Big Data Statistics 2.0

Smart Wuhan, Built on Big Data


The following is an abstract for a presentation given in the Committee of 100 Fourth Tien Changlin (田长霖) Symposium held in Wuhan, China, on June 20, 2013.

The presentation in simplified Chinese is available at 智慧武汉:善用大数据.

The urban population in China doubled between 1990 and 2012.  It is estimated that an additional 400 million people will move from the countryside to the cities in the next decade.  China has announced plans to become a well-off society, while maintaining harmony, during this time period.  This is an enormous challenge to China and its cities like Wuhan.

A well-off society necessarily includes a sound infrastructure and sustainable economic development with entrepreneurial spirits and drive for innovation.  It must constantly improve quality of life for its citizens with effective management of the environment and natural resources.  Most of all, it must change governance so that flexibility, high efficiency and responsiveness are the norms that its citizens would expect.

If data were letters and single words, statistics would be grammar that binds them together in an international language that quantifies what a well-off society is, measures performance, and communicates results.  Modern technology can now collect and deliver electronic information in great variety with massive volume at rapid speed during the Big Data era.  Combined with open policy, talented people, and partnership between the academia, government, and private sector, Wuhan can get smart with Big Data, as it has started with projects like “China Technology and Science City” and “Citizen’s Home.”  Although there are many areas yet to expand and improve, a smart Wuhan will lead the nation up another level toward a well-off society.

Link to presentation in simplified Chinese: 智慧武汉:善用大数据.

Big Data Statistics Statistics 2.0

The Essentials of Identification Codes

Big Data promises to improve governance of society and better inform the public in the 21st century.  Although every data record has some information to contribute, linking and merging relevant electronic records can minimize the collection of duplicate data and increase the value and utility of the integrated data rapidly and exponentially.  Essential in this approach is the presence of identification codes that will facilitate the actual integration of record and data.

The identification code is a key to unlocking the enormous power in Big Data.  However, it may also be the primary cause of system failures, misuses and abuses, and even fraudulent or criminal activities, if it is not properly applied and managed.  In addition to technology, statistical design and quality feedback loops, proper education and training, relevant policies and regulations, and public awareness are all needed for the effective and responsible use of identification codes and Big Data.

The Need for Identification Codes

When a student enters a school, a record will store the student’s name, gender, age, family background, field of study, and other data.  When she takes a course and receives a final grade, the results are recorded.  When she satisfies all the requirements for graduation, another record will show the grade point average she has achieved and the degree she is awarded.

Each record represents a snapshot for the student.  The records are collected over time for administrative purposes.  Together the longitudinal snapshots provide comprehensive information about the education of a student.

When the student enters the workforce, additional data are collected over her lifetime about the industry and occupation she works in, the job she performs, the wages and promotions she receives, the taxes and insurances she pays, and the employment or unemployment status she is in.

In like manner, massive amounts of data are collected about a firm, including its initial registration as a business, periodic reports on revenues and expenses, entry into the stock markets, acquisitions or mergers with other companies, payment of taxes and fees, growth in sales and staffing, and expansion or death of the business.

These administrative records used to be stored in dusty file cabinets, but they are now mostly digitized and available for computer processing when the Big Data era arrived at the turn of the millennium.

Timely and proper integration of the records of all students would provide unprecedented details about how the school is performing, such as its graduation or dropout rate over time.  Further roll up of all schools would inform a nation about its state of education, such as its capacity to support employment and economic growth.  This is what Big Data promises to bring in the 21st century.  From allocation of resources, measurement of performance, to formulation of policy, every segment of society can benefit from the details and insights of Big Data to improve governance and inform the public.

Although every data record has some information to contribute, linking and merging relevant electronic records minimizes the collection of duplicate data and increases the value and utility of the integrated data exponentially.  Essential in this approach is the presence of identification codes that will facilitate the actual integration of record and data.  Statisticians can make significant contributions to building new statistical systems with their thinking and methods in this process.

