data analytics unleashed. However, there are many enterprises in the logistics sector that didn’t apply the big data. A survey by Accenture (2014) showed that only 30% of the target companies in the logistics and supply chain sectors have implemented the tool of big data analysis as an organizational imperative to run their businesses though more than 90% have a big data strategy in place to be executed in the next 6-12 months.
Big data is the untapped logistics asset, says a report from DHL. In the age of emerging technologies such as mobile devices and sensors, big data would bring unprecedented transformations in the logistics sectors. In particular, with the surge of e-commerce move and delivery on an end-to-end basis, innovations in logistics supply chain will be overwhelming, which will be bound to change the landscape of global economy.
Industry Profile Report
Differences of Big Data from existing technologies (Michael, Michele, 2013):
Unlike past eras in technology that were focused on driving down operational costs mostly through automation, the ‘Analytics Age’ has the potential to drive elusive top-line revenue for enterprises. For those enterprises that become adept with Big Data analytics, they will simultaneously minimize operational costs while driving top-line revenues to net substantial profit margins for their enterprise.
Big Data analytics uses a wide variety of advanced analytics, as displayed below:
Deeper insights.
Rather than looking at segments, classifications, regions, groups or other summary levels you will have insights into all the individuals, all the products, all the parts, all the events and all the transactions etc.
Broader insights.
The world is complex. Operating a business in a global, connected economy is very complex given constantly evolving and changing conditions. As humans, we simplify conditions so we can process events and understand what is happening. But our best-laid plans often go astray because of the estimating or approximating. Big data analytics takes into account all the data, including new data sources, to understand the complex, evolving, and interrelated conditions to produce more accurate insights.
Frictionless actions.
Increased reliability and accuracy that will allow for deeper and broader insights to be automated into systematic actions.
The spectrum of opportunities and pitfalls in the adoption of Big Data:
Opportunities
The key to success for organizations seeking to take advantage of this opportunity is (Minelli, Michael, (2013)):
1. Leverage all current data and enrich it with new data sources.
2. Enforce data quality policies and leverage today’s best technology and people to support the policies.
3. Relentlessly seek opportunities to imbue your enterprise with fact-based decision making.
4. Embed your analytic insights throughout your organization.
With so much data being generated, the real challenge is finding the right data and deriving actionable intelligence.
Pitfalls
For those ready to dive into a Big Data implementation, be sure to weigh the pros and cons. Three of the most common problems in big data deployments are incomplete data collection, false starts, and disruptive drains on IT and data-professional staff productivity.
Here are some insights into the pitfalls to avoid:
1. Failure to capture critical data.
With haste and inexperience, you might miss relevant data that could illuminate revenue opportunities or ways to reduce customer churn. If competitors start taking advantage of what you miss, the entire business could be vulnerable.
2. False starts.
Taking multiple shots at big data will delay implementation. The impact of any delay will only be magnified if competitors beat you to a breakthrough.
3. Resource drains.
IT and data-management teams are under pressure to maintain daily operations, deliver new reports and analyses, and incorporate new capabilities. Overburdening employees with too many roles or short-staffing the day-to-day work is not the way to go. In fact, many successful practitioners report that their big data teams are quite separate from preexisting BI, data warehousing, and data management teams.
Factors driving technology leaders to manage Big Data (Philip Russom 2013):
1. Big data just gets bigger.
Embracing big data to keep pace with its growth, get a business return and fold it into enterprise data architecture. Thus, it is important to beef up data management infrastructure and skills as early as possible. Otherwise, an organization can get so far behind from a technology viewpoint that it is difficult to catch up. From a business viewpoint, delaying the leverage of big data delays the business value. Similarly, capacity planning is more important than ever, and should be adjusted to accommodate the logarithmic increases typical of big data.
2. Resistance is futile: big data will be assimilated into enterprise data.
Technology leaders have to start somewhere, even if it is a data management silo devoted to one form of big data. Typical silos manage Web logs, sensor and machine data logs, and persisted data streams. Yet, it is also important to determine how each form of big data will eventually fit into an overall architecture for enterprise data.
3. Advanced analytics is the primary path to business value from big data.
The current uptick in advanced analytics among organizations is driven by the availability of new big data, plus the new business facts and insights that can be learned from its study.
4. Joining big data with traditional data is another path to value.
For example, 360-degree views of customers and other business entities are more complete and bigger when based on both traditional enterprise data and big data. In fact, some sources of big data come from new customer “touchpoints” (mobile apps, social media) and so belong in customer view.