Types of Identification Codes

The name of an individual or a company was the preferred identification code when files were still physical, such as in paper form.  It has been conventional to consolidate records under the same name and sort them by alphabetical order in English, number of strokes in Chinese, or chronological order.

However, a major shortcoming of using names, especially when processed massively by computer, is that they are not unique.  The top four family names of Lee, Wang, Zhang, and Liu accounted for 334 million individuals in China in 2006 [1], exceeding the total U.S. population.  Chinese names may also appear differently because of the simplified and traditional characters.  The English first name of Robert, the 61st most popular male name at birth in the U.S. in 2011 [2,3], can have at least 7 common variations for the same person, including Bert, Bo, Bob, Bobby, Rob, Robbie, and Robby.  Bert may also be short for Albert.  Individuals may apply to change their names or use more than one name; women may change their names after marriage.  Human errors can add errant names.  References to the same name across nations with different languages can be notoriously difficult.

The name of a company is usually checked and validated to avoid duplication during the registration process and protected by applicable local, national and international rules and laws including trademarks after registration.  The company may use multiple names including abbreviations and stock market symbols; it can also change its name due to merger with another company, acquisition agreement, reorganization, or a simple desire to change its brand.

A non-unique identification code poses the risk of linking and merging records incorrectly, leading to incorrect results or conclusions.  Supplementing a name with auxiliary information, such as age, gender, and an address, would reduce but not eliminate the chance of record mismatches, and at the cost of increasing processing time.

A series of numbers, letters and special characters (alphanumeric) or a series of numbers alone (numeric) is increasingly used as the identification code of choice with modern machine sorting, linking, and merging of electronic records.  Numeric codes tend to be less restrictive because they are independent of the writing system.  Alphanumeric codes using letters from the English alphabet may be suitable for systems using languages based on the Latin alphabet, but systems using non-Latin scripts may still find them unavailable or difficult to use, understand, or interpret.  It is also easier to understand how numeric codes are sorted compared to alphanumeric codes.

When the Social Security Act of 1935 was passed in the U.S., one of the first challenges in implementation was to create an identification code that would “permanently identify each individual to be covered” and “be sufficiently elastic to function indefinitely as additional workers became covered” [4].  An 8-field alphanumeric code was initially chosen, but it was soon rejected by the statistical agencies, as well as labor and justice departments.   This exchange was described [4,5] as the first sign of “the tremendous impact machines would have on the way [government] would do business.”  This was BEFORE computers were introduced for actual use.

Today, the impact of information technology is obvious and continues to increase in every aspect of government, business, and individual activities.  An identification code may be applied to a person, a company, a vehicle, a credit card, a cargo, en email account, a location, or just about any practical entity.

An electronic record that does not contain an identification code or cannot be correctly linked with other records may be described as lacking in “structure” or “unstructured” in the Big Data era.  Since the beginning of the 21stcentury, “unstructured” data are occurring in much higher frequency than “structured” data.  However, they contain relatively limited information content and utility compared to “structured” data, especially for continuing, consistent, and reliable information about a society or an economy over time.

Effective use of the identification code is a key to unlocking the enormous power inherent in Big Data.

Effective Use of Identification Codes

  1. Match and Merge Records.  Ideal identification codes are mutually exclusive and exhaustive, establishing an unambiguous one-to-one relationship between the code and the entity, including those yet to appear in the future.  The code facilitates direct and perfect machine sorting, matching, and merging of electronic records, potentially increasing the amount of information about the entity with no limit.
  2. Anonymize and Protect Identity.  A code offers the first-line protection of identity by anonymizing the entity.  Due to the increasing importance of the code and the relative ease of linking with other records, the risks and stakes of identity fraud or theft through the identification code have also risen, requiring responsible policy and management of the code as safeguards.
  3. Provide Basic Description and Classification.  An identification code can provide the most basic description of the content and context of the data records, from which simple observations or summaries can be quickly derived.  Over time, this concept also evolved into codes for classification and the separate development of “metadata” [6,7] for efficiently building structure into data systems and broadening their use across systems.
  4. Perform Initial Quality Check.  Unintentional human errors in typing or transcribing an identification code incorrectly may damage the quality of integrated data and the eventual analytical results.  Fraudulent or malicious altering of the identification codes may inflict even more severe damage to the integrity and reliability of the data.  Early detection with the deployment of “check digit” [8,9] in the identification code may eliminate more than 90 percent of these common errors.
  5. Facilitate Statistical Innovations.  By collecting and integrating data continuously for each entity such as a student, a dynamic frame with rich content can be built for all students and all schools.  New data elements may be defined for analysis; statistical summaries may be produced in real time or according to set schedules to describe the performance of a school or the state of education for a nation, while strictly protecting the confidentiality of individuals and security of their data.   Innovative efforts to construct these dynamic frames, or longitudinal data systems, have started in the U.S. and China [10].  The Data Quality Campaign [11] lists “a unique statewide student identifier that connects student data across key databases across years” to be the top essential element in building state longitudinal data systems for education in the U.S.

Personal Identification Codes of the U.S. and China

The U.S. does not have a national identification system. The Social Security Number (SSN) was created to track earnings of workers in the U.S. in 1936, before computers were introduced for commercial use.  Its transition into the computer age revealed some of the strengths and weaknesses of its evolving role as an identification code.

The 9-digit SSN is composed of 3 parts:

U.S. Social Security Card
U.S. Social Security Card
  • Area Number (3 digits) – initially geographical region where the SSN was issued and later the postal area code of the mailing address in the application
  • Group Number (2 digits) – representing each set of SSN being assigned as a group
  • Serial number (4 digits) – from 0001 to 9999

Demographic data are collected in the SSN application [12], including name, place of birth, date of birth, citizenship, race, ethnicity, gender, parents’ name and SSN, phone number and mailing address.  The U.S. Social Security Administration is responsible for issuing the SSN.  Some of the SSN are reserved and not used.  Once issued, a SSN is supposed to be unique because it would not be issued again.  However, some duplicate situations exist.

A wallet manufacturer decided to promote its product in 1938 by showing how a copy of a Social Security card from one of its employees would fit into its wallets, which were sold through department stores [13].  In all, over 40,000 people mistakenly reported this to be their own SSN, with some as late as in 1977.

Use of the SSN by the government and later the private sector has expanded substantially since its creation.  Beginning in 1943, federal agencies were required by executive order to use the SSN whenever the agency finds it advisable to establish a new system of permanent account numbers for individuals [5].  In the early 1960s, federal employees and individual tax filers were required to use SSN.  In the late 1960s, SSN began to serve as military identification numbers.  Throughout the 1970s when computers were increasingly used, SSN was required for federal benefits and financial transactions such as opening bank accounts and applying for credit cards and loans.  Beginning in 1986, parents must list the SSN for each dependent for whom the parents want to claim as a tax deduction.   The anti-fraud change resulted in 7 million fewer minor dependents being claimed in the first year of implementation [14].

As SSN became essentially an unofficial national identifier that can link and merge many electronic files for the same person together, it can also be the direct cause of misuse and abuse such as identity theft [15].  The SSN does not have a check digit; it cannot be used reliably for authentication of identity.  Academic researchers have also demonstrated ways to use publicly available information “to reconstruct SSN with a startling degree of accuracy” [16].  These identified vulnerabilities have resulted in more cautious, secured, and responsible use of the SSN in the U.S. in recent years.  The original 1943 executive order requiring the use of SSN was rescinded and replaced by another executive order in 2008 that makes the use of SSN optional.