5. Big data can extend older applications.
This includes any application that relies on a 360-degree view, as mentioned above. Big data can also beef up the data samples parsed by many analytic applications, especially those for fraud, risk, and customer segmentation.
Dealing with New & Disruptive Technologies
Global economies, through innovation, have evolved considerably to form the nature of how business is conducted today. Rapid technological developments cause some firms to collapse while others have used new technologies to exploit business opportunities. Indeed, big data, being a disruptive technology, means that it is both a threat and an opportunity in the business environment. It can be a threat if companies fail to exploit the potential of big data while competitors are quick at adopting this new technology. Hence, companies should seize the opportunity of big data to conduct business more efficiently in the twenty-first century. As DHL (2013) points out, logistics companies such as DHL manage an enormous amount of goods and data sets every day. This means that the logistics industry basically relies on big data usage as a core part of their business models. For this reason, the success of logistics companies is linked to managers’ ability to fully exploit this new disruptive technology. Therefore, important questions are:
- How can the disruptive nature of new technologies be used to one’s advantage?
- How can innovation build upon these new technologies?
These questions are particularly interesting in today’s world where technology evolves faster than ever, loads of information is readily available via the internet, and communication occurs instantly amongst each other. Indeed, in addition to traditional resources such as labor, capital, and land, information has become a critical element in shaping competitive advantage in today’s world (DHL 2013). According to Wu, Ming, Wang, and Wang (2014), “knowledge has become a main source of wealth, and knowledge workers are the most vital asset, and how to manage knowledge is the most important task for organizations and individuals” (p. 6314). In a sense, this means that it is no longer a competitive advantage to simply have knowledge; instead companies nowadays must be able to use knowledge intelligently. Big data allows logistics companies to make intelligent use of their knowledge through package tracking, customer relationship management software, and Enterprise Resource Planning (ERP) software such as SAP and Oracle for example.
According to George, Haas, and Pentland (2014), big data involves huge sets of data, meaning these data sets must be managed in smart ways. People need to utilize powerful techniques to discover trends and patterns within a huge set of data. Therefore, nowadays the logistics industry needs employees with these technical skills. Indeed, from a human resources perspective it can be said that employees working with big data need the proper qualifications and training in order to efficiently work with big data applications. Therefore, logistics companies need people who have mathematical knowledge (e.g. statisticians) as well as computer skills who can work together with marketers in order to identify broader market trends. Equipped with this knowledge of market trends and needs, logistics companies can then design products and processes in ways that more effectively fulfill market needs.
Characteristics of Building on Emerging Technologies
Building upon emerging technologies such as big data requires the logistics industry to merge big data into the life cycles of existing products and services. DHL has done a particularly good job at accomplishing this. This can be seen from the fact that it has improved its operational efficiency, customer experience, and b2b supply chain management in recent years (DHL 2013). As a result, DHL has been able to please customers and reduce its operational costs. It is important to note that although using big data is helpful, this technology also has its limits. This is because of the existence of many indirect sales channels such as online platforms, Smartphone applications and portals. These indirect sales channels cause logistics companies to not have an entirely clear picture of customer activity.
In an online article from our week 9 class readings, Van Hove (2014) highlights the importance of original innovation. According to the author, if a company wants to innovate successfully then it is not enough to simply replicate the innovative actions of strong competitors. In short, Van Hove (2014) claims that in order to innovate effectively, employees need a clear idea of why they do what they do as a business. People are more motivated to add value to their organization if they can identify with the organization’s strategies and goals. Furthermore, to maximize innovative prowess, managers should encourage employees to share creative ideas and to be persistent in achieving agreed upon goals.
We generally agree with these arguments made by Van Hove (2014). To us, the author is basically saying that innovative companies are ones that are able to excite their employees and to make them ambitious. This is done by first inspiring the employees through ambitious goals and secondly by increasing the employees’ identification to the company. In this way, intrinsic motivation of employees will be harnessed, creating an environment conducive to original innovation.
While this approach to innovation is true when managing people, it does not always apply when managing products or processes. In contrast to the author, we believe it is often important for companies to replicate their competitors’ actions, especially when it comes to utilizing emerging technologies to enhance business opportunities. Van Hove (2014) states that “businesses exist because of opportunity and opportunity is why we innovate”. The author seems to be saying that firms should exploit environmental opportunities by creating innovative products or processes. This is a logical statement, however, in today’s world opportunities are often linked to innovations themselves. In other words, innovative technologies themselves represent opportunities for companies to innovate further.