China had a relatively late start in personal identification codes.  It revised the Resident Identification Number (RIN) from 15 digits to 18 digits on July 1, 1999, raising the embedded birth year from 2 to 4 digits and adding a check digit.  The 18-digit RIN is composed of 4 parts [17,18]:

Chinese Identification Card
Chinese Identification Card
  • Address Area Number (6 digits) – administrative code for the individual’s residence
  • Birthdate Number (8 digits) – in the form of YYYYMMDD where YYYY is year, MM is month and DD is day of the birthdate
  • Serial Number (3 digits) – with odd numbers reserved for males and even numbers reserved for females
  • Check Digit (1 digit) – computed digit based on 17 previous digits using the ISO 7064 standard algorithm [18,19]

Security offices at county-level local governments issue the resident identification cards to individuals upon application no later than age 16.  Data collected include name, gender, race, birthdate, and residential address.  The resident identification cards may be valid permanently or for a time period as short as 5 years, depending on the age of the applicant.  According to official announcements, the RIN is also used to track individual health records in the National Electronic Health Record System in China [20].

Business Identification and Industry Classification Codes of the U.S. and China

An Employer Identification Number (EIN) to a business is equivalent to the SSN to an individual in the U.S. [21].  However, a business in this case may also be a local, state, or federal government; it may also be a company without employees or an individual who has to pay withholding taxes on his/her employees.  The EIN is a unique 9-digit number assigned by the U.S. Internal Revenue Service (IRS) according to the GG-NNNNNNN format, where GG was a numerical geographical code to the location of the business prior to 2001 and the remaining 7 numeric digits have no special meanings.  Once issued, an EIN will not be reissued by IRS.  In addition, each state has its own, different Employer Identification Number for its tax collection and administrative purposes.

Information collected about the business during the EIN application process include legal name, trade name, executor name, responsible party name, mailing address, location of principal business, type of entity or company, reason for application, starting date of business, accounting year, highest number of employees expected in the next 12 months, first date of paid wages, and principal activity of business [22].

U.S. statistical agencies use the North American Industry Classification System (NAICS) to classify business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. economy [23].  NAICS was adopted and replaced the Standard Industrial Classification (SIC) system in 1997.

NAICS is a hierarchical classification coding system consisting of 2, 3, 4, 5, or up to 6 numeric digits.  The top 2-digit codes represent the major economic sectors such as Construction and Manufacturing.  Each 2-digit sector contains a collection of 3-digit subsectors, each of which in turn contains a collection of 4-digit industry groups.  For example, 31-33 is the Manufacturing sector for which the following hierarchy exists for the Rice Milling industry:

311                  Food Manufacturing

3112                Grain and Oilseed Milling

31121              Flour Milling and Malt Manufacturing

311212            Rice Milling

One of the strengths of the hierarchical system is that aggregation can be performed easily up the chain.  For example, sum of all 311X companies should form the 311 Food Manufacturing industry in the U.S.

Consistent creation and assignment of NAICS codes is a challenge in a global, dynamic economy where obsolete industries may disappear and new industries may spawn and grow overnight.  Examples of challenging industries include “high technology” industries in the past and the recent “green” industries.  Application of the NAICS codes is subject to interpretation and consistency issues.  For example, the U.S. Census Bureau and the U.S. Bureau of Labor Statistics disagree in creating and maintaining their respective business frames due to differences in data sources and assignment of NAICS codes [10].  Inconsistent use of NAICS codes disrupts or even invalidates analysis and interpretation of time series or longitudinal data.

A new business in China must apply to the local Quality and Technical Supervision Office for a 9-digit National Organization Code, which contains 8 digits and 1 check digit [22].  The Chinese regulation, GB 11714-1997 on Rules of Coding for the Representation of Organizations, is patterned after international standards, ISO 6523 Information Technology – Structure for the Identification of Organizations and Organization Parts [25].  Online directories exist to look up information about the organization based on the National Organization Code [26].