Strategic Adoption of DHL
Deutsche Post DHL Group contains two powerful business brands in the marketplace: The Deutsche Post who is Europe’s largest mail services operator and market leader in the German mail and parcel market; and DHL, the leading global brand in the logistics industry. Originally founded in 1969 by Larry Hillblom, Adrian Dalsey and Robert Lynn, DHL primarily carried out their delivery between San Francisco and Honolulu, but expanded aggressively to countries that could not be served by other courier providers through its blue ocean strategy since the 1970s. As of 2014, DHL has around 285,000 employees in 120,000 destinations in all continents.
In 2009, DHL presented its strategy 2015 which emphasized three key objectives for its development: become the provider of choice for customers, an attractive investment for shareholders and the employer of choice for staff. The strategic approach of its four business divisions (mail, express, global forwarding and freight, supply chain) in its annual report of 2012 and 2013 fully reflects its objectives. By making the important progress towards these three key objectives, in April 2014, “Strategy 2020: Focus. Connect. Grow” has been announced, the new strategy aims to build on these successes and further accelerate DHL’s growth. Slightly different from its previous four business divisions, the new one consists of post/ecommerce/Parcel, express, global forwarding and freight, supply chain. As the newly named division, post/ecommerce/parcel would continue to design a market-based cost structure, provide the highest quality to its customers, motivating its workforce and keep them informed as well as tap into new online and offline markets. By acquiring Allyouneed.com, DHL has established an online supermarket where they offer same-day food delivery with its retail customers; and DHL offers one of the largest online marketplaces in Germany on MeinPaket.de, all these efforts are in line with its goal of offering effective digital communications as the internet has become a useful tool for its customers to access its services. When looking at DHL’s strategic approach in the recent five years, we shall be able to find that DHL’s strategic approach is trend-setting and refuses to be left behind in general.
Stepping into the big data era, where this new technology has been forecasted to play a vital role in logistics industry, as the trend leader, DHL would definitely strive to explore the benefits that big data could bring in.
The contribution to operational efficiency
1. Real-time route optimization
In 2010, DHL and Blue Dart, part of DHL Group, launched an “intelligent” pick-up and delivery vehicle called Smart Truck, which combines a number of innovative technologies including a route planner, and this is able to adjust the routes based on incoming shipment and traffic data. DHL Smart Truck reduced number of miles travelled by 15% and length of average route by 8% during its pilot stage, and also reduced fuel consumption and CO2 emission.
2. Crowd-based pick-up and delivery.
DHL MyWays is an app enabling flexible parcel delivery through crowd-sourcing on which consignees can state where they want delivery and other app users in the area can build points by making ad hoc deliveries in their area.
3. Operational capacity planning
One research project at DHL called “DHL Parcel Volume Prediction” is investigating the correlation between external factors like Google search results, weather conditions or even flu outbreaks and online parcels orderings. This analytic tool results in the operational capacity planning improvement.
Development of New Business Models
1. Market intelligence for small and medium-sized enterprises
DHL Geovista is an online geo marketing tool for SMEs to analyze business potential which is currently under piloting. It is used for predicting realistic sales and analyzing local competitors based on the scientific models.
2. Address verification
A correct address is the prerequisite for a punctual delivery. DHL Address Management is able to return incomplete or incorrect incoming data with validated data from its database.
Nevertheless, all new technology involves trade-offs between risk and return (HBR, 2013). With regard to big data, there are a series of challenges that not only DHL, but all logistics players shall overcome for the sake of the successful implementation, such as data transparency & governance, and data privacy. However, with the rapid development of internet where data is driven to be a crucial point for the success of logistics sector, DHL, the entrepreneurial logistics provider, will be expected to perform more efficiently.
References:
Big data: The next frontier for
innovation, competition and productivity, McKinsey Global Institute, 2011.
Big Data in
Logistics: a DHL perspective on how to move beyond the hype, December,2013
Big Data
Analytics in Supply Chain: Hype or Here to Stay. 2014, Accenture Global
Operation Megatrends Study.
Minelli,
Michael , (2013). 'What Is Big Data and Why Is It Important?'. In: (ed), Big
data, big analytics: emerging business intelligence and analytic trends for
today's businesses . 1st ed. : Hoboken, N.J. : John Wiley & Sons, Inc .
pp.(13-14).
Philip Russom, (2013). Managing Big Data. TDWI Best Practices Report. TDWI
Research.
Mohanbir Sawhney, Robert C. Wolcott and Inigo Arroniz. (2006). The 12 Different
Ways for Companies to Innovate. MITSloan Management Review.
DHL. (2013). Big
Data in Logistics: A DHL Perspective on how to move beyond the hype. Troisdorf, Germany: Jeske, M., Grüner, M., Weiß, F.