The value of the Chinese Industrial Statistical Dataset is well recognized by economists and other analysts domestically and internationally.  Substantial resources were invested into the construction and maintenance of the comprehensive data system that describes almost all state-owned and large enterprises (annual sales of over RMB Ұ5 million until 2010 and over RMB Ұ20 million thereafter) in China longitudinally since 1998.  However, serious data quality problems have been reported, and the primary cause can be traced to the inconsistent and incorrect application of the identification codes [27].  This situation exists although China started its standardization of organization codes in 1989 and is currently in the third phase of implementation [28].

As recently as last month, Guangdong province has announced its commitment to use a shared platform on the National Organization Codes as part of its campaign to combat corruption [29].

China also has a standard industry classification system under GS-T4754-2002 [30].  The hierarchical system has 4 categories with the highest level indicated by a 1-digit letter, and the lower levels represented by 2, 3, and 4 digits respectively.  For the previous example of Rice Milling, the Chinese classification system provides the following hierarchy:

C                      Manufacturing

C13                  Food Manufacturing

C131                Grain Milling

C1312              Rice Milling


As technology continues to evolve and grow, larger amount of digitized data will be collected more rapidly at relatively low cost.  This has characterized the Big Data era.

These Big Data contain unprecedented amount of information.  If integrated and structured, their value and power will be increased exponentially beyond any existing statistical systems have been able to provide.  Identification codes that facilitate linking and merging of records hold the key to unlocking this enormous trove of opportunities.

As the gateway to the enormous power of Big Data, identification codes may also be the primary cause of system failures, misuses and abuses, and even fraudulent or criminal activities, if they are not properly applied and managed.

The practical challenges of applying an identification code are complex.  In addition to technology, statistical design and quality feedback loops, proper education and training, effective policies and regulations, and public awareness are all needed for the effective and responsible use of identification codes.  These topics will be discussed in future papers.

Co-authored by Jeremy S. Wu, Ph.D., and Hao Ding,


[1]  Quantitative Ranking of Chinese Family Names (中國姓氏人口數),November 25, 2012.  Available at on April 29, 2013.

[2] Wikipedia.  Robert.  Available at on April 29, 2013.

[3] U.S. Social Security Administration.  Change in Name Popularity.  Available at on April 29, 2013.

[4] U.S. Social Security Administration.  Fifty Years of Operations in the Social Security Administration, by Michael A. Cronin, June 1985.  Social Security Bulletin, Volume 48, Number 6.  Available at on April 29, 2013.

[5] U.S. Social Security Administration.  The Story of the Social Security Number, by Carolyn Puckett, 2009.  Social Security Bulletin, Volume 69, Number 2.  Available at on April 29, 2013.

[6] Wikipedia.  Metadata. Available at on April 29, 2013.

[7] Wikipedia. 元数据. Available at on April 29, 2013.

[8] Wikipedia.  Check Digit.  Available at on April 29, 2013.

[9] Wikipedia. 效验码. Available at on April 29, 2013.

[10] Wu, Jeremy S. 21st Century Statistical Systems, August 1, 2012.  Available at on April 29, 2013

[11] Data Quality Campaign.  10 Essential Elements of a State Longitudinal Data System.  Available at on April 29, 2013.

[12] U.S. Social Security Administration.  Application for a Social Security Card, Form SS-5.  Available at on April 29, 2013.

[13] U.S. Social Security Administration.  Social Security Cards Issued by Woolworth.  Available at on April 29, 2013.

[14] Wikipedia.  Social Security Number.  Available at, on April 29, 2013.

[15] President’s Identity Theft Task Force. 2007. Combating Identity Theft: A Strategic Plan.  Available at on April 29, 2013.

[16] Timmer, John.  New Algorithm Guesses SSNs Using Data and Place of Birth, July 6, 2009. Available at on April 29, 2013.

[17]  GB11643-1999 Citizen Identity Number 公民身份号码.  Available at on April 29, 2013.

[18] Wikipedia.  Resident Identity Card.  Available at on April 29, 2013.

[19] Wikipedia.  ISO 7064.  Available at on April 29, 2013.

[20]  Electronic Health Record 电子健康档案. Available at on April 29, 2013.