George, G., Haas, M.R., & Pentland, A. (2014).
Big Data and Management. Academy of
Management Journal, 57(2), 321-326.
Van Hove, M. (2014, July
11). Transformation Through Strategy And
Innovation. Retrieved from
http://www.strategos.com/transformation-strategy-innovation/
Wu,
Z.Y., Ming, X.G., Wang, Y.L., & Wang, L. (2014). Technology solutions for
product lifecycle knowledge management: framework and a case study. International Journal of Production Research,
52 (21), 6314-6334.
Thanks a lot for team Crab's sharing. As I've studied some logistics related courses before, I enjoy very much to read this article.
ReplyDeleteI fully agree with team Crab that logistics and supply chain industries are supposed to be the most ideally situated for using big data analytics. So far as I know, the modern logistics depends quite a lot on the handling of the electronic data. For example, the logistics concepts developed by the Japanese like "Just In Time (JIT)", "Kanban Management" and "Lean logistics", how well the relevant data (production, freight, storage, etc.) could be analysized and implemented is really important for these modern logistics concepts. I believe that with the great support of big data technology, the logistics /supply chain industry will be developed much better and faster in the near future.
Thanks again for Team Crab's sharing.
Always like your blogger,thank you for sharing your study about big data!
ReplyDeletePressure is mounting on logistics infrastructure in China due to increasing domestic consumption. This increase in consumer spending will put significant strain on an already inadequate logistics infrastructure. On top of that, e-commerce growth will continue to top 100 percent year on year. In fact, a number of the pure e-commerce providers have opted to build their own distribution networks, like JD and also Taobao is on their way to build their own supply chain. Under this circumstance, big data in logistic has a big market in China. And I believe in China there will be more and more people begin to learn and use big data to realize faster, correct and efficient logistic service. But in the developing process, will Chinese market welcome foreign logistic companies with advanced technology to give us an example or develop by ourselves in a short time?
Nice work! DHL, as one of the biggest market players, has just 3% of the world's transport volumes. Under globalization, the logistics system is inter-connected in the world. So information sharing and collaboration among logistic companies and governments would be the most critical issue to tackle when adopting Big Data technology. It would be a good to understand where and how DHL start a big data initiative that linked with strategic choice. Anyway, thanks for sharing a full picture of big data application in the logistics industry.
ReplyDeleteGood choice of topic, appreciate your hard work as usual. I believe the video explaining “why UPS trucks never turn left” introduced by Frank in the class has given you some inspirations, which is really an amazing business practice.
ReplyDeleteFirst of all, thanks for sharing the status quo of the usage of big data analytics, which is surprisingly less than I expected. According to Accenture (1 Feb 2015. Big Data Analytics in Supply Chain), the greatest concerns about the use of big data analytics include but not limited to large investment, as well as security and privacy concerns. I can see that DHL has invested sufficient resource to smooth the logistic process, but have they done anything to protect the company and its customers’ security and privacy?
By the way, there is no doubt that to filter and extract correct and useful information is essential and challenging. But I think most of the readers, like me, will be happy to see if there are any solutions or recommendations on execution.
At last, I can see you have done a lot of research that plenty of supporting evidences are given. However, I hate to say that some of the descriptions are wordy and redundant. Take “Dealing with New & Disruptive Technologies” part for example, I think it would be better to focus on big data and go direct to the point to make it clear.
Thanks so much for team 8 for detailed analysis and recommendation on applying big data in logistic industry. The key question for logistic company is how to extract the kinds of useful, meaningful managerial insights from big data, turn data into intelligence, intelligence into action that will result in competitive advantage and maintain sustainable edge.
ReplyDeleteAs various tangible goods and intangible data stockpile at the various sites, including incoming/outcoming parcels, ample volume data and complex interrelationships, Efficient and effective management of data throughout the supply chain significantly improves the ultimate service provided to the customers.
Agree with team 8 statement of the importance of upgrading capabilities of employees to work with big data by providing them with appropriate training in order to efficiently work with big data applications. Meanwhile it should not be ignored to redesign organizational incentives, since most companies use incentive systems focused on meeting KPI of division, group, or site. These tend to inhibit cooperation. In order to enhance cooperation among different divisions or groups to work closely to achieve organizational effectiveness and efficiency, companies may need to redesign the organization and develop new incentive system so as to fully utilize advantages on such huge investment on IT hardware.
Thank you team CRAB for presenting such an interesting analysis on big data and DHL. I see you have also made good use of the class readings in your blog. Great job! I am deeply inspired by the first youtube you have posted. Indeed, big data analytics is more than technology, it’s a new way of thinking!
ReplyDeleteWith the modern technology, data is easy to store and easy to retrieve, thus it is not a problem for companies in general to harness such data. However, given the vast volume of data, which data are actually relevant? How can companies analyze such enormous amounts of data in the quickest way in order to stand out from its peers in today’s hypercompetitive business environment?
“A big data strategy needs to be more than a technology discussion. It also needs to focus on defining a business goal, identifying who you are going to work with to achieve it, and how you’re going to find or train the necessary talent,” says Vince Dell’Anno from Accenture Analytics.
When adopting big data technology, I think it is very important for companies to first come up with a strategic plan to identify the value of big data, hiring the right talents for data analyses, and knowing how to utilize the insights obtained from results of data analytics to achieve organizational strategic goals. A change in mindset of companies’ management is thus vital especially when economic environments are being rapidly driven by big data. Sticking to old business models will only bring competitive disadvantage to the company.
Student no:53970301
ReplyDeleteDear members of Crab,
Thank you very much for sharing the story of DHL and I find it very educational to read your article. I agree that it is a must for the logistics sector to adopt big data to continue to evolve in an era of continuous development of business and IT technology. However, they should also beware about the security issue and the backup plan should the big data system encounter major failure such as hackers attack or system break down. I hope my sharing will help enlighten the group in devising solution for the managers like us to think in advance about the potential risk while at the same time taking the advantage of the convenience of IT technology.
Dear Team of Crabs,
ReplyDeleteIt is thrilling to read your article. It is the first time I know about the application of big data in logistic industry. It is also quite amazing to know the different tools DHL create by using big data technology. Since I used to be in the logistic field, hard to say being in the logistic industry, all I have known is the traditional logistic operations. When talking about logistic strategy, it tends to be more like supply chain. The big data applied in the logistic industry is an innovation which overturn the formation of the logistic industry.
After reading this article, I will be not confident to say that I understand the logistic industry. There are still a lot of unknown waiting for me to discover.
Anyway, thanks for your sharing, And I learn a lot for your article.
Dear classmates, thank you for the awesome article and introducing this new concept to me. It is a golden opportunity for the logistic companies to adopt the big data to keep the development of the business. First, the huge amount data pose is a problem for the companies to store and analyze. Maintaining the effectiveness and accuracy is the key to the application of the big data. Pitfall analysis really impressed me a lot for the reason that the analysis is critical and you guys provided us some practical ways to avoid the drawbacks, which helps the reader to deeply understand the industry. Strategy always plays an initial part in business filed and the writer detailed describe the l strategic adoption of DHL. And the employees about big data is another key factors to control and supervise.
ReplyDeleteThanks for the hard work and perfect article.
Dear "Crabers"
ReplyDeleteIt was a great journey to go through your blog that talking about big data and logistic industry. I have never image that big data bring such a new world to logistic industry! Your demonstration on the impact that big data have placed or would place to DHL on the operational efficiency and new business model sound reasonable and interesting. I think it will be much more attractive if you could dig deeper in the new business model and present more detail on the actual number the impacts.
Thanks for your team’s sharing. This is the last comment for all my readings of the bloggers, and I've found a common point from all these emerging technologies: they provide a fair and new opportunity for the development of small and middle sized company. It's true that without these emerging technologies, the development or even the starting of some companies is impossible.
ReplyDeleteYour analysis remind me of one thing that the enterprise need to pay attention to.the privacy of customers in the competition of logistic industry.In the era of big data, all the information of customers are recorded in the database, and the company need to strengthen their ability to perfect regulations to keep privacy of customers.And also give an inspiration that the nation should enhance legislation.
Dear Crab, thanks for your sharing. Undoubtedly, this is a Big Data age, and just like what stated in the article, the big data left a deep impression to us not only because of its being big, but also its ubiquitous. I have some friends whom working at different industries such as bank and transportation, while they are doing almost the same things – data analysis. According to the analysis, they can get some behavior trends of customers and forecast their possible demands in the future.
ReplyDeleteI agree with you that the main problems come with Big Data are incomplete data collection and resource drains. A precise database is the key to start a successful analysis while the indirect sales channels bring limitations to this technology at the same time. This may not the problem only being faced by DHL, many other logistic companies might encounter the same situation.
One more thing I wish to understand is the relationship between address verification and the big data, I cannot catch the point here very well. And I was just fail to checkout my shopping bag online because of the failure billing address verification, I don't know whether it has any links with the big data ……… T^T