[21] Wikipedia.  Employer Identification Number.  Available at on April 29, 2013.

[22] U.S. Internal Revenue Service.  Form SS-4: Application for Employer Identification Number.  Available at on April 29, 2013.

[23] U.S. Census Bureau.  North American Industry Classification System.  Available at on April 29, 2013.

[24] National Administration for Code Allocation to Organizations.  Introduction to Organizational Codes, 组织机构代码简介.  Available at on April 29, 2013.

[25] Wikipedia.  ISO/IEC 6523.  Available at on April 29, 2013.

[26] National Administration for Code Allocation to Organizations.  National Organization Code Information Retrieval System, 全国组织机构信息核查系. Available at on April 29, 2013.

[27] Nie, Huihua; Jiang, Ting; and Yang, Rudai.  A Review and Reflection on the Use and Abuse of Chinese Industrial Enterprises Database.  World Economics, Volume 5, 2012.  Available at on April 29, 2013.

[28] National Administration for Code Allocation to Organizations.  Historical Development of National Organization Codes, 全国组织机构代码犮展历. Available at on April 29, 2013.

[29] National Administration for Code Allocation to Organizations.  Guangdong Aggressively Promotes the Use of identification Codes in its Campaign against Corruption, 广东积极发挥代码在反腐倡廉中的促进作用, March 7, 2013. Available at on April 29, 2013.

[30]  National Economic Industry Classification, GB-t4754-2002, 国民经济行业分类(GB-T4754-2002)(总表).  Available at on April 29, 2013.

Big Data Statistics

Statistics 2.0: Dynamic Frames


A frame identifies all the known units in a population from which a census can be conducted or a random sample can be drawn, providing the structural foundation for the extraction of maximum, reliable information from designed statistical studies with the support of established statistical theories.  The significance of the Big Data era is that most data are now digitized, easily stored, and processed in large quantity at relatively low cost.  Big Data offers unprecedented opportunities for statisticians to rethink and innovate.  Among the many possibilities offered by Big Data is the creation and maintenance of Dynamic Frames – frames that are rich in content, capture the most up-to-date data as soon as they become available, and produce results and reports in real time on demand.
Traditional Population and Frame
A population is an important concept in the study of statistics.  It is commonly understood to be an entire collection of items of interest, be it a nation’s people or businesses, a day’s production of light bulbs, or an ocean’s fish [1,2,3].
A less well-known term is a frame, or a list of the units that cover the entire population with its identification system.   A frame is the working definition of a population under study.  It identifies all the known units in a population from which a census can be conducted or a random sample can be drawn, providing the structure for statistical description and analysis about the population [2,4,5].

Figure 1

Figure 1 shows a flow chart of a conventional statistical study by census or random sample.  Quoting from [4], an ideal frame should have the following qualities:
  • All units have a logical, numerical identifier
  • All units can be found – their contact information, map location or other relevant information is present
  • The frame is organized in a logical, systematic fashion
  • The frame has additional information about the units that allow the use of more advanced sampling frames
  • Every element of the population of interest is present in the frame
  • Every element of the population is present only once in the frame
  • No elements from outside the population of interest are present in the frame
  • The data is “up-to-date”
Modeling may be considered part of a sampling process, sometimes bypassing the need for a frame by assuming that the model and data adequately represent the underlying population. 
Practicing statisticians understand the importance of frames – it is the structural foundation for the extraction of maximum, reliable information from designed statistical studies with the support of established statistical theories.  However, there are few statistical papers or forums that discuss the best practices for creating and maintaining a frame, primarily because it is viewed as an administrative or clerical task.
Many lament how difficult it is to obtain or maintain a good frame or their bitter experience of working with incomplete or error-prone frames.  Indeed, poor quality frames may prevent a well-planned statistical study from even taking place or create misleading or biased results. 
Inadequate attention to the creation and maintenance of a flexible, up-to-date, and dynamic population frame has been costly to the statistics profession and the U.S. in terms of efficiency and innovation.
For example, according to [6], although “an accurate and complete address list is a critical ingredient in all U.S. Census Bureau surveys and censuses,” each program prepared its own separate list until the concept of a national frame was advanced not even 20 years ago in the name of the Master Address File (MAF). 
The MAF is used primarily to support mail delivery of questionnaires [7], which is increasingly an outdated mode for information collection.  It is relied upon heavily for follow-up visits to non-respondents, when rising labor costs are now met with tight budget constraints.  Web-based questionnaire delivery or data submission was not allowed in the latest 2010 decennial census in the U.S.  The MAF is also not designed to promote or support web-based applications. 
The arrival of the Big Data era seems to have caught the statistics profession in a deer-in-the-headlight moment.  As statistician is hailed as “the sexiest job for the next 10 years” and beyond [8], the profession is still wondering why statistics is undervalued and left out, while in search of a role it should play in the Big Data era [9].
Only a few seem to recognize that statistics is “the science of learning from data” [10], regardless of how big or small the data are, and that the moment has arrived for the profession to join the revolution and remain relevant in the future.
Statistics 2.0: Dynamic Frames
Big Data is a relative concept.  Tomorrow’s Big Data will be bigger than today’s Big Data.  If it is only the size of data that statisticians would consider, the impact of Big Data would be limited to only scaling the existing software and methods. 
The significance of the Big Data era is that most data are now digitized, including sound, vision, and handwriting [e.g., 11], much of which have never been available before.  They can be easily stored and processed in large quantity at relatively low cost.  Today’s consumers of statistics are much higher in number and less interested in technical details, but they also want comprehensive, reliable, easy-to-use information rapidly and readily.
Big Data is as much a revolution in information technology as it is for advancement in statistics because it offers unprecedented opportunities for statisticians to rethink its systems and operations and innovate.
For example, mathematical statistics clearly demonstrates that a 5 percent random sample is superior to a 5 percent non-random sample.  However, how does it compare to a 50 percent or a 95 percent non-random sample?  We have continued to caution, warn, condemn, or dismiss large, non-random samples, but have done little to go beyond the existing framework of mathematical statistics.  Is there not a point, albeit that it may vary from case to case, where the inherent statistical bias can be reduced by the large size of a non-random sample so that they can become practically acceptable and meaningful?
As another example, as long as Figure 1 remains the typical process of conducting statistical studies in a sequential and cross-sectional manner, there is little room for innovative improvement to reduce turnaround time or introduce new metrics such as measuring longitudinal change at the unit level [12].  Is it absolutely impossible to produce accurate and reliable statistical results in real time?  Or is it because we have become so comfortable with the present software, approach, and convenience that there is no desire to consider other possibilities?
Random sampling has been the dominant mode of statistical operation for a century [13].  Because of Big Data, one may now study an entire population almost as easily as one can study a random sample today.  Should we ignore this opportunity? 
If statisticians do not recognize or embrace the challenges of theory and practice posted by Big Data as part of the core of studying and practicing statistics, the risk is high that others including the yet-undefined “data scientists” will fill the void [14].
Among the many possibilities offered by Big Data is the creation and maintenance of Dynamic Frames – population frames that are rich in content, capture the most up-to-date data as soon as they become available, and produce results and reports according to established schedules or even in real time. 
With some user base exceeding one billion people in membership, E-Commerce companies and the social media are well positioned to apply their data from online transactions, emails, and blog postings to conduct market research and perform predictive analyses.  A lay person may also capture these data in a less structured manner.

Figure 2

Figure 2 provides a simple schematic on how the Dynamic Frames may work, which are also described as longitudinal data systems in educational applications in the U.S. [15,16]
In essence, primary efforts are put into the creation and maintenance of the frame so that it is optimized by the previously identified properties.  It is constantly updated with new data for every sampling unit over time. 
Statisticians must be fully engaged in the design, implementation, and operation of Dynamic Frames, in addition to the production of descriptive and analytical results.  There are many new and traditional functions that statisticians can make major contributions. 
For example, the identification code is a key to unlocking the enormous power in Big Data.  It controls the extent additional records and data may be linked, determines firsthand the overall quality of data and study, and is the first safeguard to protect confidentiality.
As another example, the size and content for the units have no conceivable limit.  They depend only on availability of data, ability to link and match records, and design of system.  Effective operation minimizes mismatches of records and collection of duplicative data that do not change or change in predictable manner.   Appropriate replacement or imputation for missing values ensures quality and timely integration of data.
Other enhancement of traditional statistical functions [14] include, but are not limited to, establishing continuous quality loops back to the data sources; developing new definitions, metrics, and standards for the dynamic frames; applying new statistical modeling for imputation, profiling, risk assessment, and creating artificial intelligence; developing innovative visualizations; improving statistical training and education; and protecting confidentiality.
Dynamic frames will retain its original purpose as a list of known units for conducting censuses and drawing random samples as needed, but the potential use of structured Big Data is limited only by the imagination and innovative spirit of the statistics profession.  Statisticians need to embrace Big Data as its own revolution, which will lead to the next level of human knowledge and practice by study and use of data.

Co-authored by 
Jeremy S. Wu, Ph.D.,

Junchi Guo, Ph. D. Candidate,

[1] Hansen, Morris H.; Hurwitz, William N.; and Madow, William G.  (1953).  Sample Survey Methods and Theory.  Wiley Classics Library Edition, John Wiley & Sons, Inc. 
[2] Kish, Leslie.  (1965).  Survey Sampling.  Wiley Classics Library Edition, John Wiley & Sons, Inc. 
[3] Cochran, William G.  (1977).  Sampling Techniques.  A Wiley Publication in Applied Statistics, Third Edition, John Wiley & Sons, Inc.
[4] Wikipedia.  Sampling Frame.  Available at on April 8, 2013.
[5]  Sampling Frame 抽样框.  Available at on April 8, 2013.
[6] U.S. Census Bureau.  Master Address File: Update Methodology and Quality Improvement Program, by Philip M. Ghur,  Machell Kindred, and Michael L. Mersch, 1994.  Available at on April 8, 2013.
[7] U.S. Census Bureau.  The Master Address File for the 2010 Census, by Joseph Salvo, April 7, 2006.  Brookings Breakfast Briefings on the Census.  Available at on April 8, 2013.
[8] Varian, Hal.  Hal Varian explains why statisticians will be the sexy job in the next 10 years,  September 15, 2009.  YouTube.  Available at on April 8, 2013.
[9] Pierson, Steve and Wasserstein, Ron.  Big Data and the Role of Statistics, March 28, 2012.  Available at on April 8, 2013.
[10] van der Lann, Mark; Hsu, Jiann-Ping; and Rose, Sherri.  Statistics Ready for a Revolution.  Amstat News, September 1, 2010.  Available at on April 8, 2013.
[11] Washington Post.  From the President’s Hand to the Internet.  Available at on April 8, 2013.
[12] Diggle, Peter J.; Heagerty, Patrick J.; Liang, Kung-Yee; and Zeger, Scott L. (2001).  Analysis of Longitudinal Data.  Second Edition, Oxford University Press.
[13] Wu, Jeremy S., Chinese translation by Zhang, Yaoting and Yu, Xiang.  One Hundred Years of Sampling, invited paper in Sampling Theory and Practice, ISBN7-5037-1670-3, 1995.  China Statistical Publishing Company.
[14] Wu, Jeremy S. 21st Century Statistical Systems, August 1, 2012.  Available at on April 8, 2013. 
[15] Data Quality Campaign.  Using Data to Improve Student Achievement.  Available at on April 8, 2013.
[16] U.S. Department of Education.  Statewide Longitudinal Data Systems Grant Program, National Center for Education Statistics.  Available at on April 8, 2013.